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Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals., Objectives: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 ( LD 50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [ LD 50 ( LD 50 ≤ 50 mg / kg )], and nontoxic chemicals ( L D 50 > 2,000 mg / kg )., Methods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches., Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results., Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models., (© The Author(s) 2018. Published by Oxford University Press on behalf of the UK Environmental Mutagen Society.)

Knowing the physicochemical and general biochemical properties of a compound is critical to understanding how it behaves in different biological environments and to anticipating what is likely to happen in situations where that behavior cannot be measured directly. Quantitative structure-property relationship (QSPR) models provide a way to predict those properties even before a compound has been synthesized simply by knowing what its structure would be. This chapter describes a general workflow for compiling the data upon which a useful QSPR model is built, curating it, evaluating that model's performance, and then analyzing the predictive errors with an eye toward identifying systematic errors in the input data. The focus here is on models for the absorption, distribution, metabolism, and excretion (ADME) properties of drugs and toxins, but the considerations explored are general and applicable to any QSPR.

Misuse of prescription opioids, opioid addiction, and overdose underscore the urgent need for developing addiction-free effective medications for treating severe pain. Mu opioid peptide (MOP) receptor agonists provide very effective pain relief. However, severe side effects limit their use in the clinical setting. Agonists of the nociceptin/orphanin FQ peptide (NOP) receptor have been shown to modulate the antinociceptive and reinforcing effects of MOP agonists. We report the discovery and development of a bifunctional NOP/MOP receptor agonist, AT-121, which has partial agonist activity at both NOP and MOP receptors. AT-121 suppressed oxycodone's reinforcing effects and exerted morphine-like analgesic effects in nonhuman primates. AT-121 treatment did not induce side effects commonly associated with opioids, such as respiratory depression, abuse potential, opioid-induced hyperalgesia, and physical dependence. Our results in nonhuman primates suggest that bifunctional NOP/MOP agonists with the appropriate balance of NOP and MOP agonist activity may provide a dual therapeutic action for safe and effective pain relief and treating prescription opioid abuse., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

When medicinal chemists need to improve oral bioavailability (%F) during lead optimization, they systematically modify compound properties mainly based on their own experience and general rules of thumb. However, at least a dozen properties can influence %F, and the difficulty of multiparameter optimization for such complex nonlinear processes grows combinatorially with the number of variables. Furthermore, strategies can be in conflict. For example, adding a polar or charged group will generally increase solubility but decrease permeability. Identifying the 2 or 3 properties that most influence %F for a given compound series would make %F optimization much more efficient. We previously reported an adaptation of physiologically based pharmacokinetic (PBPK) simulations to predict %F for lead series from purely computational inputs within a 2-fold average error. Here, we run thousands of such simulations to generate a comprehensive "bioavailability landscape" for each series. A key innovation was recognition that the large and variable number of p K a 's in drug molecules could be replaced by just the two straddling the isoelectric point. Another was use of the ZINC database to cull out chemically inaccessible regions of property space. A quadratic partial least squares regression (PLS) accurately fits a continuous surface to these thousands of bioavailability predictions. The PLS coefficients indicate the globally sensitive compound properties. The PLS surface also displays the %F landscape in these sensitive properties locally around compounds of particular interest. Finally, being quick to calculate, the PLS equation can be combined with models for activity and other properties for multiobjective lead optimization.

When medicinal chemists need to improve bioavailability (%F) within a chemical series during lead optimization, they synthesize new series members with systematically modified properties mainly by following experience and general rules of thumb. More quantitative models that predict %F of proposed compounds from chemical structure alone have proven elusive. Global empirical %F quantitative structure-property (QSPR) models perform poorly, and projects have too little data to train local %F QSPR models. Mechanistic oral absorption and physiologically based pharmacokinetic (PBPK) models simulate the dissolution, absorption, systemic distribution, and clearance of a drug in preclinical species and humans. Attempts to build global PBPK models based purely on calculated inputs have not achieved the <2-fold average error needed to guide lead optimization. In this work, local GastroPlus PBPK models are instead customized for individual medchem series. The key innovation was building a local QSPR for a numerically fitted effective intrinsic clearance (CL loc ). All inputs are subsequently computed from structure alone, so the models can be applied in advance of synthesis. Training CL loc on the first 15-18 rat %F measurements gave adequate predictions, with clear improvements up to about 30 measurements, and incremental improvements beyond that.

Few opioid ligands binding to the three classic opioid receptor subtypes, mu, kappa and delta, have high affinity at the fourth opioid receptor, the nociceptin/orphanin FQ receptor (NOP). We recently reported the discovery of AT-076 (1), (R)-7-hydroxy-N-((S)-1-(4-(3-hydroxyphenyl)piperidin-1-yl)-3-methylbutan-2-yl)-1,2,3,4-tetrahydroisoquinoline-3-carboxamide, a pan antagonist with nanomolar affinity for all four subtypes. Since AT-076 binds with high affinity at all four subtypes, we conducted a structure-activity relationship (SAR) study to probe ligand recognition features important for pan opioid receptor activity, using chemical modifications of key pharmacophoric groups. SAR analysis of the resulting analogs suggests that for the NOP receptor, the entire AT-076 scaffold is crucial for high binding affinity, but the binding mode is likely different from that of NOP antagonists C-24 and SB-612111 bound in the NOP crystal structure. On the other hand, modifications of the 3-hydroxyphenyl pharmacophore, but not the 7-hydroxy Tic pharmacophore, are better tolerated at kappa and mu receptors and yield very high affinity multifunctional (e.g. 12) or highly selective (e.g. 16) kappa ligands. With the availability of the opioid receptor crystal structures, our SAR analysis of the common chemotype of AT-076 suggests rational approaches to modulate binding selectivity, enabling the design of multifunctional or selective opioid ligands from such scaffolds.

The antagonist-bound crystal structure of the nociceptin receptor (NOP), from the opioid receptor family, was recently reported along with those of the other opioid receptors bound to opioid antagonists. We recently reported the first homology model of the 'active-state' of the NOP receptor, which when docked with 'agonist' ligands showed differences in the TM helices and residues, consistent with GPCR activation after agonist binding. In this study, we explored the use of the active-state NOP homology model for structure-based virtual screening to discover NOP ligands containing new chemical scaffolds. Several NOP agonist and antagonist ligands previously reported are based on a common piperidine scaffold. Given the structure-activity relationships for known NOP ligands, we developed a hybrid method that combines a structure-based and ligand-based approach, utilizing the active-state NOP receptor as well as the pharmacophoric features of known NOP ligands, to identify novel NOP binding scaffolds by virtual screening. Multiple conformations of the NOP active site including the flexible second extracellular loop (EL2) loop were generated by simulated annealing and ranked using enrichment factor (EF) analysis and a ligand-decoy dataset containing known NOP agonist ligands. The enrichment factors were further improved by combining shape-based screening of this ligand-decoy dataset and calculation of consensus scores. This combined structure-based and ligand-based EF analysis yielded higher enrichment factors than the individual methods, suggesting the effectiveness of the hybrid approach. Virtual screening of the CNS Permeable subset of the ZINC database was carried out using the above-mentioned hybrid approach in a tiered fashion utilizing a ligand pharmacophore-based filtering step, followed by structure-based virtual screening using the refined NOP active-state models from the enrichment analysis. Determination of the NOP receptor binding affinity of a selected set of top-scoring hits resulted in identification of several compounds with measurable binding affinity at the NOP receptor, one of which had a new chemotype for NOP receptor binding. The hybrid ligand-based and structure-based methodology demonstrates an effective approach for virtual screening that leverages existing SAR and receptor structure information for identifying novel hits for NOP receptor binding. The refined active-state NOP homology models obtained from the enrichment studies can be further used for structure-based optimization of these new chemotypes to obtain potent and selective NOP receptor ligands for therapeutic development.

There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Combined Rule Extraction and Feature Elimination (CRF), based on 1-norm regularized random forests. CRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied CRF to several drug activity prediction and microarray data sets. CRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.

The opioid receptor-like receptor, also known as the nociceptin receptor (NOP), is a class A G protein-coupled receptor (GPCR) in the opioid receptor family. Although NOP shares a significant homology with the other opioid receptors, it does not bind known opioid ligands and has been shown to have a distinct mechanism of activation compared to the closely related opioid receptors mu, delta, and kappa. Previously reported homology models of the NOP receptor, based on the inactive-state GPCR crystal structures, give limited information on the activation and selectivity features of this fourth member of the opioid receptor family. We report here the first active-state homology model of the NOP receptor based on the opsin GPCR crystal structure. An inactive-state homology model of NOP was also built using a multiple template approach. Molecular dynamics simulation of the active-state NOP model and comparison to the inactive-state model suggest that NOP activation involves movements of transmembrane (TM)3 and TM6 and several activation microswitches, consistent with GPCR activation. Docking of the selective nonpeptidic NOP agonist ligand Ro 64-6198 into the active-state model reveals active-site residues in NOP that play a role in the high selectivity of this ligand for NOP over the other opioid receptors. Docking the shortest active fragment of endogenous agonist nociceptin/orphaninFQ (residues 1-13) shows that the NOP extracellular loop 2 (EL2) loop interacts with the positively charged residues (8-13) of N/OFQ. Both agonists show extensive polar interactions with residues at the extracellular end of the TM domain and EL2 loop, suggesting agonist-induced reorganization of polar networks, during receptor activation., (Copyright © 2012 Wiley Periodicals, Inc.)

Background: In the context of drug discovery and development, much effort has been exerted to determine which conformers of a given molecule are responsible for the observed biological activity. In this work we aimed to predict bioactive conformers using a variant of supervised learning, named multiple-instance learning. A single molecule, treated as a bag of conformers, is biologically active if and only if at least one of its conformers, treated as an instance, is responsible for the observed bioactivity; and a molecule is inactive if none of its conformers is responsible for the observed bioactivity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule., Methods: We encoded the 3-dimensional structures using pharmacophore fingerprints which are binary strings, and accomplished instance-based embedding using calculated dissimilarity distances. Four dissimilarity measures were employed and their performances were compared. 1-norm SVM was used for joint feature selection and classification. The approach was applied to four data sets, and the best proposed model for each data set was determined by using the dissimilarity measure yielding the smallest number of selected features., Results: The predictive abilities of the proposed approach were compared with three classical predictive models without instance-based embedding. The proposed approach produced the best predictive models for one data set and second best predictive models for the rest of the data sets, based on the external validations. To validate the ability of the proposed approach to find bioactive conformers, 12 small molecules with co-crystallized structures were seeded in one data set. 10 out of 12 co-crystallized structures were indeed identified as significant conformers using the proposed approach., Conclusions: The proposed approach was proven not to suffer from overfitting and to be highly competitive with classical predictive models, so it is very powerful for drug activity prediction. The approach was also validated as a useful method for pursuit of bioactive conformers.

Background: It is commonly believed that including domain knowledge in a prediction model is desirable. However, representing and incorporating domain information in the learning process is, in general, a challenging problem. In this research, we consider domain information encoded by discrete or categorical attributes. A discrete or categorical attribute provides a natural partition of the problem domain, and hence divides the original problem into several non-overlapping sub-problems. In this sense, the domain information is useful if the partition simplifies the learning task. The goal of this research is to develop an algorithm to identify discrete or categorical attributes that maximally simplify the learning task., Results: We consider restructuring a supervised learning problem via a partition of the problem space using a discrete or categorical attribute. A naive approach exhaustively searches all the possible restructured problems. It is computationally prohibitive when the number of discrete or categorical attributes is large. We propose a metric to rank attributes according to their potential to reduce the uncertainty of a classification task. It is quantified as a conditional entropy achieved using a set of optimal classifiers, each of which is built for a sub-problem defined by the attribute under consideration. To avoid high computational cost, we approximate the solution by the expected minimum conditional entropy with respect to random projections. This approach is tested on three artificial data sets, three cheminformatics data sets, and two leukemia gene expression data sets. Empirical results demonstrate that our method is capable of selecting a proper discrete or categorical attribute to simplify the problem, i.e., the performance of the classifier built for the restructured problem always beats that of the original problem., Conclusions: The proposed conditional entropy based metric is effective in identifying good partitions of a classification problem, hence enhancing the prediction performance.

A rare Gly210 deletion in protoporphyrinogen oxidase (PPO) was recently discovered in herbicide-resistant Amaranthus tuberculatus. According to the published X-ray structure of Nicotiana tabacum PPO, Gly210 is adjacent to, not in, the PPO active site, so it is a matter of interest to determine why its deletion imparts resistance to herbicides. In our kinetic experiments, this deletion did not affect the affinity of protoporphyrinogen IX nor the FAD content, but decreased the catalytic efficiency of the enzyme. The suboptimal Kcat was compensated by a significant increase in the Kis for inhibitors and a switch in their interactions from competitive to mixed-type inhibition. In our protein modeling studies on herbicide-susceptible PPO and resistant PPO, we show that Gly210 plays a key role in the alphaL helix-capping motif at the C-terminus of the alpha-8 helix which helps to stabilize the helix. In molecular dynamics simulations, the deletion had significant architecture consequences, destabilizing the alpha-8 helix-capping region and unraveling the last turn of the helix, leading to enlargement of the active site cavity by approximately 50%. This seemingly innocuous deletion of Gly210 of the mitochondrial PPO imparts herbicide resistance to this dual-targeted protein without severely affecting its normal physiological function, which may explain why this unusual mutation was the favored evolutionary path for achieving resistance to PPO inhibitors.

Hepatitis B virus (HBV) DNA polymerase (HDP) is a pharmacological target of intense interest. Of the seven agents approved in USA for the treatment of HBV infections, five are HDP inhibitors. However, resistance development against HDP inhibitors, such as lamivudine and adefovir, has severely hurt their efficacy to treat HBV. As a step toward understanding the mechanism of resistance development and for gaining detailed insights about the active site of the enzyme, we have built a homology model of HDP which is an advance over previously reported ones. Validation using various techniques, including PROSTAT, PROCHECK, and Verify-3D profile, proved the model to be stereochemically significant. The stability of the model was studied using a 5 ns molecular dynamics simulation. The model was found to be sufficiently stable after the initial 2.5 ns with overall root mean squared deviation (RMSD) of 4.13 A. The homology model matched the results of experimental mutation studies of HDP reported in the literature, including those of antiviral-resistant mutations. Our model suggests the significant role of conserved residues, such as rtLys32, in binding of the inhibitors, contrary to previous studies. The model provides an explanation for the inactivity of some anti-HIV molecules which are inactive against HDP. Conformational changes which occurred in certain binding pocket amino acids helped to explain the better binding of some of the inhibitors in comparison to the substrates.

Protein modeling has been a very challenging problem in drug discovery and computational biology. The latest advances and progress in computational power have helped to solve this problem to a considerable extent; however, predicting accurate three-dimensional structure of proteins has always been and remains a complicated assignment. Of the two common methods of protein structure prediction, template-based modeling has become more popular than ab initio modeling. In this review, we summarize the developments in methodology and of understanding for comparative protein modeling during the last three years, including for homologue search, fold recognition, secondary structure prediction, model building, loop building, side-chain prediction and model quality assessment.

Latrunculins are unique macrolides containing a thiazolidinone moiety. Latrunculins A, B and T and 16-epi-latrunculin B were isolated from the Red Sea sponge Negombata magnifica. N-Alkylated, O-methylated analogs of latrunculin B were synthesized and biological evaluation was performed for antifungal and antiprotozoal activity. The natural latrunculins showed significant bioactivity, while the semisynthetic analogs did not. Docking studies of these analogs into the X-ray crystal structure of G-actin showed that, in comparison with latrunculins A and B, N-alkylated latrunculins did not dock satisfactorily. This suggests that the analogs do not fit well into the active site of G-actin due to steric clashes and provides an explanation for the absence of bioactivity.

Glycogen synthase kinase-3, a serine/threonine kinase, has been implicated in a wide variety of pathological conditions such as diabetes, Alzheimer's disease, stroke, bipolar disorder, malaria and cancer. Herein we report 3D-QSAR analyses using CoMFA and CoMSIA and molecular docking studies on 3-anilino-4-phenylmaleimides as GSK-3alpha inhibitors, in order to better understand the mechanism of action and structure-activity relationship of these compounds. Comparison of the active site residues of GSK-3alpha and GSK-3beta isoforms shows that all the key amino acids involved in polar interactions with the maleimides for the beta isoform are the same in the alpha isoform, except that Asp133 in the beta isoform is replaced by Glu196 in the alpha isoform. We prepared a homology model for GSK-3alpha, and showed that the change from Asp to Glu should not affect maleimide binding significantly. Docking studies revealed the binding poses of three subclasses of these ligands, namely anilino, N-methylanilino and indoline derivatives, within the active site of the beta isoform, and helped to explain the difference in their inhibitory activity.

A detailed computational study on a series of spiroquinazolinones showing phosphodiesterase 7 (PDE7) inhibitory activity was performed to understand the binding mode and the role of stereoelectronic properties in binding. Our docking studies reproduced the essential hydrogen bonding and hydrophobic interactions for inhibitors of this class of enzymes. The N1 proton of the quinazolinone scaffold was involved in H-bonding to an amide side chain of the conserved glutamine residue in the active site. The central bicyclic ring of the molecules showed hydrophobic and pi-stacking interactions with hydrophobic and aromatic amino acid residues, respectively, present in the PDE7 active site. The docked conformations were optimized with density functional theory (DFT) and DFT electronic properties were calculated. Comparison of molecular electrostatic potential (MEP) plots of inhibitors with the active site of PDE7 suggested that the electronic distribution in the molecules is as important as steric factors for binding of the molecules to the receptor. The hydrogen bonding ability and nucleophilic nature of N1 appeared to be important for governing the interaction with PDE7. For less active inhibitors (pIC(50) < 6.5), the MEP maximum at N1 of the spiroquinazolinone ring was high or low based on the electronic properties of the substituents. All the more active molecules (pIC(50) > 6.5) had MEP highest at N3, not N1. Efficient binding of these inhibitors may need some rearrangement of side chains of active-site residues, especially Asn365. This computational modeling study should aid in design of new molecules in this class with improved PDE7 inhibition., ((c) 2008 Wiley Periodicals, Inc.)

A new latrunculin, oxalatrunculin B (3), was isolated from Red Sea sponge Negombata corticata. Extensive spectroscopic analysis revealed an unprecedented heterocycle in which the rare thiazolidinone ring found in latrunculins was oxidized with three additional oxygens. An actin polymerization inhibition assay agreed with MM-PBSA free energy calculations that 3 binds more weakly than latrunculin B to actin. Significant antifungal and anticancer activity of 3 was found, suggesting an alternate target in addition to actin for latrunculin bioactivity.

Diverse analogs of 1,5-diarylpyrazoles having 3-hydroxymethyl-4-sulfamoyl (SO2NH2)/methyl sulfonyl (SO2Me)-pheny group at N1 were synthesized and evaluated for their in vitro cyclooxygenase (COX-1/COX-2) inhibitory activity. The SAR study mainly involved the variations at positions C-3, C-5 and N1 of the pyrazole ring. Several small hydrophobic groups at/around position-4 of C-5 phenyl, viz. 3,4-dimethylphenyl analog 9, 3-methyl-4-methylsulfanylphenyl analog 14 and 2,3-dihydrobenzo[b]thiophenyl analog 17, exhibited impressive COX-2 inhibitory potency. In general, the sulfonamide analogues with a CHF2 at C-3 were found to be more potent than those having a CF3 group. The three dimensional quantitative structure activity relationship comprising comparative molecular field analysis (3D-QSAR-CoMFA) afforded the models with high predictivity which further validated the acceptance of hydroxymethyl (CH2OH) group in the hydrophilic pocket of the COX-2 enzyme.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were carried out on quinazoline, quinoline, and cyanoquinoline derivatives inhibiting c-Src kinase. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were developed. The conventional r2 values for CoMFA and CoMSIA are 0.93 and 0.89, respectively. In addition, a homology model of c-Src kinase with the activation loop resembling the active conformation was constructed using the crystal structure of the kinase domain of Lck. The ATP binding pocket of the active form of c-Src is similar to that of the c-Abl kinase in which the activation loop resembles that of an active form. One of the potent c-Src and c-Abl dual kinase inhibitors (77 or SKI-606) was docked inside the active sites of both c-Src and c-Abl. The orientation and hydrogen bonding interactions of 77 are similar in both kinases. The results of 3D-QSAR analyses and structure based studies will be useful for the design of novel c-Src and c-Abl dual kinase inhibitors.