Computational Intelligence Methods for ADMET ...

Frontiers in Drug Design & Discovery, 2009, 4, 351-377

351

Computational Intelligence Methods for ADMET Prediction David Hecht1,* and Gary B. Fogel2 Southwestern College, 900 Otay Lakes Rd., Chula Vista, CA 91910, USA and Natural Selection, Inc., 9330 Scranton Rd., San Diego, CA 92121, USA Abstract: Quantitative structure-property relationship (QSPR) models have proven to be an effective approach for increasing the efficiency of small molecule drug discovery and development processes. Despite their importance to drug discovery, difficulties remain in the appropriate selection and weighting of descriptors, determination of appropriate descriptor combinations, and optimization strategies that can increase the value of QSPR models. Here we review the utility of some of the more popular applications of computational intelligence to QSPR modeling including: artificial neural networks, fuzzy logic, and evolutionary computing.

Key Words: Computational intelligence, evolutionary algorithms, artificial neural networks, fuzzy logic, machine learning, support vector machines, QSPR, ADME-tox, high-throughput screening, virtual screening. 1. INTRODUCTION The discovery and development of a new drug often takes 12-15 years to bring to market at a cost of more than $1.3B [1-3]. For every 5,000-10,000 compounds screened, only 250 enter preclinical testing. Of these, only 5 will survive to enter clinical testing with only 1 approved drug by the U.S. Food and Drug Administration (FDA) after an average of 15 years of total research and development [2, 4]. Only 2 out of every 10 approved and marketed drugs recover their research and development costs [2]. It has been estimated that more than 75% of the high cost of drug discovery and development is actually spent on compounds that fail later in the more costly portions of the drug development process (e.g., during clinical development) [5]. In fact, for the very expensive Phase III clinical trials, only half of compounds tested end up being approved [6]. During the 1980s and 1990s, roughly 40% of the failures during clinical trials were attributed to the absorption, distribution, metabolism, and excretion (ADME) properties of the clinical candidates [7, 8]. A more detailed analysis of Phase I failures indicated that during this period 33% failed for lack of efficacy, 9% for market reasons, 18% for toxicity and adverse events, and 40% for poor pharmacokinetic and ADME properties [9, 10]. *Corresponding Author: Tel: (619) 421-6700; E-mail: [email protected] Gary W. Caldwell / Atta-ur-Rahman / Z. Yan / M. Iqbal Choudhary (Eds.) All rights reserved – © 2009 Bentham Science Publishers.

352 Frontiers in Drug Design & Discovery, 2009, Vol. 4

Hecht and Fogel

In light of these data, in the late 1990s pharmaceutical and biotechnology companies realized the importance of optimizing absorption, distribution, metabolism, excretion as well as toxicological (ADMET) properties in early phases of the drug discovery and development process [9,11]. Traditionally, compounds were first identified through screening and then optimized for potency and specificity for molecular targets with in vitro enzyme/binding assays or in vivo cell-based assays. Optimization of ADMET properties was reserved for pre-clinical and clinical development. A typical clinical development process is shown below in Fig. (1).

Drug Clinical Development: 10-15 yrs > $800,000

IND Filing

Phase I Clinical Trials

Phase II Clinical Trials

Phase III Clinical Trials

• 6 months to > 1-2 yrs • phamacokinetics, pharmacodynamics, & toxicology • carcinogenicity, reproductive safety • dosing, animal studies, metabolites & stability • regulatory: QC/QA • GMP/GLP manufacturing & scale up

•1-2 or more years •small trials: mostly normals – some efficacy •focused on safety

•2-5 years •safety •more focus on efficacy •larger trials

•2-5 years •large trials •clinically relevant end points

NDA Fig. (1). A “typical” clinical development workflow from the initial new drug (IND) filing through the three phases of clinical trials culminating in the new drug application (NDA) and market launch. At each stage of this process drug candidates are eliminated as the costs increase exponentially.

Unfortunately the very same physico-chemical properties that were optimized for potency in new leads discovery and optimization often resulted in poor ADMET profiles. For example, lead compounds that were optimized for high molecular weight and

Computational Intelligence Methods

Frontiers in Drug Design & Discovery, 2009, Vol. 4 353

increased lipophilicity tended to have high potency but poor absorption. These observations lead to the development of “filters” that could be used to select for more “druglike” characteristics of small molecules. Perhaps the most famous of these is Lipinski’s “Rule of Five” [12]. Caution has to be exercised when using these filters blindly as there are examples in the literature of very successful drugs that fail one or more filter criteria [13]. In order to avoid spending hundreds of millions of dollars for compounds that fail in late development, the paradigm quickly switched to elimination of high-risk compounds in early phases. In silico modeling in addition to high-throughput in vitro screening was very quickly and widely adopted [14-16]. These included various property predictions as well as quantitative structure-property relationship (QSAR) and quantitative structureproperty relationship (QSPR) models [5, 17-20]. As a testament to the successful widespread adoption of pre-clinical ADME screening and testing, the clinical failure rate due to ADME has dropped to 10-14% in 2008 [7,9,10]. An analysis of recent Phase I failures indicated that 36% failed for lack of efficacy; 7% for “other,” 43% for toxicity and adverse events, and only 14% for poor pharmacokinetic and ADME properties. Currently toxicity and lack of efficacy are the main causes for failure [10]. Even more troublesome is the observation that greater than 90% of recent market withdrawals have been due to toxicity causing adverse events and side effects in patients [7, 9, 10]. QSAR and QSPR models have proven to be an effective approach for handling the massive quantities of structural and biological data generated with combinatorial libraries and HTS in lead discovery, lead optimization, and drug development [21]. QSAR/QSPR models are essentially a function relating parameters/descriptors/features based on physicochemical properties of small molecule compounds to a biological response. These descriptors are quite easy to calculate for small molecules. However, only a fraction of the descriptors are truly useful for predicting activity or other properties. In addition, some descriptors that are not very useful on their own, may be very informative when in combination with other descriptors. Modeling approaches that can relate the appropriate selection and weighting of descriptors in automated, improved, and efficient ways is a very active area of research. One rather new development has been the interest in applying existing tools and techniques from the field of computational intelligence [22]. Computational intelligence (CI) is a broad field of computer science that makes use of nature-inspired modeling paradigms for optimization and pattern recognition. These approaches, such as artificial neural networks (ANNs), fuzzy logic, evolutionary computation (EC), can be used in addition to other machine learning methods to automatically select, analyze and interpret relevant data and information [23-27]. Here we review the utility of some of the more popular applications of computational intelligence to QSPR modeling. This paper is organized as follows. In the next section ADME and toxicological properties are briefly summarized. The subsequent section briefly describes current QSAR and QSPR technologies. An additional section introduces general concepts of computational intelligence and the section following presents examples of their application to QSPR modeling. The final section discusses some of the future challenges for ADMEtox modeling.


Hecht and Fogel

2. ADMET Pharmacokinetics is the study of what happens to drugs as they are administered and pass through the body. Oral administration is the preferred method for most drug development programs as it is convenient and helps to ensure compliance. An orally-administered drug dissolves in the stomach and/or intestine and passes through the intestinal wall. In many cases, drugs first enter the liver where they are processed by enzymes, the most important class of which are the cytochrome P450s. Metabolized drugs can often become quite toxic or have other negative effects on the body. Unprocessed drugs then exit via the portal vein. The percentage of the initial dose that reaches the circulatory system is called the “bioavailability” of the drug. The “half-life” or t of a drug is the time it takes for the blood plasma concentration of the drug to reach of its initial value. Serum albumin and other proteins often bind the drugs and prevent them from reaching their intended targets. A fraction of the unbound drug is then distributed to the various organs and tissues where it binds to its target and has its effect. Ultimately, the remainder is excreted and passes out of the body. Pharmacodynamics is the study of the effects a drug has on the body and on metabolism. These effects can be positive or negative. Positive effects include the desired effects of the drug. Negative effects can include genotoxicity. This section presents a brief introduction to adsorption, distribution, metabolism, excretion and toxicology. 2.1. Absorption In order for orally administered drugs to reach their intended molecular targets, they first need to dissolve in the stomach and/or small intestine and then pass through the epithelial cell layer in the intestine in order to get into the circulatory system. Drugs targeted for the central nervous system (CNS) need to pass through an additional barrier, the blood-brain barrier. Yet another barrier of interest is the skin – for topical administration and absorption. Drugs generally move across epithelial barriers, such as the one in the small intestine, via the paracellular pathway (between cells) or via the transcellular pathway (through cells). Generally, only small molecular weight compounds ( 5

c)

number of hydrogen-bond donors (-OH and -NH groups) > 5

d)

number of hydrogen-bond acceptors (N and O atoms) > 10

Similar analyses have been performed that include additional descriptors such as: molar refractivity [39], counts of the number of rings, rotatable bonds, as well as hydrogen bond donors and acceptors [41, 42].


Hecht and Fogel

Recent analyses comparing drug candidates in development with marketed drugs concluded that larger, more lipophilic compounds tended to be identified from highthroughput screening of compound libraries in lead discovery [42, 43]. As compounds pass through the different stages of pre-clinical and clinical development, the mean molecular weight of drug candidates tends to converge to that of marketed drugs. Likewise, many promising lipophilic compounds are discontinued as development proceeds. Additionally it was determined that drugs developed for oral administration tended to be lighter and had fewer rotatable bonds and hydrogen bond acceptors and donors than drugs developed for other indications [43]. These observations explain a significant portion of the inefficiency and high-attrition rates of current drug discovery and development approaches. A major limitation of using predictive rules like the Rule-of-Five is that many of the “inactive” or “non-druggable” compounds in commercially available screening libraries also obey these rules. For example 68.7% of the compounds in the Available Chemical Directory (ACD) screening database (containing >2.4 million compounds) do not violate the Rule-of-Five [44]. In other words, simple ADME filters are not enough to eliminate “non-druglike” molecules from screening libraries. This often results in precious resources being spent on optimization and development of drug candidates that ultimately fail. The later the failure in the drug discovery process, the greater the cost. This has given rise to the mantra: “fail fast, fail early.” In order to address this deficiency, more sophisticated in silico models of absorption, often modeling activities in in vitro assays such as Caco-2 or MCDK and BBB, have been developed [44]. Many of these models use computational intelligence methodologies such as ANNs, EC, and fuzzy logic and will be discussed in more detail in Section 5. Several commercial software packages for the prediction of drug absorption include properties such as: aqueous solubility and partition coefficients; Caco-2 cell permeability; BBB permeability; MDCK cell and skin permeability; and cell absorption. Some representative programs (and companies) include: ADMET Predictor (Simulations-Plus, www.simulations-plus.com); Discovery Studio, TOPKAT and Accord (Accelrys, www. accelrys.com); ChemSilico (ChemSilico, www.chemsilico.com); KnowItAll (Bio-Rad, www.biorad.com); ADME boxes (Pharma-Algorithms, www.pharma-algorithms.com); Pre-ADMET (www.preadmet.bmdrc.org); QikProp (Schrödinger, www.schrodinger. com); VolSurf (Molecular Discovery, www.moldiscovery.com); VolSurf and Sybyl (Tripos, www.tripos.com); and various MDL databases and tools (Symyx, www.mdli. com). 2.2. Distribution Prediction of tissue distribution of a drug is a very important consideration in drug development. Descriptors such as logP, molecular weight, as well as acidity have been proven to be useful in modeling distribution [45]. Additional terms are usually added to account for plasma-protein binding, tissue composition, blood composition, as well as blood flow to the tissues [46-49] Modeling of plasma protein binding is also very important as bound drugs are often prevented from crossing cell membranes and getting to their intended targets [50]. On the other hand, drugs that bind to proteins tend to have a longer t [51]. Plasma proteins



that bind drugs include: albumin (for acidic drugs); 1-acid glycoproteins (for basic drugs); lipoproteins (for neutral and basic drugs) as well as erythrocytes and ,,globulins [20, 51]. Several commercially available software packages exist for prediction of multicompartment pharmacokinetic models, drug release, dissolution, and transport, elimination half-life, and plasma protein binding. Some representative examples include: GastroPlus (Simulations-Plus, www.simulations-plus.com); Pre-ADMET (www.preadmet.bmdrc. org); and KnowItAll (Bio-Rad, www.biorad.com). 2.3. Metabolism Predicting the potential interactions and metabolic pathways of a drug is extremely difficult. There is great interest in developing models for biotransformation (e.g., toxic metabolites & intermediates), enzyme and/or receptor binding and inhibition (e.g., cytochrome P450, hERG potassium channels), and synergistic/antagonistic drug-drug interactions [52, 53]. Biotransformation studies are important to identify what enzymes are metabolizing a drug, what metabolites are produced and if so, how they are cleared [54, 55]. If these metabolites are reactive, they can cause toxicity or other adverse events. Rule-based expert systems have been developed based on chemical similarities and decision trees [55]. These include: MDLI Metabolite Database (Symyx, www.mdli.com); Meteor (Lhasa, www.lhasalimited.org); MetaDrug (GeneGo, www.genego.com); MexAlert and MetabolExpert (CompuDrug, www.compudrug.com); and MetaSite (Molecular Discovery, www.moldiscovery.com). Potential cytochrome P450 interactions (as well as interactions with other metabolically important enzymes) have traditionally been studied using QSAR models [56, 57]. Because of the potential for arrhythmia and cardiac failure, there is also currently interest in developing QSAR models for potential interactions with the hERG potassium channel [58, 59]. Unfortunately there have been relatively few models of metabolic stability and its effect on t, or on potential drug-drug interactions [54]. Because of the complexity of modeling metabolism and metabolic pathways, there is currently great interest in applying computational intelligence methodologies. This will be discussed in more detail in Section 5 below. 2.4. Excretion Currently very little effort has been directed towards in silico models of excretion processes [36]. While most drugs are excreted to via the kidneys or the bile to some extent, for the most part they are eliminated via other routes (e.g., they are metabolized). 2.5 Toxicology As toxicity is currently the major reason for drug candidate failure in clinical trials, there is currently considerable interest in developing predictive in silico models. These models generally fall into one of two following categories: expert systems (based on rules generated from human experts as well as the scientific literature) and QSAR models – in particular for cytochrome P450’s and hERG receptors (as discussed in Section 2.3) [60-62].


Hecht and Fogel

Expert systems have been used to predict toxicological endpoints that include: rodent carcinogenicity; Ames mutagenicity; developmental toxicity potential; skin and eye irritation; acute oral toxicity LD50; acute inhalation toxicity LC50; acute toxicity LD50; acute toxicity EC50; maximum tolerated dose (MTD); chronic lowest observable adverse effect level (LOAEL); and skin sensitization [63]. Several representative examples of predictive toxicological software include: Actelion Property Explorer (Actelion, www.actelion.com); ADMET-Predictor (SimulationsPlus, www.simulations-plus.com); ChemSilico (ChemSilico, www.chemsilico.com); DEREK (Lhasa, www.lhasalimited.org); Hazard Expert (CompuDrug, www.compudrug. com); KnowItAll (Bio-Rad, www.biorad.com); LAZAR (www.predictive-toxicology. org/lazar/); MCASE, CASE, MTOX (Multicase, www.multicase.com); OncoLogic (www.epa.gov/oppt/sf/); Pre-ADMET (www.preadmet.bmdrc.org); TOPKAT (Accelrys, www.accelrys.com); ToxBoxes (Pharma-Algorithms, www.pharma-algorithms.com); and ToxScope (Lead Scope, www.leadscope.com). Recent developments of microarray technologies have completely transformed the fields of toxicogenomics and pharmacogenomics [64-71]. Not only are microarray experiments used identifying biomarkers and validating drug targets, they are also used to study the metabolic and potential toxicological effects of compounds in a highthroughput mode. The amount of data generated from these experiments is astronomical and CI approaches are routinely employed in these analyses [64-71]. 3. QSAR & QSPR QSAR models are in essence a mathematical function that relates features and descriptors generated from small molecule structures to some experimental determined activity or property. The first QSAR models introduced in 1969 were rather simple, identified the relationship between the water-octanol partition coefficient and biological activity [38]: log(1/C) = k1logP – k2(logP)2 + k3s + k4

(1)

where C is the concentration of the compound that gives a biological response, P is the water-octanol partition coefficient, and k1, k2, k3, and k4 are constants. QSPR models are used often to model and predict ADMET properties. QSAR and QSPR are very similar in that much of the same computational approaches are used in their development and optimization. The major differences arise from the activities/properties they are designed to predict. For QSAR models, relevant biological responses most often include: the concentration needed to inhibit 50% of activity (IC50); the dose required to reduce activity by 50% in cell based or animal studies (ED50); the inhibition constant, Ki; as well as the bonding constant Kd. As mentioned above, QSPR models are generated to predict physico-chemical properties and as well as biological activities relevant to ADMET. These often include the dose required to kill 50% of the cells or animals tested (LD50), solubility, lipohilicity and partition coefficients, absorption through intestinal walls, measures of cell membrane permeabilities, as well as BBB penetration. Some of the more widely used commercially available software packages for performing QSAR and QSPR include Cerius2 and Cata-



lyst (Accelrys), MOE (Chemical Computing Group), OpenEye, MDL, and Sybyl (Tripos). 3.1. QSAR and QSPR: 1D & 2D Models QSAR and QSPR models are based on molecular descriptors or features. While there are literally thousands of descriptors available, they generally fall into one of four major classes: 1) counts of features; 2) physico-chemical properties; 3) topological indices and atom connectivities; and 4) calculated intramolecular energies. The first class includes descriptors such as hydrogen bond acceptors, hydrogen bond donors, aromatic ring systems, carbonyl groups, basic nitrogens, and carboxyl groups. Descriptors based on predicted physico-chemical properties include dipole moments, volumes, polarizabilities, water-octanol partition coefficients, solubilities, molecular weights, melting points, boiling points, heat of sublimations, and molar refractivities. Topological indices and atom connectivities are based on the two- and three-dimensional structures of compounds. These include branching indices, kappa shape indices, electrotopological state indices, atom-pairs, topological torsions, as well as surface areas both polar and non-polar. Finally, there are many descriptors based on calculated intramolecular energies using both quantum mechanical as well as empirical methodologies. Numerous software packages are available for generating molecular descriptors. Some of the more popular and well known ones include: Sybyl (Tripos), Catalyst and Cerius2 (Accelrys), MOE (Chemical Computing Group), OpenEye, and Dragon. QikProp (Schrödinger) is primarily focused on generating descriptors and predicted activities relevant to ADMET. Because of the large number of descriptors available, the rate limiting step in the development of QSAR and QSPR models is often their identification, appropriate reduction, and weighting. A variety of techniques are typically employed for this purpose including multiple linear regression (MLR), partial least squares regression (PLS), and principle component analysis (PCA). MLR is perhaps the most widely used method for modeling linear correlations between descriptors and activities. For best results, the number of samples should be > 2n, where n is the number of descriptors. It is also important that descriptors used are not significantly correlated in order to avoid redundancies. PLS is useful for cases where the number of samples is small with respect to the number of descriptors. Unfortunately this is very often the case in drug discovery and development where data points are often very expensive and difficult to obtain. In PLS there is a linear transformation of the original descriptors into a new space composed of a smaller number of orthogonal variables. PCA is useful for transforming a large number of correlated descriptors into a far fewer number of orthogonal descriptors or principal components. The first principal component accounts for as much of the variability as possible, with each subsequent principal component accounting for additional variability. Development of improved and more efficient strategies is a very active area of research, and some of the more popular techniques include computational intelligence methodologies such as ANNs and evolutionary algorithms (EAs) [72-74]. In particular, ANNs have proven useful for selection of features that are nonlinearly correlated to small molecule activities [75-83]. The “genetic function approximation” (GFA) is another variation of evolutionary computing in which populations of QSAR models are generated and optimized [84].


Hecht and Fogel

This approach has become very popular and is included in Accelrys’ Cerius2 software package for QSAR model generation. 3.2. Pharmacophore Modeling, CoMFA and CoMSIA QSAR and QSPR models using pharmacophore modeling have proven to be very useful in drug discovery and development [85-87]. A pharmacophore is essentially the three dimensional substructure of an active compound or structure class that is both necessary and sufficient for bioactivity. The first step in generating a pharmacophore requires generation of a 3D structural alignment of a set of active compounds. Common structural and chemical features in the aligned structures are then identified and the distances and angles between the features are calculated. These features often include: hydrogen bond donors and acceptors, charged or polar groups, as well as aromatic groups. These models are extremely computationally efficient and large numbers of compounds (literally millions) can be screened against these models. Scoring is based on how well they fit the model. One very popular variation on pharmacophore modeling is comparative molecular field analysis (CoMFA) [85, 87]. As in pharmacophore modeling, a 3D structural alignment is performed on a set of training compounds. However, for a CoMFA model, the structural alignment is performed in a lattice of grid of points to which a molecular force field is applied [88]. Interaction energies are calculated for the molecule at each point of the lattice. These energies typically have steric, electrostatic and hydrophobic terms. Because of the large numbers of descriptors, PCS and/or PLS are typically used to reduce the number of descriptors during model development. Comparative molecular similarity indices analysis (CoMSIA), is very similar to CoMFA but is instead based on similarity [89]. 4. COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING The field of computational intelligence has many tools and techniques for building predictive models for processes that are extremely complex and where our understanding of the fundamentals is limited [76, 90, 91]. There are very few problems more complex than that of modeling biological responses in response to administration of a drug. It should not be surprising, then, that many of these tools and methodologies have been successfully applied to QSAR & QSPR modeling [22, 59, 92]. In QSAR & QSPR models, these computational intelligence approaches are used to predict experimental activities based on descriptors or features requiring a method of supervised learning. Perhaps one of the most useful applications has been that of feature selection. As mentioned previously, there are literally thousands of descriptors currently available. This section presents a brief introduction to ANNs, fuzzy logic, EC, as well as other machine learning approaches. 4.1. Artificial Neural Networks Artificial neural networks are transfer functions modeled loosely after the neural architecture of the human brain that accept some number of input features and yield some output decision. ANNs (or more commonly referred to as simply “neural networks”) are patterned after the neuronal structure of the brain as a tool for pattern recognition [9395]. Supervised learning of ANNs occurs using a training set of examples in which the neural net learns the relevant mapping of inputs to output decisions.



A typical ANN architecture consists of an input layer, one or more hidden layers, and an output layer. An example is shown in Fig. (2). Linear neural net models do not have a hidden layer: input nodes are directly connected to the output node(s). Non-linear models have at least one hidden layer with connections to both the input layer and the output layer. The number of connections between the nodes of each layer and their relative weightings will vary from model to model. For QSAR and QSPR, inputs to the ANNs are molecular descriptors and the output is a decision concerning the predicted activity or other property [77]. As was the case for use of MLR, PLS, and PCA, feature selection needs to be performed in order to select which ones to include or exclude from the model as input. Each input or feature then needs to be weighted with respect to maximizing predictive accuracy on the output decision over the training examples. The relative weights of each input are often unknown.

Output Layer

Input Layer

Hidden Layer(s)

Fig. (2). An artificial neural network architecture using five input nodes, one hidden layer with four nodes, and two output nodes. This architecture is a feed-forward multi-layer perceptron. Other architectures are possible making use of recurrence, a variable number of connections, variable number of nodes, nodes per layer, layers, processing elements internal to each node.

Optimization of the relative weights and/or the architecture of the ANN (e.g., the connections between layers) can be performed in order to minimize the mean squared error (MSE) between the predicted output and actual values over the training set. For example:

MSE =

1 N

N

(P k =1

k

Ok ) 2

(2)

where P was the predicted activity, O was the observed activity, and N was the number of patterns in the training set. Backpropagation is one of the most commonly used of the training algorithms for weight adjustment. A validation set of held-out examples (not used for training) is used to test the best model. The model can be re-designed if neces-


Hecht and Fogel

sary. In some cases a second held-out testing set of data is used to assess final predictive accuracy. 4.2. Fuzzy Systems Fuzzy systems which are based on fuzzy set theory [96, 97], attempt to build models that capture uncertainties and imprecision not easily quantified by other methods. Fuzzy algorithms have proven useful for clustering or classification in bioinformatics [98-102] where they are used to handle uncertainties in rule-based representations. For prediction of “drug-likeness” a fuzzy model representation might take the form: IF the activity score is ACTIVE and compliance to the Rule-of-Five is MOSTLY TRUE THEN the decision of drug-likeness is TRUE For prediction of toxicity a fuzzy model representation might take the form: IF the structure is SIMILAR TO a known cytrochrome P450 inhibitor and the predicted metabolite score is ACTIVE THEN the decision of toxicity is TRUE Fuzzy systems seem ideal for modeling toxicity and metabolism where the inputs used to generate the model do not cleanly separate into discrete values or are subjective. Whereas other methods would force the inputs or continuous variables into partitions on user defined discrete intervals, a fuzzy system can be designed to represent membership in vaguely defined partitions. This is useful when the discrete interval boundaries are largely subjective and/or difficult to determine empirically. There are many subdisciplines of fuzzy logic theory that have been developed to handle linguistic variables, and many of these are appropriate for use in biological problems such as prediction of toxicity or of metabolism. 4.3. Evolutionary Computation Evolutionary algorithms are designed to mimic natural evolution as a populationbased optimization process. An typical example is provided in Fig. (3). EAs use random variation and selection as a means for discovering solutions to complex problems. A typical evolutionary computation process starts with an initial set (population) of solutions. These are randomly altered (e.g., mutated and/or recombined) to generate the individuals comprising the current population which are subsequently evaluated using a fitness function (defined by the user). Based on their scores, a subset of individual solutions in the population are chosen to be parents for the next generation. The cycle then continues with random alteration, scoring with the fitness function and then selection. This continues until a halting criterion has been met, such as a specific number of generations or exceeding the available time. Methods of evolutionary computation include evolutionary programming [103], evolution strategies [104], genetic algorithms [105107], genetic programming [108], particle swarm optimization [109], ant-colony optimization [110], differential evolution [111, 112], and others. Each approach has its own advantages and disadvantages relative to specific problems. The “No Free Lunch” theorem indicates that no single optimization approach will work best over all problems [113].



4.4 Evolved Artificial Neural Networks and Evolved Fuzzy Systems One very powerful application of evolutionary computing has been the optimization of the connections (and weightings) between the input layer, the hidden layer(s), and the output layers in neural networks [114, 115]. The evolutionary algorithm creates populations of ANNs and scores each ANN based on mean squared error between the predicted and actual outputs. Likewise, evolutionary algorithms can be used to optimize any fuzzy classifiers or fuzzy inputs that are used in ANNs (e.g., fuzzy neural nets) or in fuzzy systems [116]. Evolutionary computing can also be used to evolve the selection of features to be used in a neural net model simultaneous with the optimization of that model’s architecture [117-120].

Population Initialization

Random Variation

Fitness Scoring

Parent Solutions

Process Termination

Fig. (3). A flow diagram of a standard evolutionary algorithm. The loop of variation, scoring, and, generation of parent solutions for the next “generation” of evolution continues until a termination criterion is satisfied.

4.5. Other Common Machine Learning Approaches Support vector machines (SVM) have recently been used for prediction of compound activities [121, 122]. Support vector machines represent the input descriptors/features as vectors that are projected onto higher-dimensional space. An optimal hyperplane is then constructed separating the actives and inactives. The hyperplane is used to predict the activity of new compounds that are tested [123-125]. Other techniques employed for modeling of ADMET properties include clustering and decision trees with recursive partitioning [22, 44, 59, 126]. K-means clustering is one of the oldest and most widely used clustering methods. Data are grouped by similarities in their features/descriptors. Decision trees consist of nodes where each node is connected to all the outcomes of a decision based on a single attribute. Recursive partitioning is often used to examine every attribute of the data and rank them with regards to their ability to partition the rest of the data. In general, the tree is first grown to its full size by evaluating each and every attribute and generating nodes for each outcome. The tree is then pruned back based on its predictive performance. 5. COMPUTATIONAL INTELLIGENCE AND ADMET MODELING In this section, current applications of computational intelligence to predictive ADMET and QSPR models are reviewed. These models have focused on a relatively small (but very important) subset of ADMET properties and activities reflecting the needs of drug development programs to increase the survivability of drug candidates. 5.1. Absorption Absorption is critical to the development of orally available pharmaceuticals. Models for aqueous solubility, intestinal absorption, Caco-2 permeability as well as BBB penet-


Hecht and Fogel

ration are well established and are routinely applied in lead discovery as a screen for “non-druglike” compounds. As these models predict properties and activities related to solute-solvent interactions including hydrophobic interactions, they often employ topological and surface property based descriptors such as PSA. Aqueous solubility is perhaps one of the most commonly modeled ADMET properties. Solubility refers to the maximum amount of compound that can dissolve in a given quantity of water. These models are commonly used as experimental determinations of solubility are very costly in terms of time, money and perhaps most importantly, the amount of compound used. In general, milligrams of compound are required for solubility determination. Table 1 provides representative examples of computational intelligence based aqueous solubility models. While MLR was a common approach used method early on [130-135], in recent years there has been an increased use of ANNs and EAs [120, 144152, 155-163]. When EAs are combined with other techniques such as ANNs, they are most often used for feature selection [162]. However, EAs can also be used effectively in order to evolve the ANNs themselves [120]. Table 1.

Models of Aqueous Solubility

Reference #

Method

Descriptors

127-139

MLR

calculated molecular descriptors

140, 141

MLR

topological and molecular descriptors

142

MLR

surface and calculated molecular descriptors

143

PLS

infrared spectral data

144-147

ANN


148-156

ANN


157-160

EA


161

EA


120, 162

EA & ANN


163

ANN & Fuzzy Logic

topological descriptors

164

SVM


165

EA, ANN, SVM


Experimental determinations of intestinal absorption are generally very low throughput, extremely time consuming, and require costly animal models [166]. Because of these considerations, in silico models are very commonly used – particularly early on in drug discovery and development. Table 2 provides representative examples of human intestinal absorption models. While MLR and PLS were the techniques most commonly



used in the development of these models, there are a number of models using ANNs and EA as well as decision trees and recursive partitioning [183]. In two models, EAs were used for descriptor selection and then ANNs were generated [181, 182]. While the types of descriptors used in these models varied considerably, topological and surface properties were often used as well as H-bonding terms and logP values. Table 2.

Models of Intestinal Absorption

Reference #

Method

Descriptors

167-169

MLR


170

MLR

H-bond descriptors and other calculated molecular descriptors

171

MLR

molecular groups

172

MLR

PSA

173

PLS

MolSurf

174

PLS

H-Bond descriptors and logP

175

PLS


176

PLS

atom types

177

ANN

molecular hashkeys

178

ANN

PSA, logP, and topological descriptors

152

ANN


158, 179, 180

EA


181, 182

EA & ANN


183

Recursive Partitioning


184

Decision Trees


164, 185-187

SVM


Although Caco-2 permeability studies are less costly and easier to run than other intestinal absorption models, there remains great interest in using in silico filters – especially when screening large libraries [9, 11]. As was the case for the intestinal absorption models, both MLR, PLS, and ANNs were the techniques most commonly used. Again, topological and surface property based descriptors proved to be the most useful for these models. As experimental models of BBB penetration tend to be relatively expensive and low throughput [166]. It is imperative that drugs targeted for CNS indications are able to pass through this barrier. For these reasons, in silico BBB permeability filters are applied early in drug discovery and development. Table 4 lists BBB models constructed using


Hecht and Fogel

MLR, PLS, ANNs as well as EAs and SVMs. Of particular interest are models that combine computational intelligence techniques [229, 231]. Table 3.

Models of Caco-2 Permeability

Reference #

Method

Descriptors

188, 189

MLR

PSA and MW

190, 191

MLR

topological and surface descriptors

192, 193

MLR


194

MLR

H-bond and molecular descriptors

175, 195, 196

PLS


197

PLS

MolSurf descriptors

198

PLS

VolSurf descriptors

174

PLS

logP and H-bonding descriptors

181

ANN


199-201

ANN


100

ANN


101

ANN


202

EA


158

EA


203

SVM


Table 4.

Models of BBB Permeability

Reference #

Method

Descriptors

204-207

MLR

PSA, logP & molecular descriptors

208-214

MLR


215

MLR


216

MLR

Solvation energy

217

MLR

PSA, H-bond descriptors, logP

174

PLS

H-bonding descriptors and logP

218

PLS

MolSurf descriptors


Frontiers in Drug Design & Discovery, 2009, Vol. 4 367 (Table 4) contd....

Reference #

Method

Descriptors

219

PLS

surface and molecular descriptors

175, 196, 220, 221

PLS


222

PCA

VolSurf descriptors

223

ANN


58, 82, 224, 225

ANN


226

EA


227

EA, CoMFA, CoMSIA


164, 228

SVM


229

SVM & ANN


230

Decision Tree


231

ANN, SVM, Clustering, & Decision Tree


5.2. Distribution, Clearance, and Metabolism In addition to optimizing compounds for the ability to be absorbed, it is also very important to optimize compounds for their distribution to different organs and tissues, their clearance from the body, as well as their metabolic stabilities. As was the case for absorption, in silico models are used routinely to screen compounds for these purposes. Models of cytochrome P450 activity are extremely important for evaluating the potential for metabolism and reactive intermediate formation before compounds transit through the portal vein and into general circulation. As these models are designed to predict enzyme activity they often include 3D-QSAR techniques such as pharmacophore modeling [245, 246] as well as CoMFA [252, 253]. Examples of modeling techniques such as ANNs, EAs, SVMs, MLR, and PLS are also presented in Table 5. Table 5.

Models of Predicted Cytochrome P450 Activity

Reference #

Method

Descriptors

232, 233

MLR

surface descriptors, logP

234

PLS

logP

158

EA


235, 236

ANN


164, 237-242

SVM



Hecht and Fogel

(Table 5) contd....

Reference #

Method

Descriptors

243

ANN, SVM


244

3D QSAR

structural fragments

245, 246

3D QSAR

pharmacophore

56, 247-251

3D QSAR


252, 253

3D QSAR

CoMFA

254

Clustering


255-257

Clustering

microarray gene expression data

As mentioned previously, once drugs enter the blood stream, they often bind to blood proteins such as albumin and may be prevented from reaching their targets. Again, in silico models to predict albumin binding have also proven to be very useful drug development [9,11]. Some representative examples of these models are presented in Table 6. A variety of techniques have been used including ant colony optimization, a type of evolutionary algorithm [264]. Table 6.

Models of Human Serum Albumin Binding

Reference #

Method

Descriptors

258

MLR


259-260

MLR

logP, topological descriptors, PSA

261

PLS


58, 262, 263

ANN


180

EA


264

EA (Ant Colony Optimization)


164

SVM


265

Expert System

pharmacophores

Although there are fewer examples, models of clearance, t and metabolic stability are also very important. Table 7 lists several based on EAs and SVMs. Examples of models based on fuzzy logic or clustering are also provided.


Table 7.


Models of Clearance & Metabolic Stability

Reference #

Method

Property

266

Fuzzy Systems

clearance time

180

EA

urinary excretion

267

EA

volume of distribution

268

Clustering

metabolic stability

269

SVM

metabolic stability

164

SVM

t and volume of distribution

5.3. Toxicity Currently toxicity is the major cause of drug candidate failure during development and clinical trials and is responsible for >90% of the drugs pulled off the market [7, 9, 10]. The ramifications of toxicity are enormous not only in terms of costs, but in actual lives. One of the most common examples is that of hERG receptor modeling. In some cases, drugs will bind to the hERG receptor and cause arrhythmia and hear failure. It is therefore important to screen out compounds with the potential for this adverse effect as early as possible. The examples presented in Table 8 utilize EAs and SVMs. Table 8.

Models of Predicted hERG Receptor Binding

Reference #

Method

Descriptors

270

EA


271

EA & CoMFA


58, 272-274

SVM


275

SVM and Clustering


Other examples of in silico toxicology models are presented in Table 9. While most of these models have been generated using molecular descriptors, there are a couple based on gene expression profiles from microarray data. 6. GENERAL TRENDS AND FUTURE DIRECTIONS Table 10 presents an analysis of the literature cited in Tables 1-9 grouping them by year and methodology. From this analysis, a couple of general overall trends for the field seem to emerge, although the data presented here is only a sampling of the available literature.


Table 9.

Hecht and Fogel

Models of Toxicity

Reference #

Method

Descriptors

Toxicity

152

ANN


Ames genotxocity

276

EA & ANN


drug transfer to breast milk

277

ANN, SVM, Clustering, Decision Trees


overall predicted toxicity

278

SVM & ANN



70

Clustering

gene expression profiles


279

Clustering

gene expression profiles

hepatotoxicity

280

Clustering

structures and data in LAZAR

Ames genotxocity

281

Clustering

Structures and molecular descriptors


Prior to 2000, MLR appeared to be the preferred methodology for generating ADMET models. This changed in the early 2000s, when computational intelligence-based approaches became more popular - particularly with the use of ANNs and EAs. Since 2005, SVM and clustering approaches (including decision tree analyses) have also been used with increasing regularity. The increased use of clustering and decision tree analyses in recent years reflects the great interest in developing models of metabolism and toxicity using gene expression data coming from microarray data. Because of the astronomical quantity of data produced, it is to be anticipated that computational intelligence methodologies will continue to play a major role. Table 10. Number of References Cited Grouped by Methodology and Year

Method

1980s-1999

2000-2004

2005 -2008

ANN

6

21

7

EA

2

9

9

MLR

18

28

2

PLS

7

8

0

SVM

0

3

16

Clustering & Decision Trees

0

2

9



Again, this reflects the shift in recent years from development of newer and improved models relevant to absorbance to models of predicted metabolic activities and toxicity. Perhaps one of the largest hurdles to overcome is the amount of proprietary metabolic and toxicity data maintained in the databases of pharmaceutical companies. A promising recent development has been microarray data that has been made public (from NCBI as well as other sources). Another very important area of opportunity is the development of improved models of distribution and clearance. There are currently relatively few examples of applications of computational intelligence and this is an area of likely future application. 7. CONCLUSIONS As discussed previously, there is great interest in developing new and improved ADMET models in order to improve the efficiency and productivity of drug discovery and development. Because of the great complexities, scarce and “noisy data,” as well as overwhelming numbers of parameters involved, researchers have borrowed heavily from the field of computational intelligence and machine learning. In this paper we have reviewed applications of computational intelligence methods to the development of predictive ADMET models. There is a great opportunity for the development of novel approaches and methodologies that will increase the likelihood of survival of drug candidates through the development process. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]

DiMasi, J.A.; Grabowski, H.G. Manage. Decis. Econ., 2007, 28, 469-479. PhRMA Industry Profile 2008 Report, www.phrma.org/publications Center for Drug Evaluation and Research: www.fda.gov/cder/rdmt Tufts Center for the Study of Drug Development, Backgrounder: How New Drugs Move through the Development and Approval Process (November 2001). Darvas, F.; Keser, G.; Papp, Á.; Dormán, G.; Ürge, L.; Krajcsi, P. Curr. Top. Med. Chem., 2002, 2, 1287-1304. Von Eschenbach, A.C. “Statement Before the Senate Agriculture, Rural Development, Food and Drug Administration and Related Agencies Appropriations Subcommittee, ” U.S. Food and Drug Administration, June 2007, www.fda.gov/ola/2007/criticalpath060107.html (accessed 27 November 2007). Kennedy T. Drug Discov. Today, 1997, 2, 436-44. Prentis, R.A.; Lis, Y.; Walker, S.R. Br. J. Clin. Pharmacol., 1988, 25, 387-96. Tsaioun, K. Drug Discov., 2007, 20, 21. Schuster D.; Laggner, C.; Langer, T. Curr. Pharm. Des., 2005, 11, 3545-59. Weiss, A.J. www.dddmag.com, 2002, 27. Lipinski, C. A.; Christopher, A. L. Adv. Drug Deliv. Rev., 1997, 23, 3-25. Kubinyi, H. Nat. Rev., 2003, 2, 665-668. Lahoz, A.; Gombau, L.; Donato, M.T.; Castell, J.V.; Gómez-Lechón, M.J. Mini Rev. Med. Chem., 2006, 6, 1053-1062. Wunberg, T.; Hendrix, M.; Hillisch, A.; Lobell, M.; Meier, H.; Schmeck, C.; Wild, H.; Hinzen, B. Drug Discov. Today, 2006, 11, 175-180. Kumar, R.A.; Clark, D.S. Curr. Opin. Chem. Biol., 2006, 10, 162-168. Yamashita, F.; Hashida, M. Drug Metab. Pharmacokin., 2004, 19, 327-338. Lüpfort, C.; Reichel, A. Chem. Biodivers., 2005, 2, 1462-1486. Lombardo, F.; Gifford, E.; Shalavea, M.Y. Mini Rev. Med. Chem., 2003, 3, 861-875. van de Waterbeemd, H.; Gifford, E. Nat. Rev. Drug Discov., 2003, 2, 193-204. Desai, P.V.; Coutinho, E.C. Asian Chem. Lett., 2001, 5, 77-86. Duch, W.; Swaminathan, K.; Meller, J. Curr. Pharm. Des., 2007, 13, 1497-1508. Engelbrecht, A.P. Computational intelligence: An introduction, New York: J. Wiley; 2003. Konar, A. Computational intelligence; principles, techniques and applications, Berlin: Springer 2005. Duda R.O.; Hart, P.E.; Stork, D.G. Pattern classification, New York: J. Wiley, 2nd edt 2001.

372 Frontiers in Drug Design & Discovery, 2009, Vol. 4 [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72]

Hecht and Fogel

Webb, A. Statistical pattern recognition, New York: J. Wiley 2002. Hastie, T.; Tibshirani, R.; Friedman, J. The elements of statistical learning, Springer 2001. Kramer, S. D. Pharm. Sci. Technol. Today, 1999, 2, 373-380. Stenberg, P.; Bergstrom, C. A. S.; Luthman, K.; Artursson, P. Clin. Pharmacokinet., 2002, 41, 877. Sanders, N. R.; Habgood, M. D.; Dziegielwska, K. M. Clin. Exp. Pharmacol. Physiol., 1999, 26, 1119. Stenberg, P.; Norinder, U.; Luthman, K.; Artursson, P. J. Med. Chem., 2001, 44, 1927-1937. Yazdanian, M; Glynn, S.L.; Wright, J.L.; Hawi, A. Pharm. Res., 1998, 15, 1490-1494. Artursson, P.; Karlsson, J. Biochem. Biophs. Res. Commun., 1991, 175, 880. Irvine, J.D.; Takahashi, L.; Lockhart, K.; Cheong, J.; Tolan, J.W.; Selick, H.E.; Grove, J.R. J. Pharm. Sci., 1999, 88, 28-33. Basak, S. C.; Gute, B. D.; Drewes, L.R. Pharm. Res., 1996, 13, 775-778. Boobis, A.; Gundert-Remy, U.; Kremers, P.; Macheras, P.; Pelkonen, O. Eur. J. Pharm. Sci., 2002, 17, 183-193. Tetko, I.V.; Tanchuk, V.Y.; Villa, A.E. J. Chem. Inf. Comput. Sci., 2001, 41, 1407-1421. Hansch, C. Acc. Chem. Res., 1969, 2, 232-239. Ghose, A. K.; Vishwanadhan, V. N.; Wendoloshki, J. J. J. Comb. Chem., 1999, 1, 55-68. Muthas, D.; Sabnis, Y.A.; Lundborg, M.; Karlén, A. J. Mol. Graph. Model., 2008, 26, 1237-1251. Oprea, T. I. J. Comput. Aided Mol. Des., 2000, 14, 251. Wenlock, M. C.; Austin, R. P.; Barton, P.; Davis, A. M.; Leeson, P.D. J. Med. Chem., 2003, 46, 1250. Vieth, M.; Siegel, M. G.; Higgs, R. E.; Watson, I. A.; Robertson, D. H.; Savin, K. A.; Durst, G. L.; Hipskind, P. A. J. Med. Chem., 2004, 47, 224. Hou, T.; Wang, J.; Zhang, W.; Wang, W.; Xu, X. Curr. Med. Chem., 2006, 13, 2653-2667. Poulin, P.; Schoenlein, K.; Theil, F.P J. Pharm. Sci., 2001, 90, 436-447. Lombardo, F.; Obach, R.S.; Shalaeva, M.Y.; Gao, F. J. Med. Chem., 2002, 45, 2867-2876. Poulin, P.; Theil, F.P. J. Pharm. Sci., 2000, 89, 16-35. Poulin, P.; Theil, F.P. J. Pharm. Sci., 2002, 91, 129-156. Poulin, P.; Theil, F.P. J. Pharm. Sci., 2002, 91, 1358-1370. Smith, D. A.; Van de Waterbeemd, H.; Walker, D. K. Pharmacokinetics and Metabolism in Drug Design, Wiley–VCH, Weinheim, Germany, 2001. Lutsevich, A.N. Pharm. Chem. J., 1990, 24, 593-599. Ekins, S.; Waler, C.L.; Swaan, P.W.; Cruciani, G.; Wrighton, S.A.; Wikel, J.H. J. Pharmacol. Toxicol. Methods, 2000, 44, 251-272. Ekins, S.; Wrighton, S.A. J. Pharmacol. Toxicol. Methods, 2001, 45, 65-69. Baranczewski, P.; Staczak, A.; Sundberg , K.; Svensson, R.; Wallin, A; Jansson, J.; Garberg, P.; Postlind , H. Pharmacol. Rep., 2006, 58, 453-72 Wishart, D.S. Drugs R D , 2007, 8, 349-362. Ekins, S.; Bravi, G.; Blinkley, S.; Gillespie, J. S.; Ring, B.J.; Wikel, J. H.; Wrighton, S. A. J. Pharm. Exp. Ther., 1999, 290, 429-438. Ekins, S.; Bravi, G.; Blinkley, S.; Gillespie, J. S.; Ring, B.J.; Wikel, J. H.; Wrighton, S. A. Pharmacogenetics, 1999, 9, 477-489. Yap C.W.; Chen, Y.Z. J. Pharm. Sci., 2005, 94, 153-168. Li, H.; Yap, C.W.; Ung, C.Y.; Xue, Y.; Li, Z.R.; Han, L.Y.; Lin, H.H.; Chen, Y.Z. J. Pharm. Sci., 2007, 96, 2838-2860. Richard, A. M.; Benigni R. SAR QSAR Environ. Res., 2002, 13, 1-19. Greene, N. Adv. Drug Deliv. Rev., 2002, 54, 417-431. Durham, S. K.; Pearl, G. M. Drug Discov., 2001, 4, 110-115. Mohan, C.G.; Gandhi, T.; Garg, D.; Shinde, R. Mini Rev. Med. Chem., 2007, 7, 499-507. Gomase, V.S.; Tagore, S. Curr. Drug Metab., 2008, 9, 250-254. Gomase, V.S.; Tagore, S.; Kale, K.V. Curr. Drug Metab., 2008, 9, 221-31. Collings, F.B.; Vaidya, V.S. Toxicology, 2008, 245, 167-74. Mendrick, D.L. Toxicology, 2008, 245, 175-81. Ganter, B.; Zidek, N.; Hewitt, P.R.; Müller, D.; Vladimirova, A. Pharmacogenomics, 2008, 9, 35-54. Ekins, S. J. Pharmacol. Toxicol. Methods, 2006, 53, 38 - 66. Maggioli, J.; Hoover, A.; Weng, L. J. Pharmacol. Toxicol. Methods, 2006, 53, 31 -37. Wishart, D.S. Drug Metab. Rev., 2005, 37, 279-310. Liu, S.S.; Liu, H.L.; Yin, C.S.; Wang, L.S. J. Chem. Inf. Comput. Sci., 2003, 43, 964-969.

Computational Intelligence Methods [73]

[74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] [86] [87] [88] [89] [90] [91] [92] [93] [94] [95] [96] [97] [98] [99] [100] [101] [102] [103] [104] [105] [106] [107] [108] [109] [110] [111] [112] [113] [114] [115]


Embrechts, M.J.; Ozdemir, M.; Lockwood, L.; Breneman, C.; Bennett, K.; Devogelaere, D.; Rijckaert, M. In Evolutionary Computation in Bioinformatics, Fogel, G. and Corne, D. Eds., Morgan Kauffman, San Francisco (2002), 317-339. Nicolotti, O.; Gillet, V.J.; Fleming, P.J.; Green, D.V.S. J. Med. Chem., 2003, 45, 5069-5080. Fogel, G.B. Brief. Bioinformatics, 2008, 9, 307-316. Devillers, J. Eds. Neural Networks in QSAR and Drug Design, an essential reference source for those on the frontiers of this field. Academic Press 1996. Chalk, A.J.; Beck, B.; Clark, T. J. Chem. Inf. Comput. Sci., 2001, 41, 457-462. Lui, B.; Nadramija, D.; Baic, I.; Tranajsti, N. J. Chem. Inf. Comput. Sci., 2003, 43, 1094-1102. Mattioni, B.E.; Jurs, P.C. J. Mol. Graph. Model., 2003, 21, 391-419. Weekes, D.; Fogel, G.B. Biosystems, 2003, 72, 149-158. Winkler, D.A.; Burden, F.R. J. Mol. Graph. Model., 2004, 22, 499-505. Lewis, R.A. J. Med. Chem., 2005, 48, 1638-1648. Karakoc, E.; Sahinalp, S.C.; Cherkasov, A. J. Chem. Inf. Model., 2006, 46, 2167-2182. Yap, C.W.; Li, H.; Ji, Z.L.; Chen, Y.Z. Mini Rev. Med. Chem., 2007, 7, 1097-107. Kellogg, G.E.; Semus, S.F. 3D QSAR in modern drug design, In Hillisch, A and Hilgenfeld R. (Eds.): Modern Methods of Drug Discovery. Birkhauser Verlag, Switzerland, (2003) 223-241. Selassie, C.D. History of quantitative structure-activity relationships, Burger’s medicinal chemistry and drug discovery, 6th Edt 2003, 1, 1-48. Akamatsu , M. Curr. Top. Med. Chem., 2002, 2, 1381-94. Cramer III, R. D.; Patterson, D. E.; Bunce, J. D. J. Am. Chem. Soc., 1988, 110, 5959-5967. Klebe, G.; Abraham, U.; Mietzner, T. J. Med. Chem., 1994, 37, 4130-4146. Mjolsness, E.; DeCoste, D. Science, 2001, 293, 2051-5. Fogel, G.B.; Corne, D.W.; Pan, Y. (eds). Computational Intelligence in Bioinformatics, Hoboken, N.J. Wiley, 2008. Kitchen, D.B.; Stahura, F.L.; Bajorath, J. Mini Rev. Med. Chem., 2004, 4, 1029-39. McCulloch, W.S.; Pitts W. Bull. Math. Biophys., 1943, 5, 115-33. Rosenblatt, F. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Washington, DC: Spartan Books, 1962. Haykin, S. Neural Networks: A Comprehensive Foundation, Upper Saddle River, NJ: Prentice Hall, 1998. Zadeh, L.A. Information Control, 1965, 8, 338-53. Zadeh, L.A. Information Control, 1968, 12, 94-102. Torres, A.; Nieto, J.J. J. Biomed. Biotechnol., 2006, 2, 91908. Mordeson, J.N.; Malik, D.S.; Cheng, S.-C. Physica, 2000. Szczepaniak, P.S.; Lisoba, P.J.G.; Kacprzyk J. Physica, 2000. Dong , X.; Bondugula, R.; Popescu, M. 2006 IEEE Int. Conf. Fuzzy Syst., 2006, 817-24. Ruspini, E.H.; Bonissone, P.P.; Pedrycz, W. (eds). Handbook of Fuzzy Computation, Bristol, UK: Oxford University Press, 1998. Fogel, L.J.; Owens, A.; Walsh , M.J. Artificial Intelligence Through Simulated Evolution, New York, NY: Wiley, 1966. Rechenberg , I. Evolutionsstrategie: Optimerung technischer Systeme nach Prinzipien der biologischen Evolution, Stuttgart, Germany: Fromman-Holzboog, 1973. Bremmerman H.J. Optimization through evolution and recombination, In: Yovits M.C.; Jacobi G.T.; Goldstein G.D. (eds). Self-Organizing Systems. Washington DC: Spartan Press, 1962. Holland, J.H. Adaptation in Natural and Artificial Systems, Ann Arbor, MI: University of Michigan Press, 1975. Michalewicz, Z. Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Berlin, Germany: Springer, 1996. Koza, J. Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: MIT Press, 1992. Eberhart, R.C.; Shi , Y.; Kennedy, J. Swarm Intelligence, San Francisco, CA: Morgan Kaufmann, 2001. Dorigo, M; Gambardella, L.M. IEEE Trans. Evol. Comput., 1997, 1, 53-66. Storn, R.; Price, K. Technical Report TR-95-012, ICSI, March 1995. Storn , R. IEEE Trans. Evol. Comput., 1999, 3, 22-34. Wolpert, D.H. and Macready W.G. IEEE Trans. Evol. Comp., 1997, 1, 67-82. Fogel D.B.; Fogel L.J.; Porto V.W. Biol. Cybern., 1990, 63, 487-93. Yao X. Proc. IEEE, 1999, 87, 1423-47.

374 Frontiers in Drug Design & Discovery, 2009, Vol. 4 [116] [117] [118] [119] [120] [121] [122] [123] [124] [125] [126] [127] [128] [129] [130] [131] [132] [133] [134] [135] [136] [137] [138] [139] [140] [141] [142] [143] [144] [145] [146] [147] [148] [149] [150] [151] [152] [153] [154] [155] [156] [157] [158] [159] [160] [161] [162] [163] [164] [165]

Hecht and Fogel

Fogel, G.B.; Cheung, M. IEEE Congress on Evolutionary Computation, Edinburgh, UK, 2005, 274281. MA, C.Y.C.; Wong, S.W.M.; Hecht, D.; Fogel, G.B. IEEE Congress on Evolutionary Computation, Vancouver, Canada, 2006, 9284. Hecht, D.; Fogel, G.B. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2007, 4, 476. Hecht, D.; Cheung, M.; Fogel, G.B. Biosystems, 2008, 92, 10-15. Cheung, M.; Johnson, S.; Hecht, D.; Fogel, G.B. IEEE Congress on Evolutionary Computation, Hong Kong, 2008. Han, L.Y. ; Ma, X.H. ; Lin, H.H.; Jia, J.; Zhu, F.; Xuec, Y.; Li, Z.R.; Cao, Z.W. ; Ji, Z.L.; Chen, Y.Z. J. Mol. Graph. Model., 2008, 26, 1276-1286. Martin, O. and Schomburg, D. Proteins, 2008, 70, 1367-1378. Vapnik, V. Statistical Learning Theory, Wiley: New York, 1998. Plewczynski, D.; Tkacz, A.;Godzik, A.;Rychlewski, L. Cell. Mol. Biol. Lett., 2005, 10, 73-89. Chang, C. C.; Lin, C. J. Neural Comput., 2001, 13, 2119-2147. Tan, P.-N.; Steinbach, M.; Kumar, V. Introduction to Data Mining, Addison Wesley, Pearson International Edition, 2006. Gao, H.; Shanmugasundaram, V.; Lee, P. Pharm. Res., 2002, 19, 497-503. Klamt, A.; Eckert, F.; Hornig, M.; Meck, M.E.; Burger T. J. Comput. Chem., 2002, 23, 275-281. Jorgensen, W.L.; Duffy, E.M. Bioorg. Med. Chem. Lett., 2000, 10, 1155-1158. Medir, M.; Giralt, F. AIChE J., 1982, 28, 341. Nirmalakhandan, N. N.; Speece, R. E. Environ. Sci. Technol., 1988, 22, 328. Nirmalakhandan, N. N.; Speece, R. E. Environ. Sci. Technol., 1989, 23, 708. Patil, G. S. Chemosphere, 1991, 22, 723. Makino, M. Environ. Int., 1998, 24, 653. Katritzky, A. R. J. Chem. Inf. Comput. Sci., 1998, 38, 720. Yin, C.; Liu, X.; Guo, W.; Lin, T.; Wang, X.; Wang L. Water Res., 2002, 36, 2975-2982. Delgado, E. J. Fluid Phase Equilib., 2002, 199, 101. Jorgensen, W. L.; Duffy, E. M. Adv. Drug Deliv. Rev., 2002, 54, 355. Abraham, M. H.; Joelle, J. J. Pharm. Sci., 1999, 88, 868. Chen, X. Q.; Cho, S. J.; Li, Y.; Venkatesh, S. J. Pharm. Sci., 2002, 91, 1838-1852. Huibers, P. D. T.; Katritzky, A. R. J. Chem. Inf. Comput. Sci., 1998, 38, 283. Bergstrom, C. A. S.; Norinder, U.; Luthman, K.; Artursson, P. Pharm. Res., 2002, 19, 182. Collette, T. W. Vib. Spectrosc., 1997, 15, 113. Manallack, D.T; Tehan, B.G.; Gancia, E.; Hudson, B.D.; Ford, M.G.; Livingstone, D.J.; Whitley, D.C.; Pitt, W.R. J. Chem. Inf. Comput. Sci., 2003, 43, 674-679. Yan, A.; Gasteiger, J. J. Chem. Inf. Comput. Sci., 2003, 43, 429-434. Tetko, I.V.; Tanchuk, V.Y.; Kasheva, T.N.; Villa, A.E.P. J. Chem. Inf. Comput. Sci., 2001, 41, 14881493. Liu, R.; So, S.-S. J. Chem. Inf. Comput. Sci., 2001, 41, 1633-1639. Engkvist, O.; Wrede, P. J. Chem. Inf. Comput. Sci., 2002, 42, 1247-1249. Huuskonen, J. J. Chem. Inf. Comput. Sci., 2000, 40, 773-777. Bruneau, P. J. Chem. Inf. Comput. Sci., 2001, 41, 1605-1616. Huuskonen, J.; Salo, M.; Taskinen, J. J. Chem. Inf. Comput. Sci., 1998, 38, 450-456. Votano, J.R.; Parham, M.; Hall, L.H.; Kier, L.B. Mol. Divers., 2004, 8, 379-391. Sutter, J. M.; Jurs, P. C. J. Chem. Inf. Comput. Sci., 1996, 36, 100. Mitchell, B. E.; Jurs, P. C. J. Chem. Inf. Comput. Sci., 1998, 38, 489. Huuskonen, J.; Rantanen, J.; Livingstone, D. Eur. J. Med. Chem., 2000, 35, 1081. McElroy, N. R.; Jurs, P. C. J. Chem. Inf. Comput. Sci., 2001, 41, 1237. Nicolotti, O.; Carotti, A. J. Chem. Inf. Model., 2006, 46, 264-276. Yamashita, F.; Fujiwara, S.-I.; Wanchana, S.; Hashida, M. J. Drug Target., 2006, 14, 496-504. Cheng, A.; Merz, K.M. Jr. J. Med. Chem., 2003, 46, 3572-358. Wegner, J.K.; Zell, A. J. Chem. Inf. Comput. Sci., 2003, 43, 1077-1084. Wanchana, S.; Yamashita, F.; Hashida, M. Pharmazie, 2002, 57, 127-129. Votano, J.R.; Parham, M.; Hall, L.H.; Kier, L.B.; Hall, L.M. Chem. Biodivers., 2004, 1, 1829-1841. Yaffe, D.; Cohen, Y.; Espinosa, G.; Arenas, A.; Giralt, F. J. Chem. Inf. Comput. Sci., 2001, 41, 1177. Balakin, K.V.; Ivanenkov, Y.A.; Savchuk, N.P.; Ivashchenko, A.A; Ekins, S. Curr. Drug Discov. Technol., 2005, 2, 99-113. Palmer, D.S.; O'Boyle, N.M.; Glen, R.C.; Mitchell, J.B. J. Chem. Inf. Model., 2007, 47, 150-158.

Computational Intelligence Methods [166] [167] [168] [169] [170] [171] [172] [173] [174] [175] [176] [177] [178] [179] [180] [181] [182] [183] [184] [185] [186] [187] [188] [189] [190] [191] [192] [194] [195] [196] [197] [198] [199] [200] [201] [202] [203] [204] [205] [206] [207] [208] [209] [210]


Harrison, A.P.; Erlwanger, K.H.; Elbrønd, V.S.; Anderson, N.K.; Unmack, N.K. J. Pharmacol. Toxicol. Methods, 2004, 49, 187-199. Zhao, Y. H.; Le, J.; Abraham, M. H.; Hersey, A.; Eddershaw, P. J.; Luscombe, C. N.; Boutina, D.; Beck, G.; Sherborne, B.; Cooper, I.; Platts, J. A. J. Pharm. Sci., 2001, 90, 749. Abraham, M. H.; Zhao, Y. H.; Le, J.; Hersey, A.; Luscombe, C. N.; Reynolds, D. P.; Beck, G.; Sherborne, B.; Cooper, I. Eur. J. Med. Chem., 2002, 37, 595. Jones, R.; Connolly, P. C.; Klamt, A.; Diedenhofen, M. J. Chem. Inf. Model., 2005, 45, 1337. Raevsky, O. A.; Fetisov, V. I.; Trepalina, E. P.; McFarland, J. W.; Schaper, K. J. Quant. Struct. Act. Rel., 2000, 19, 366. Klopman, G.; Stefan, L. R.; Saiakhov, R. D. Eur. J. Pharm. Sci., 2002, 17, 253. Clark, D. E. J. Pharm. Sci., 1999, 88, 807. Norinder, U.; Osterberg, T.; Artursson, P. Eur. J. Pharm. Sci., 1999, 8, 49. Osterberg, T.; Norinder, U. J. Chem. Inf. Comp. Sci., 2000, 40, 1408. Norinder, U.; Osterberg, T. J. Pharm. Sci., 2001, 90, 1076-1085. Sun, H. M. J. Chem. Inf. Comp. Sci., 2004, 44, 748. Ghuloum, A. M.; Sage, C. R.; Jain, A. N. J. Med. Chem., 1999, 42, 1739. Niwa, T. J. Chem. Inf. Comp. Sci., 2003, 43, 113-119. Wegner, J. K.; Frohlich, H.; Zell, A. J. Chem. Inf. Comp. Sci., 2004, 44, 931. Wang, J.; Krudy, G.; Xie, X.-Q.; Wu, C.; Holland, G. J. Chem. Inf. Model., 2006, 46, 2674-2683. Wessel, M. D.; Jurs, P. C.; Tolan, J. W.; Muskal, S. M. J. Chem. Inf. Comp. Sci., 1998, 38, 726-735. Agatonovic-Kustrin, S.; Beresford, R.; Yusof, A. P. M. J. Pharmaceut. Biomed. Anal., 2001, 25, 227. Zmuidinavicius, D.; Didziapetris, R.; Japertas, P.; Avdeef, A.; Petrauskas, A. J. Pharm. Sci., 2003, 92, 621. Deconinck, E.; Hancock, T.; Coomans, D.; Massart, D. L.; Vander Heyden, Y. J. Pharmaceut. Biomed. Anal., 2005, 39, 91. Xue, Y.; Li, Z. R.; Yap, C. W.; Sun, L. Z.; Chen, X.; Chen, Y. Z. J. Chem. Inf. Comput. Sci., 2004, 44, 1630-1638. Liu, H. X.; Hu, R. J.; Zhang, R. S.; Yao, X. J.; Liu, M. C.; Hu, Z. D.; Fan, B. T. J. Comput. Aided Mol. Des., 2005, 19, 33. Hou, T.; Wang, J.; Li, Y. J. Chem. Inf. Model., 2007, 47, 2408-2415. Palm, K.; Luthman, K.; Ungell, A. L.; Strandlund, G.; Artursson, P. J. Pharm. Sci., 1996, 85, 32. van de Waterbeemd, H.; Camenisch, G.; Folkers, G.; Raevsky, O. A. Quant. Struct. Act. Rel., 1996, 15, 480-490. Krarup, L. H.; Christensen, I. T.; Hovgaard, L.; Frokjaer, S. Pharmaceut. Res., 1998, 15, 972. Ponce, Y. M.; Perez, M. A. C.; Zaldivar, V. R.; Ofori, E.; Montero, L. A. Int. J. Mol. Sci., 2003, 4, 512. Kulkarni, A.; Han, Y.; Hopfinger, A. J. J. Chem. Inf. Comp. Sci., 2002, 42, 331. [193] Hou, T. J.; Zhang, W.; Xia, K.; Qiao, X. B.; Xu, X. J. J. Chem. Inf. Comp. Sci., 2004, 44, 1585. Ren, S.; Lien, E.J. Prog. Drug Res., 2000, 54, 1-23. Nordqvist, A.; Nilsson, J.; Lindmark, T.; Eriksson, A.; Garberg, P.; Kihlen, M. Qsar Comb. Sci., 2004, 23, 303 Segarra, V.; Lopez, M.; Ryder, H.; Palacios, J. M. Quant. Struct. Act. Rel., 1999, 18, 474. Norinder, U.; Osterberg, T.; Artursson, P. Pharmaceut. Res., 1997, 14, 1786. Cruciani, C.; Crivori, P.; Carrupt, P. A.; Testa, B. J. Mol. Struct., 2000, 503, 17. Fujiwara, S.; Yamashita, F.; Hashida, M. Int. J. Pharm., 2002, 237, 95-105. Di Fenza, A.; Alagona, G.; Ghio, C.; Leonardi, R.; Giolitti, A.; Madami, A. J. Comput. Aided Mol. Des., 2007, 21, 207-221. Deim, Z. Drug Dev. Ind. Pharm., 2005, 31, 935-942. Yamashita, F.; Wanchana, S.; Hashida, M. J. Pharm. Sci., 2002, 91, 2230-2239. Guangli, M.; Yiyu, C. J. Pharm. Pharm. Sci ., 2006, 9, 210-21. Young, R. C.; Mitchell, R. C.; Brown, T. H.; Ganellin, C. R.; Griffiths, R.; Jones, M.; Rana, K. K.; Saunders, D.; Smith, I. R.; Sore, N. E.; Wilks, T. J. J. Med. Chem., 1988, 31, 656. Van de Waterbeemd, H.; Kansy, M. Chimia, 1992, 46, 299. Clark, D. E. J. Pharm. Sci., 1999, 88, 815. Iyer, M.; Mishra, R.; Han, Y.; Hopfinger, A.J. Pharm. Res., 2002, 19, 1611-1621. Abraham, M. H.; Chadha, H. S.; Mitchell, R. C. J. Pharm. Sci., 1994, 83, 1257. Platts, J. A.; Abraham, M. H.; Zhao, Y. H.; Hersey, A.; Ijaz, L.; Butina, D. Eur. J. Med. Chem., 2001, 36, 719. Kaznessis, Y. N.; Snow, M. E.; Blankley, C. J. J. Comput. Aided Mol. Des., 2001, 15, 697.

376 Frontiers in Drug Design & Discovery, 2009, Vol. 4 [211] [212] [213] [214] [215] [216] [217] [218] [219] [220] [221] [222] [223] [224] [225] [226] [227] [228] [229] [230] [231] [232] [233] [234] [235] [236] [237] [238] [239] [240] [241] [242] [243] [244] [245] [246] [247] [248] [249] [250] [251] [252] [253] [254] [255] [256]

Hecht and Fogel

Rose, K.; Hall, L. H.; Kier, L. B. J. Chem. Inf. Comp. Sci., 2002, 42, 651. Hutter, M. C. J. Comput. Aided Mol. Des., 2003, 17, 415. Hou, T. J.; Xu, X. J. J. Chem. Inf. Comp. Sci., 2003, 43, 2137. Narayanan, R.; Gunturi, S. B. Bioorg. Med. Chem., 2005, 13, 3017. Cabrera, M. A.; Bermejo, M.; Perez, M.; Ramos, R. J. Pharm. Sci., 2004, 93, 1701. Lombardo, F.; Blake, J. F.; Curatolo, W. J. J. Med. Chem., 1996, 39, 4750. Feher, M.; Sourial, E.; Schmidt, J.M. Int. J. Pharmaceutics., 2000, 201, 239-247. Norinder, U.; Sjoberg, P.; Osterberg, T. J. Pharm. Sci., 1998, 87, 952. Stanton, D. T.; Mattioni, B. E.; Knittel, J. J.; Jurs, P. C. J. Chem. Inf. Comp. Sci., 2004, 44, 1010. Luco, J. M. J. Chem. Inf. Comp. Sci., 1999, 39, 396. Subramanian, G.; Kitchen, D. B. J. Comput. Aided Mol. Des., 2003, 17, 643. Crivori, P.; Cruciani, G.; Carrupt, P. A.; Testa, B. J. Med. Chem., 2000, 43, 2204-2216. Dorronsoro, I.; Chana, A.; Abasolo, I.; Castro, A.; Gil, C.; Stud, M.; Martinez, A. Qsar Comb. Sci., 2004, 23, 89. Ajay; Bemis, G. W.; Murcko, M. A. J. Med. Chem., 1999, 42, 4942-4951. Liu, R. F.; Sun, H. M.; So, S. S. J. Chem. Inf. Comput. Sci., 2001, 41, 1623. Hou, T. J.; Xu, X. J. J. Mol. Model., 2002, 8, 337. Lessigiarska, I.; Pajeva , I.; Cronin, M.T.; Worth, A.P. SAR QSAR Environ. Res., 2005, 16, 79-91. Kortagere, S.; Chekmarev, D.; Welsh, W.J.; Ekins S. Pharm. Res., 2008, 25, 1836-1845. Doniger, S.; Hofmann, T.; Yeh, J. J. Comput. Biol., 2002, 9, 849-864. Zhao, Y.H.; Abraham, M.H.; Ibrahim, A;, Fish, P.V.; Cole, S.; Lewis, M.L.; de Groot, M.J.; Reynolds, D.P. J. Chem. Inf. Model., 2007, 47, 170-175. Li, H.; Yap, C. W.; Ung, C. Y.; Xue, Y.; Cao, Z. W.; Chen, Y. Z. J. Chem. Inf. Model., 2005, 45, 1376. Lewis, D.F.V.; Dickins, M. Toxicology, 2002, 170, 45-53. Lewis, D.F.V.; Modi, S.; Dickins, M. Drug Metab. Rev., 2002, 34, 69-82. Caldwell, J.; Gardner, I.; Swales, N. Toxicol. Pathol., 1995, 23, 102-114. Moon, T.; Chi, M.H.; Kim, D.H.; Yoon, C.N.; Choi, Y.S. QSAR, 2000, 19, 257-263. Bazeley, P.S.; Prithivi, S.; Struble, C.A.; Povinelli, R.J.; Sem, D.S. J. Chem. Inf. Model., 2006, 46, 2698-2708. Ekins, S.; Ring, B.J.; Grace, J.; McRobie-Belle, D.J.; Wrighton, S.A. J. Pharmacol. Toxicol. Methods, 2000, 44, 313-324. Zheng, C.J.; Han, L.Y.; Yap, C.W.; Ji, Z.L.; Cao, Z.W.; Chen, Y.Z. Pharmacol. Rev., 2006, 58, 259279. Kriegl, J. M.; Arnhold, T.; Beck, B.; Fox, T. QSAR Comb. Sci., 2005, 24, 491-502. Terfloth, L.; Bienfait, B.; Gasteiger, J. J. Chem. Inf. Model., 2007, 47, 1688-1701. Kriegl, J.M.; Arnhold, T.; Beck, B.; Fox, T. J. Comput. Aided Mol. Des., 2005, 19, 189-201. Arimoto, R.; Prasad, M.A.; Gifford, E.M. J. Biomol. Screen., 2005, 10, 197-205. White, R.E. Annu. Rev. Pharmacol. Toxicol., 2000, 40, 133-157. Boyer, S.; Zamora, I. J. Comp. Aided Mol. Des., 2002, 16, 403-413. de Groot, M.J.; Alex, A.A.; Jones, B.C. J. Med. Chem., 2002, 45, 1983-1993. de Groot, M.J.; Ackland; Horne, V.A.; Alex, A.A.; Jones, B.C. J. Med. Chem., 1999, 42, 1515-1524. Wang, Q.; Halpert, J.R. Drug Metab. Disp., 2002, 30, 86-95. Ekins, S.; Bravi, G.; Ring, B.J.; Gillespie, T.A.; Gillespie, J.S.; Vandenbranden, M.; Wrighton, S.A.; Wikel, J.H. J. Pharm. Exp. Ther., 1999, 288, 21-29. Ekins, S.; Bravi, G.; Wikel, J.H.; Wrighton, S.A. J. Pharm. Exp. Ther., 1999, 291, 424-433. Afzelius, L.; Masimirembwa, C.M.; Karlen, A.; Andersson, T.B.; Zamora, I. J. Comput. Aided Mol. Des., 2002, 16, 443-458. Ekins, S.; Bravi, G.; Binkley, S.; Gillespie, S.; Ring, B.J.; Wikel, J.H.; Wrighton, S.A. Drug Metab. Disp., 2000, 28, 994-1002. Poso, A.; Gynther, J.; Juvonen, R. J. Comput. Aided Mol. Des., 2001, 15, 195-202. Rao, S.; Aoyama, R.; Schrag, M.; Trager, W.F.; Rettie, A.; Jones, J.P. J. Med. Chem., 2000, 43, 27892796. Jensen, B.F.; Vind, C.; Padkjær, S.B.; Brockhoff, P.B.; Refsgaard, H.H.F. J. Med. Chem., 2007, 50, 501-511. Coe, K.J.; Nelson, S.D.; Ulrich, R.G.; He, Y.; Dai, X.; Cheng, O.; Caguyong, M.; Roberts, C.J.; Slatter, J.G. Drug Metab. Dispos., 2006, 34, 1266-1275. Slatter JG, Cheng O, Cornwell PD, de Souza A, Rockett J, Rushmore T, Hartley D, Evers R, He Y, Dai X, Hu R, Caguyong M, Roberts CJ, Castle J, Ulrich RG. Xenobiotica, 2006, 36, 902-937.

Computational Intelligence Methods [257] [258] [259] [260] [261] [262] [263] [264] [265] [266] [267] [268] [269] [270] [271] [272] [273] [274] [275] [276] [277] [278] [279] [280] [281]


Slatter, J.G.; Templeton, I.E.; Castle, J.C.; Kulkarni, A.; Rushmore, T.H.; Richards, K.; He, Y.; Dai, X.; Cheng, O.J.; Caguyong, M.; Ulrich, R.G. Xenobiotica, 2006, 36, 938-962. Katritzky, A.R.; Karelson, M.; Lobanov, V. Pure Appl. Chem., 1997, 69, 245-248. Trotter, M.W.B.; Holden, S.B. QSAR Comb. Sci., 2003, 22, 533-548. Colmenarejo, G.; Alvarez-Pedraglio, A.; Lavandera, J.-L. J. Med. Chem., 2001, 44, 4370-4378. Kratochwil, N.; Huber, W.; Muller, F.; Kansy, M.; Gerber, P.R. Biochem. Pharm., 2002, 64, 13551374. Manallack, D.T.; Livingstone, D.J. Eur. J. Med. Chem., 1999, 34, 195-208. Yao, X.; Liu, H.; Zhang, R.; Liu, M.; Hu, Z.; Panaye, A.; Doucet, J.P.; Fan, B. Mol. Pharm., 2004, 2, 348-356. Gunturi, S.B.; Narayanan, R.; Khandelwal, A. Bioorg. Med. Chem., 2006, 14, 4118-4129. Saiakhov, R.D.; Stefan, L.R.; Klopman, G. Persp. Drug Disc. Des., 2000, 19, 133-135. Nestorov, I.; Gueorguieva, I.; Jones, H.M.; Houston, B.; Rowland, M. Drug Metab. Dispos., 2002, 30, 276-82. Ghafourian, T.; Barzegar-Jalali, M.; Dastmalchi, S.; Khavari-Khorasani, T.; Hakimiha, N.; Nokhodchi, A. Int. J. Pharm., 2006, 319, 82-97. Shen, M.; Xiao, Y.; Golbraikh, A.; Gombar, V.K.; Tropsha, A. J. Med. Chem., 2003, 46, 3013-3020. Sakiyama, Y.; Yuki, H.; Moriya, T.; Hattori, K.; Suzuki, M.; Shimada, K.; Honma, T. J. Mol. Graph. Model., 2008, 26, 907-915. Yoshida, K.; Niwa, T. J. Chem. Inf. Model., 2006, 46, 1371-1378. Klein, C.D.P.; Hopfinger, A.J. Pharm. Res., 1998, 15, 303-311. Jia, L.; Sun, H. Bioorg. Med. Chem., 2008, 16, 6252-6260. Li, Q.; Jørgensen, F.S.; Oprea, T.; Brunak, S.; Taboureau, O. Mol. Pharm., 2008, 5, 117-127. Leong, M.K. Chem. Res. Toxicol., 2007, 20, 217-226. Chekmarev, D.S.; Kholodovych, V.; Balakin, K.V.; Ivanenkov, Y.; Ekins, S.; Welsh, W.J. Chem. Res. Toxicol., 2008, 21, 1304-1314. Agatonovic-Kustrin, S.; Ling, L.H.; Tham, S.Y.; Alany, R.G. J. Pharm. Biomed. Anal., 2002, 29, 103119. Judson, R.; Elloumi, F.; Stzer, R.W.; Zhen, L.; Shah, I. BMC Bioinformatics, 2008, 9, 241-257. Zhao, C.Y.; Zhang, H.X.; Zhang, X.Y.; Liu, M.C.; Hu, Z.D.; Fan, B.T. Toxicology, 2006, 217, 105119. Young, M.B.; DiSilvestro, M.R.; Sendera, T.J.; Freund, J.; Kriete, A.; Magnuson, S.R. Pharmacogenomics J., 2003, 3, 41-52. Mazzatorta , P.; Tran, L.-A.; Schilter, B.; Grigorov, M. J. Chem. Inf. Model., 2007, 47, 34-38. Yuan, H.; Wang, Y.; Cheng, Y. J. Chem. Inf. Model, 2007, 47, 159-169.

Computational Intelligence Methods for ADMET ...

Computational Intelligence Methods for ADMET ...

Suggest Documents

Computational intelligence methods for

Computational Intelligence Methods for Identifying Voltage ... - ASTESJ

Computational intelligence methods for information ... - Fizyka UMK

Computational intelligence methods for rule-based ...

Computational Intelligence Methods for Bioinformatics ...

Applying Computational Intelligence Methods to

Visual data analysis with computational intelligence methods

computational intelligence methods on biomedical signal analysis ...

Evaluation of Computational Intelligence Methods ...

Computational Intelligence Methods for Rule-Based Data Understanding

Computational Intelligence

(Computational) Intelligence

Computational Methods for Understanding

Computational Intelligence Applications for Defense

Computational Architectures for Intelligence and

Computational intelligence approach for NOx

Computational Methods

Computational Methods for Structural RNAs

Computational Methods for Oil Recovery

Statistical and Computational Methods for

Innovative Computational Methods for Structural

Geometric Methods for Computational Electromagnetics

Computational Intelligence

Computational Methods for Geodynamics