3D-QSAR studies of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists

Py-CoMSIA: An Open-Source Implementation of Comparative Molecular Similarity Indices Analysis in Python

Background/Objectives: The progression of three-dimensional (3D) quantitative structure-activity relationship (QSAR) methodologies has significantly contributed to the advancement of medicinal chemistry and pharmaceutical discovery. Comparative Molecular Similarity Indices Analysis (CoMSIA) is a widely used 3D-QSAR technique. However, its reliance on discontinued proprietary software creates accessibility challenges. This work aims to develop an open-source Python library to address these limitations and broaden access to grid-based 3D-QSAR methods. Methods: Py-CoMSIA was developed in Python using RDKit and NumPy for calculations and PyVista for visualizations. Results: Py-CoMSIA provides a functional open-source alternative to proprietary CoMSIA software. It successfully implements the core CoMSIA algorithm and generates comparable similarity indices, as demonstrated by testing several benchmarking datasets including the original CoMSIA steroid dataset. Conclusions: The Py-CoMSIA library addresses the accessibility issues associated with proprietary 3D-QSAR software by providing an open-source Python implementation of CoMSIA. This tool broadens access to complex grid-based 3D-QSAR methodologies and offers a flexible platform for integrating advanced statistical and machine learning techniques.

Computational study on the interaction between CCR5 and HIV-1 entry inhibitor maraviroc: insight from accelerated molecular dynamics simulation and free energy calculation

C-C chemokine receptor type 5 (CCR5) is the co-receptor of human immunodeficiency virus type 1 (HIV-1) and plays an important role in HIV-1 virus infection. Maraviroc has been proved to be effective for anti-HIV-1 by targeting CCR5. Understanding the detailed interaction mechanism between CCR5 and Maraviroc will be of great help to the rational design of a more potential inverse agonist to block HIV-1 infection. Here, we performed molecular dynamics (MD) simulation and accelerated MD simulation (aMD) to study the interaction mechanism between CCR5 and Maraviroc based on a recently reported crystal structure. The results of MD simulation demonstrate that Maraviroc can form stable hydrogen bonds with residues Tyr37(1.39), Tyr251(6.51) and Glu283(7.39). The results of aMD simulation indicate that the carboxamide moiety is more flexible than the tropane group of Maraviroc in the pocket of CCR5. The electrostatic potential analysis proves that Maraviroc can escape from the pocket of CCR5 along the negative electrostatic potential pathway during the dissociation process. The free energy calculation illustrates that there exist three binding pockets during the dissociation process of Maraviroc. Our results will be useful for understanding the interaction mechanism between CCR5 and Maraviroc as well as for the rational design of a more potent inverse agonist.

Integrated computational tools for identification of CCR5 antagonists as potential HIV-1 entry inhibitors: homology modeling, virtual screening, molecular dynamics simulations and 3D QSAR analysis

Using integrated in-silico computational techniques, including homology modeling, structure-based and pharmacophore-based virtual screening, molecular dynamic simulations, per-residue energy decomposition analysis and atom-based 3D-QSAR analysis, we proposed ten novel compounds as potential CCR5-dependent HIV-1 entry inhibitors. Via validated docking calculations, binding free energies revealed that novel leads demonstrated better binding affinities with CCR5 compared to maraviroc, an FDA-approved HIV-1 entry inhibitor and in clinical use. Per-residue interaction energy decomposition analysis on the averaged MD structure showed that hydrophobic active residues Trp86, Tyr89 and Tyr108 contributed the most to inhibitor binding. The validated 3D-QSAR model showed a high cross-validated r_cv² value of 0.84 using three principal components and non-cross-validated r² value of 0.941. It was also revealed that almost all compounds in the test set and training set yielded a good predicted value. Information gained from this study could shed light on the activity of a new series of lead compounds as potential HIV entry inhibitors and serve as a powerful tool in the drug design and development machinery.

3D-QSAR STUDIES OF SUBSTITUTED 4-ARYL/HETEROARYL-4H-CHROMENES AS APOPTOSIS INDUCERS USING CoMFA AND CoMSIA

Three-dimensional (3D) quantitative structure–activity relationships (QSARs) of 36 apoptosis inducers, substituted 4-aryl/heteroaryl-4H-chromenes with anticancer activity against human breast cancer cell lines T47D, have been studied by using methods of comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA). The established 3D-QSAR models in training set show not only significant statistical quality, but also predictive ability, with high correlation coefficient (R2) values and cross-validation coefficient (q2) values: CoMFA (R2, q2: 0.944, 0.747), CoMSIA (R2, q2: 0.944, 0.704). Moreover, the predictive abilities of the CoMFA and CoMSIA models were further confirmed by a test set, giving the predictive correlation coefficients ([Formula: see text] values) of 0.845 and 0.851, respectively. Based on the CoMFA and CoMSIA contour map analyses, some key factors responsible for anticancer activity of this series of compounds have been found as follows: the steric interaction plays a decisive role in determining the anticancer activities of these compounds; bulky groups as substituent R 1 are not tolerated; in addition to a steric moderation, higher degree of electropositivity and hydrophobicity on the terminal alkyl of substituent R 2 might be favorable to the activity; the substituent R 3 should be hydrophobic; bulky and strong electron withdrawing groups for the substituent R 4 are not advantageous to the activity; simultaneously introducing large electronegative atoms as hydrogen-acceptors to the first atoms of the substituents R 5 and R 6 may increase the activity, but substituents R 5 and R 6 with a linking group – OCH 2 O – may decrease the activity. Such results can offer some useful theoretical references for understanding the action mechanism, designing more potent derivatives, and predicting their activities prior to synthesis.

2D/3D-QSAR STUDY ON ANALOGUES OF 2-METHOXYESTRADIOL WITH ANTICANCER ACTIVITY

Two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationships (QSARs) of 23 analogs of 2-Methoxyestradiol with anticancer activity (expressed as p GI50) against MCF-7 human breast cancer cells have been studied by using a combined method of the DFT, MM2 and statistics for 2D, as well as the comparative molecular field analysis (CoMFA) for 3D. The established 2D-QSAR model in training set shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient [Formula: see text] and the square of the cross-validation coefficient (q2= 0.779). The same model was further applied to predict p GI50values of the four compounds in the test set, and the resulting [Formula: see text] being as high as 0.827, further confirms that this 2D-QSAR model has high predictive ability for this kind of compound. The 3D-QSAR model also shows good correlative and predictive capabilities in terms of R2(0.927) and q2(0.786) obtained from CoMFA model. The results that 2D- and 3D-QSAR analyses accord with each other, suggest that the electrostatic interaction plays a decisive role in determining the anticancer activity of the studied compounds, and that increasing the negative charge of substituent R2and the positive charge of substituents linking to C17as well as decreasing the size of substituent R1are advantageous to improving the cytotoxicity. Such results can offer some useful theoretical references for directing the molecular design and understanding the action mechanism of this kind of compound with anticancer activity.

QSAR models for CXCR2 receptor antagonists based on the genetic algorithm for data preprocessing prior to application of the PLS linear regression method and design of the new compounds using in silico virtual screening

The CXCR2 receptors play a pivotal role in inflammatory disorders and CXCR2 receptor antagonists can in principle be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 130 antagonists of the CXCR2 receptors and their activities were investigated by the partial least squares (PLS) method. The genetic algorithm (GA) has been proposed for improvement of the performance of the PLS modeling by choosing the most relevant descriptors. The results of the factor analysis show that eight latent variables are able to describe about 86.77% of the variance in the experimental activity of the molecules in the training set. Power prediction of the QSAR models developed with SMLR, PLS and GA-PLS methods were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. A comparison between the different developed methods indicates that GA-PLS can be chosen as supreme model due to its better prediction ability than the other two methods. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied to the proposed QSAR model and the structure and potency of new compounds were predicted. The developed models were found to be useful for the estimation of pIC₅₀ of CXCR2 receptors for which no experimental data is available.

Molecular modeling studies on 11H-dibenz[b,e]azepine and dibenz[b,f][1,4]oxazepine derivatives as potent agonists of the human TRPA1 receptor

A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed on a series of the 11H-dibenz[b,e]azepine and dibenz[b,f][1,4]oxazepine derivatives as potent agonists of the human TRPA1 receptor. The CoMFA and CoMSIA models resulting from a 21 molecule training set gave r²(cv) values of 0.631 and 0.542 and r² values of 0.986 and 0.981, respectively. The statistically significant models were validated by a test set of five compounds with predictive r²(pred). values of 0.967 and 0.981 for CoMFA and CoMSIA, respectively. A systemic external validation was also performed on the established models. The information obtained from 3D counter maps could facilitate the design of more potent human TRPA1 receptor agonists.

Molecular modeling studies of 4,5-dihydro-1H-pyrazolo[4,3-h] quinazoline derivatives as potent CDK2/Cyclin a inhibitors using 3D-QSAR and docking

CDK2/cyclin A has appeared as an attractive drug targets over the years with diverse therapeutic potentials. A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) followed by molecular docking studies were performed on a series of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as potent CDK2/cyclin A inhibitors. The CoMFA and CoMSIA models, using 38 molecules in the training set, gave r(2) (cv) values of 0.747 and 0.518 and r(2) values of 0.970 and 0.934, respectively. 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. Molecular docking was applied to explore the binding mode between the ligands and the receptor. The information obtained from molecular modeling studies may be helpful to design novel inhibitors of CDK2/cyclin A with desired activity.

Therapeutic strategies underpinning the development of novel techniques for the treatment of HIV infection

The HIV replication cycle offers multiple targets for chemotherapeutic intervention, including the viral exterior envelope glycoprotein, gp120; viral co-receptors CXCR4 and CCR5; transmembrane glycoprotein, gp41; integrase; reverse transcriptase; protease and so on. Most currently used anti-HIV drugs are reverse transcriptase inhibitors or protease inhibitors. The expanding application of simulation to drug design combined with experimental techniques have developed a large amount of novel inhibitors that interact specifically with targets besides transcriptase and protease. This review presents details of the anti-HIV inhibitors discovered with computer-aided approaches and provides an overview of the recent five-year achievements in the treatment of HIV infection and the application of computational methods to current drug design.