Comparative Molecular Similarity Indices Analysis Models Research Articles

Studies on [5,6]-Fused Bicyclic Scaffolds Derivatives as Potent Dual B-RafV600E/KDR Inhibitors Using Docking and 3D-QSAR Approaches.

Research and development of multi-target inhibitors has attracted increasing attention as anticancer therapeutics. B-RafV600E synergistically works with vascular endothelial growth factor receptor 2 (KDR) to promote the occurrence and progression of cancers, and the development of dual-target drugs simultaneously against these two kinds of kinase may offer a better treatment advantage. In this paper, docking and three-dimensional quantitative structure activity relationship (3D-QSAR) studies were performed on a series of dual B-Raf/KDR inhibitors with a novel hinge-binding group, [5,6]-fused bicyclic scaffold. Docking studies revealed optimal binding conformations of these compounds interacting with both B-Raf and KDR. Based on these conformations, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were constructed, and the best CoMFA (q2 = 0.542, r2 = 0.989 for B-Raf; q2 = 0.768, r2 = 0.991 for KDR) and CoMSIA models (q2 = 0.519, r2 = 0.992 for B-Raf; q2 = 0.849, r2 = 0.993 for KDR) were generated. Further external validations confirmed their predictability, yielding satisfactory correlation coefficients (r2pred = 0.764 (CoMFA), r2pred = 0.841 (CoMSIA) for B-Raf, r2pred = 0.912 (CoMFA), r2pred = 0.846 (CoMSIA) for KDR, respectively). Through graphical analysis and comparison on docking results and 3D-QSAR contour maps, key amino acids that affect the ligand-receptor interactions were identified and structural features influencing the activities were discussed. New potent derivatives were designed, and subjected to preliminary pharmacological evaluation. The study may offer useful references for the modification and development of novel dual B-Raf/KDR inhibitors.

QSAR analysis of thiolactone derivatives using HQSAR, CoMFA and CoMSIA

The development of resistant malaria and lethality of the disease demands the search for new therapeutic candidates. In this line-up, thiolactone was identified as the potential lead structure and subjected to hologram quantitative structure–activity relationship (HQSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Overall, the QSAR results shows that the LOO cross-validated q2 values of HQSAR, CoMFA and CoMSIA models are 0.791, 0.737 and 0.753, respectively. According to HQSAR, the hydrogen bond donor and acceptor were found to play an important role in governing antimalarial activity of thiolactone derivatives. The fragment contribution map of HQSAR, and contour maps of CoMFA and CoMSIA showed the presence of an electronegative group at the fifth position, and a bulky group at the third and fourth positions of the thiolactone ring, positively contributing to antimalarial activity. Furthermore, molecular docking was performed to analyze the binding mode of newly designed thiolactones with the active site residues of pf KAS I/II. The prediction of newly designed thiolactone molecules based on QSAR and docking score are in good accordance with each other. Therefore the ligand-based QSAR models and target structure-based docking model developed in this study may be successfully utilized for the design of new antimalarial agents.

QSAR and molecular docking studies on oxindole derivatives as VEGFR-2 tyrosine kinase inhibitors

The three-dimensional quantitative structure–activity relationships (3D-QSAR) were established for 30 oxindole derivatives as vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase inhibitors by using comparative molecular field analysis (CoMFA) and comparative similarity indices analysis comparative molecular similarity indices analysis (CoMSIA) techniques. With the CoMFA model, the cross-validated value (q2) was 0.777, the non-cross-validated value (R2) was 0.987, and the external cross-validated value () was 0.72. And with the CoMSIA model, the corresponding q2, R2 and values were 0.710, 0.988 and 0.78, respectively. Docking studies were employed to bind the inhibitors into the active site to determine the probable binding conformation. The binding mode obtained by molecular docking was in good agreement with the 3D-QSAR results. Based on the QSAR models and the docking binding mode, a set of new VEGFR-2 tyrosine kinase inhibitors were designed, which showed excellent predicting inhibiting potencies. The result revealed that both QSAR models have good predictive capability to guide the design and structural modification of homologic compounds. It is also helpful for further research and development of new VEGFR-2 tyrosine kinase inhibitors.

Molecular determinants of thyroid hormone receptor selectivity in a series of phosphonic acid derivatives: 3D-QSAR analysis and molecular docking

A mathematical study was performed on a set of phosphonic acid derivatives that are substrates for thyroid hormone receptor β (TRβ) and thyroid hormone receptor α (TRα), three-dimensional quantitative structure-activity relationship (3D-QSAR) models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were employed to investigate the structural requirements for this series of compounds with improved activity. Some descriptors were also employed to significantly improve the performance of the derived models. The CoMFA model for TRβ exhibited Rcv2 of 0.612, Rpred2 of 0.7218, whereas CoMSIA model showed Rcv2 of 0.621, R2pred of 0.7358; the CoMFA model for TRα displayed Rcv2 of 0.678, Rpred2 of 0.6424, and the CoMSIA model had Rcv2 of 0.671, Rpred2 of 0.6932, which indicate that the constructed models are statistically significant. The derived contour maps further pointed out the regions where interactive fields may influence the activity. In order to validate the QSAR models and explore the origin of the selectivity at the amino acid level, molecular docking was developed, and the results indicate that Arg282, Arg320, Asn331, Gly332, Thr329 and His435 for TRβ, but Ala225, Arg228, Met259, Arg262 and His381 for TRα, respectively are important residues. The information obtained from the QSAR models can be used in the design of more potent TR agonists.

Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations.

Coagulation factor Xa (Factor Xa, FXa) is a particularly promising target for novel anticoagulant therapy. The first oral factor Xa inhibitor has been approved in the EU and Canada in 2008. In this work, 38 [6,6,5] Tricyclic Fused Oxazolidinones were studied using a combination of molecular modeling techniques including three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, molecular dynamics and Topomer CoMFA (comparative molecular field analysis) were used to build 3D-QSAR models. The results show that the best CoMFA model has q(2)=0.511 and r(2)=0.984, the best CoMSIA (comparative molecular similarity indices analysis) model has q(2)=0.700 and r(2)=0.993 and the Topomer CoMFA analysis has q(2)=0.377 and r(2)=0.886. The results indicated the steric, hydrophobic, H-acceptor and electrostatic fields play key roles in models. Molecular docking and molecular dynamics explored the binding relationship of the ligand and the receptor protein.

Insight into the Structural Determinants of Imidazole Scaffold-Based Derivatives as TNF-α Release Inhibitors by in Silico Explorations.

Presently, 151 widely-diverse pyridinylimidazole-based compounds that show inhibitory activities at the TNF-α release were investigated. By using the distance comparison technique (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) methods, the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds were explored. The proposed pharmacophore model, including two hydrophobic sites, two aromatic centers, two H-bond donor atoms, two H-bond acceptor atoms, and two H-bond donor sites characterizes the necessary structural features of TNF-α release inhibitors. Both the resultant CoMFA and CoMSIA models exhibited satisfactory predictability (with Q2 (cross-validated correlation coefficient) = 0.557, R2ncv (non-cross-validated correlation coefficient) = 0.740, R2pre (predicted correlation coefficient) = 0.749 and Q2 = 0.598, R2ncv = 0.767, R2pre = 0.860, respectively). Good consistency was observed between the 3D-QSAR models and the pharmacophore model that the hydrophobic interaction and hydrogen bonds play crucial roles in the mechanism of actions. The corresponding contour maps generated by these models provide more diverse information about the key intermolecular interactions of inhibitors with the surrounding environment. All these models have extended the understanding of imidazole-based compounds in the structure-activity relationship, and are useful for rational design and screening of novel 2-thioimidazole-based TNF-α release inhibitors.

Molecular modeling study on Mer kinase inhibitors using 3D-QSAR and docking approaches

Mer kinase is a novel cancer therapeutic target. Abnormal activation or overexpression of Mer kinase has been implicated in several human cancers. Three-dimensional quantitative structure–activity relationship (3D-QSAR) and molecular docking studies were performed on a series of pyridinepyrimidine derivatives to understand the structural basis for Mer kinase inhibitory activity. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were used to derive 3D-QSAR models. The CoMFA model produced statistically significant results with a cross-validated correlation coefficient (q 2) of 0.634 and a non-cross-validated correlation coefficient (r 2) of 0.968. The reliable CoMSIA model with q 2 of 0.665 and r 2 of 0.991 was obtained from the combination of steric, electrostatic, hydrophobic, hydrogen bond acceptor and hydrogen bond donor fields. The CoMFA and CoMSIA models showed reasonable predictive abilities with predicted correlation coefficient (r pred 2 ) of 0.629 and 0.811, respectively. The models were further evaluated by leave-five-out cross-validation and bootstrapping analyses. Docking studies were carried out to deduce probable binding conformations of this series of inhibitors inside the active site of Mer kinase. Docking studies also contributed to the identification of some crucial binding residues in the active site. The correlation of 3D-QSAR and docking results indicated the robustness of the QSAR models. Overall, a combined application of docking and 3D-QSAR results could provide a better understanding of the interactions between the inhibitors and protein. This study could assist in the design of novel compounds with enhanced Mer kinase inhibitory activity.

Structural insights of PA-824 derivatives: ligand-based 3D-QSAR study and design of novel PA824 derivatives as anti-tubercular agents

With the purpose of designing novel chemical entities with improved inhibitory potencies against drug-resistant Mycobacterium tuberculosis, the 3D- quantitative structure–activity relationship (QSAR) studies were carried out on biphenyl analogs of the tuberculosis (TB) drug, PA-824. Anti-mycobacterial activity (MABA) was considered for the 3D-QSAR studies using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The best CoMFA and CoMSIA models were found statistically significant with cross-validated coefficients (q2) of 0.784 and 0.768, respectively, and conventional coefficients (r2) of 0.823 and 0.981, respectively. The cross-validated and the external validation results revealed that both the CoMFA and CoMSIA models possesses high accommodating capacities and they would be reliable for predicting the pMIC values of new PA-824 derivatives. Based on the models and structural insights, a series of new PA-824 derivatives were designed and the anti-mycobacterial activities of the designed compounds were predicted based on the best 3D-QSAR model. The predicted data results suggest the designed compounds are more potent than existed ones.

QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

Forty-six sodium-dependent glucose cotransporters-2 (SGLT-2) inhibitors with hypoglycemic activity were selected to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set of 39 compounds were used to build up the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 7 compounds was used for the external validation. The CoMFA model predicted a q2 value of 0.792 and an r2 value of 0.985. The best CoMSIA model predicted a q2 value of 0.633 and an r2 value of 0.895 based on a combination of steric, electrostatic, hydrophobic and hydrogen-bond acceptor effects. The predictive correlation coefficients (rpred2) of CoMFA and CoMSIA models were 0.872 and 0.839, respectively. The analysis of the contour maps from each model provided insight into the structural requirements for the development of more active sglt inhibitors, and on the basis of the models 8 new sglt inhibitors were designed and predicted.

Structure based 3D-QSAR studies of Interleukin-2 inhibitors: Comparing the quality and predictivity of 3D-QSAR models obtained from different alignment methods and charge calculations

Interleukin-2 is an essential cytokine in an innate immune response, and is a promising drug target for several immunological disorders. In the present study, structure-based 3D-QSAR modeling was carried out via Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) methods. Six different partial charge calculation methods were used in combination with two different alignment methods to scrutinize their effects on the predictive power of 3D-QSAR models. The best CoMFA and CoMSIA models were obtained with the AM1 charges when used with co-conformer based substructure alignment (CCBSA) method. The obtained models posses excellent correlation coefficient value and also exhibited good predictive power (for CoMFA: q2=0.619; r2=0.890; r2Pred=0.765 and for CoMSIA: q2=0.607; r2=0.884; r2Pred=0.655). The developed models were further validated by using a set of another sixteen compounds as external test set 2 and both models showed strong predictive power with r2Pred=>0.8. The contour maps obtained from these models better interpret the structure activity relationship; hence the developed models would help to design and optimize more potent IL-2 inhibitors. The results might have implications for rational design of specific anti-inflammatory compounds with improved affinity and selectivity.

A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors.

B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q2 = 0.621, r2pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.

3D-QSAR and docking studies on piperidine-substituted diarylpyrimidine analogues as HIV-1 reverse transcriptase inhibitors

In this paper, 65 small molecules of HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) were aligned by two different alignment methods (substructure-based and docking-based alignment) for the construction of three-dimensional quantitative structure–activity relationship models. And the molecular descriptors were calculated by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Statistical parameters derived from the models using the above two different alignment methods showed that the docking-based CoMFA and CoMSIA models were better than the substructure-based models with both higher internal consistencies (q 2 of 0.727 and 0.698, respectively) and external predictive powers ( $$r_{\text{pred}}^{2}$$ of 0.906 and 0.940, respectively). According to the generated contour maps, several key structural features required for increasing the biological activity of compounds were achieved. Additionally, a docking study was also performed to explore the binding mechanisms of this series of compounds, and the obtained binding interactions are in agreement with the results of the contour maps. These structural-based and ligand-based computational studies can offer useful references for the further rational design of HIV-1 NNRTIs.

Molecular docking and QSAR analyses for understanding the antimalarial activity of some 7-substituted-4-aminoquinoline derivatives

The quinoline moiety is one of the widely studied scaffolds for generating derivatives with various pharmacophoric groups due to its potential antimalarial activities. In the present study, a series of 7-substituted-4-aminoquinoline derivatives were selected to understand their antimalarial properties computationally by molecular modeling techniques including 2D QSAR, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and molecular docking. The 2D-QSAR model built with four descriptors selected by genetic algorithm technique and CoMFA model showed satisfactory statistical results (Q2=0.540, R2ncv=0.881, F value=157.09). A reliable CoMSIA model out of the fourteen different combinations has a Q2 value of 0.638. The molecular docking studies of the compounds for 1CET as the protein target revealed that ten compounds showed maximum interactions with the binding site of the protein. The present study highlights the unique binding signatures of the ligands within the active site groove of the target and it explains the subtle differences in their EC50 values and their mechanism of inhibition.

Pharmacophore based 3DQSAR of phenothiazines as specific human butyrylcholinesterase inhibitors for treatment of Alzheimer's disease.

Quantitative three dimensional structure activity relationship (3D-QSAR) studies were performed on phenothiazine derivatives as Butyrylcholinesterase (BuChE) inhibitors. Pharmacophore Alignment and Scoring Engine (PHASE) was used to develop predictive Common Pharmacophore Hypotheses (CPHs). The alignment thus obtained was used for Comparative Molecular Field Analysis (CoMFA)/Comparative Molecular Similarity Indices Analysis (CoMSIA) model development. A fourpoint common pharmacophore hypothesis, comprising of one acceptor, one hydrophobic region and two aromatic ring centres was generated. A structurally diverse set of 80 molecules was used of which 56 were grouped into training set to develop the model and the rest 24 molecules into test set to validate the CoMFA/CoMSIA models. The models so developed showed a good r(2)predictive of 0.7587 for CoMFA and 0.7737 for CoMSIA. CoMFA and CoMSIA models had excellent Q(2) (cross-validated coefficient) of 0.7125 and 0.7093, respectively which showed high correlative and predictive abilities of the models. The 3-D contour maps of CoMFA/CoMSIA provided interpretable explanation of SAR for the compounds and also permitted interesting conclusions about the substituent effects on the phenothiazine derivatives. The outcomes of the study would help in the rational design of novel and potent therapeutic agents as specific BuChE inhibitors for symptomatic or disease modifying treatment of AD.

3D-QSAR analysis on ATR protein kinase inhibitors using CoMFA and CoMSIA.

Ataxia telangiectasia-mutated and Rad3-related (ATR) protein kinase is an attractive anticancer target. In this study, comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were performed on a series of aminopyrazine ATR inhibitors. The models generated by CoMFA had a cross-validated coefficient (q(2)) of 0.752 and a regression coefficient (r(2)) of 0.947. The CoMSIA models had a q(2) of 0.728 and an r(2) of 0.936. The reasonable quantitative structure-activity relationship model showed robust predictive ability. The contour map provided guidelines for building novel virtual compounds based on compound NO.40. In addition, the 3D structure of ATR was modeled by homology modeling. Molecular dynamic simulations were employed to optimize the structure. The docking results offered insights into the interactions between the inhibitors and the active site for potent analysis. This study provides useful guidance for the discovery of more potent compounds.

Design, synthesis, antiviral activity and three-dimensional quantitative structure-activity relationship study of novel 1,4-pentadien-3-one derivatives containing the 1,3,4-oxadiazole moiety.

1,4-Pentadien-3-one and 1,3,4-oxadiazole derivatives possess good antiviral activities, and their substructure units are usually used in antiviral agent design. In order to discover novel molecules with high antiviral activities, a series of 1,4-pentadien-3-one derivatives containing the 1,3,4-oxadiazole moiety were designed and synthesised. Bioassays showed that most of the title compounds exhibited good inhibitory activities against tobacco mosaic virus (TMV) in vivo. The compound 8f possessing the best protective activity against TMV had an EC50 value of 135.56 mg L(-1) , which was superior to that of ribavirin (435.99 mg L(-1) ). Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques were used in three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of protective activities, with values of q(2) and r(2) for the CoMFA and CoMSIA models of 0.751 and 0.775 and 0.936 and 0.925 respectively. Compound 8k with higher protective activity (EC50 = 123.53 mg L(-1) ) according to bioassay was designed and synthesised on the basis of the 3D-QSAR models. Some of the title compounds displayed good antiviral activities. 3D-QSAR models revealed that the appropriate compact electron-withdrawing and hydrophobic group at the benzene ring could enhance antiviral activity. These results could provide important structural insights for the design of highly active 1,4-pentadien-3-one derivatives.

Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach

CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q2>0.4, r2>0.5 and r2pred>0.5. Based on better q2 and r2pred values, the best predictions were obtained for the CoMFA (model 5 q2=0.488, r2pred=0.732), and CoMSIA (model 45 q2=0.525, r2pred=0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.

3D-QSAR study of tetrahydro-3H-imidazo[4,5-c]pyridine derivatives as VEGFR-2 kinase inhibitors using various charge schemes.

Vascular endothelial growth factor-2 receptor (VEGFR-2) kinase is a promising target for the development of novel anticancer drugs. Three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on a series of tetrahydro-3H-imidazo[4,5-c]pyridine derivatives to understand the structural basis for VEGFR-2 inhibitory activity. Several 3D-QSAR models were developed using various partial atomic charge schemes. Comparative molecular field analysis (CoMFA) and Comparative molecular similarity indices analysis (CoMSIA) methods were employed to derive these models. The CoMFA models performed better than the CoMSIA models. The reliable CoMFA model was obtained with the Gasteiger-Marsili charge scheme. The model produced statistically significant results with a cross-validated correlation coefficient (q(2)) of 0.635 and a coefficient of determination (r(2)) of 0.930. The model showed reasonable predictive power with predictive correlation coefficient ([Formula: see text]) of 0.582. Robustness of the model was further checked by leave-five-out cross-validation, bootstrapping and progressive scrambling analysis. The model was found to be statistically robust and expected to assist in the design of novel compounds with enhanced VEGFR-2 inhibitory activity.

Comparative Molecular Similarity Indices Analysis of Caspase-3 Inhibitors

Caspases, a family of cysteinyl aspartate-specific proteases plays a central role in the regulation and the execution of apoptotic cell death. Activation of caspases-3 stimulates a signaling pathway that ultimately leads to the death of the cell. Hence, caspase-3 has been proven to be an effective target for reducing the amount of cellular and tissue damage. In this work, comparative molecular similarity indices analysis (CoMSIA) was performed on a series of 3,4-dihydropyrimidoindolones derivatives which are inhibitors of caspase-3. The best predictions were obtained for CoMSIA model (<TEX>$q^2$</TEX> = 0.586, <TEX>$r^2$</TEX> = 0.955). The predictive ability of test set (<TEX>$r^2_{pred}$</TEX>) was 0.723. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. Our theoretical results could be useful to design novel and more potent caspase-3 derivatives.

Comparative Molecular Similarity Indices Analysis Studies on Chromone Derivatives as Topoisomerase I Inhibitors

DNA Topoisomerase I is an enzyme, which is involved in DNA replication, transcription and chromosome segregation in cellular processes. Inhibitors of Topoisomerase I are designed to interrupt DNA replication in cancer cells, resulting in cell death. The goal of these studies was to identify structural features necessary for increasing inhibitory effect of Topoisomerase I and further use these features for designing novel anticancer agents. 3D-QSAR method was performed for 15 chromone derivatives using Comparative Molecular Similarity Indices Analysis (CoMSIA) method. The best CoMSIA model was obtained from AM1 geometry optimization and align database alignment. The satisfactory CoMSIA model based on steric, electrostatic, hydrophobic and hydrogen bond acceptor fields gave a leave-one-out cross-validation q2 = 0.753 and a non-cross-validation r2 = 0.987. Therefore, the resulting contour maps produced by the best CoMSIA models could be used to understand the important structural features responsible for the design and development of new highly active topoisomerase I inhibitors.