Developing a variation of 3D-QSAR/MD method in drug design

In continuation of the previous reports on a combination of 3D-quantitative structure-activity relationships (QSAR) with computational molecular dynamics (MD) studies, a new variation of 3D-QSAR/MD method has been employed for drug-design as an alternative or supplementary for the typical experimental methods. The presented method is more cost-effective and less time-consuming than the previous methods and avoids several restrictions of experimental methods, such as validity estimation, and predictability. For this purpose, seven inhibitors for bromodomain (BRD)-containing protein, as an important protein in the development of different types of cancer and responsible for oncogenic rearrangements, have been selected to study of their interactions by docking and MD simulations using molecular mechanics/generalized born surface area (MM/GBSA) method. To build the proposed model, a common variant of 3D-QSAR methods, comparative molecular field analysis has been employed using a dataset of 100 MD-extracted ligand conformations and their corresponding MM/GBSA BRD4-binding energies. The results showed excellent predictability of the generated model for both the training set and test groups. Finally, two new inhibitors were selected among total 4000 designed derivatives (generated through evolutionary techniques) using the proposed 3D-QSAR-MD model. The potentials of these inhibitors were assessed by MD simulations, which showed the higher inhibitory of these compounds than the previous inhibitors. Therefore, this method showed high potentials for acceleration of the procedure of drug design and a basis for joining researchers in computational biology and pharmaceutical sciences.

In silico fight against novel coronavirus by finding chromone derivatives as inhibitor of coronavirus main proteases enzyme

Novel coronavirus, 2019-nCoV is a danger to the world and is spreading rapidly. Very little structural information about 2019-nCoV make this situation more difficult for drug designing. Benzylidenechromanones, naturally occurring oxygen heterocyclic compounds, having capability to inhibit various protein and receptors, have been designed here to block mutant variety of coronavirus main protease enzyme (SARC-CoV-2 Mpro) isolated from 2019-nCoV with the assistance of molecular docking, bioinformatics and molecular electrostatic potential. (Z)-3-(4'-chlorobenzylidene)-thiochroman-4-one showed highest binding affinity to the protein. Binding of a compound to this protein actually inhibits the replication and transcription of the virus and, ultimately, stop the virus multiplication. Incorporation of any functional groups to the basic benzylidenechromanones enhances their binding ability. Chloro and bromo substitutions amplify the binding affinity. ADME studies of all these compounds indicate they are lipophilic, high gastro intestine absorbable and blood-brain barrier permeable. The outcome reveals that the investigated benzylidenechromanones can be examined in the case of 2019-nCoV as potent inhibitory drug of SARC-CoV-2 Mpro, for their strong inhibition ability, high reactivity and effective pharmacological properties.

QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors

In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibitory activities of a series of newly synthesized chemicals is reported. This hybrid docking-QSAR approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease. Then the molecular descriptors were calculated from these conformations. Multiple linear regression (MLR) and least square support vector machine (LS-SVM) were used as QSAR models, respectively. The obtained results reveal that statistical parameters of the LS-SVM model are better than the MLR model, which indicate that there are some non-linear relations between selected molecular descriptors and anti-HIV activities of interested chemicals. The correlation coefficient (R), root mean square error (RMSE) and average absolute error (AAE) for LS-SVM are: R=0.988, RMSE=0.207 and AAE=0.145 for the training set, and R=0.965, RMSE=0.403 and AAE=0.338 for the test set. Leave one out cross validation test was used for assessment of the predictive power and validity of models which led to cross-validation correlation coefficient QUOTE of 0.864 and 0.850 and standardized predicted relative error sum of squares (SPRESS) of 0.553 and 0.581 for LS-SVM and MLR models, respectively.

A classification study of respiratory Syncytial Virus (RSV) inhibitors by variable selection with random forest

Experimental pEC₅₀s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development.

Synthesis and evaluation of 2-(2,6-dihalophenyl)-3-pyrimidinyl-1,3-thiazolidin-4-one analogues as anti-HIV-1 agents

A series of 2-(2,6-dihalophenyl)-3-(substituted pyrimidinyl)-1,3-thiazolidin-4-ones were designed on the prediction of quantitative structure-activity relationship (QSAR) studies, synthesized, and evaluated as HIV-1 reverse transcriptase inhibitors. Our attempts in correlating the identified molecular surface features related properties for modeling the HIV-1 RT inhibitory activity resulted in some statistically significant QSAR models with good predictive ability. The results showed that compounds 4m and 4n were highly active in inhibiting HIV-1 replication with EC(50) values in the range of 22-28 nM in MT-4 as well as in CEM cells with selectivity indexes of >10,000. The derived models collectively suggest that the compounds should be compact without bulky substitution on its peripheries for better HIV-1 RT inhibitory activity. These models also indicate a preference for hydrophobic compounds to obtain good HIV-1 RT inhibitory activity.

Molecular docking studies on 4-thiazolidinones as HIV-1 RT inhibitors

Flexible docking simulations were performed on two series of 4-thiazolidinones as HIV-1 reverse transcriptase (HIV-1 RT) inhibitors. This was done by analyzing the interaction of these compounds with the allosteric site of the HIV-1 reverse transcriptase enzyme. The binding scores for these compounds were also congruent with their anti-HIV activity. A good correlation between the predicted binding free energies and the experimentally observed inhibitory activities (EC(50)) suggest that the identified binding conformations of these inhibitors are reliable. The results of docking studies provide an insight into the pharmacophoric structural requirements for the HIV-1 RT inhibitory activity of this class of molecules.

QSAR by LFER model of HIV protease inhibitor mannitol derivatives using FA-MLR, PCRA, and PLS techniques

The present quantitative structure-activity relationship (QSAR) study attempts to explore the structural and physicochemical requirements of mannitol derivatives for HIV protease inhibitory activity using linear free energy related model of Hansch. QSAR models have been developed using electronic (Hammett sigma), hydrophobicity (pi), and steric (molar refractivity and STERIMOL L, B1, and B5) parameters of phenyl ring substituents of the compounds along with appropriate dummy variables. Whole molecular descriptors like partition coefficient (logP(calcd)) and molar refractivity (MR) were also tried as additional descriptors. Statistical techniques like stepwise regression, multiple linear regression with factor analysis as the data preprocessing step (FA-MLR), principal component regression analysis (PCRA), and partial least squares (PLS) analysis were applied to identify the structural and physicochemical requirements for HIV protease inhibitory activity. The generated equations were statistically validated using leave-one-out technique. The quality of equations obtained from stepwise, FA-MLR, PCRA, and PLS are of acceptable statistical range (explained variance ranging from 74.0% to 80.5%, while predicted variance ranges from 70.3% to 77.1%). The coefficient of molar refractivity shows that the activity decreases with increase in volume. Lipophilicity of the para substituents at Y position is conducive to the activity while lipophilicity of the para substituents at X position is detrimental to the activity. The coefficients of molar refractivity (mr(Y_p)) and STERIMOL parameters for para substituents at X and Y positions (B1(X_p) and B5(Y_p)) of the phenyl rings indicate that the width of the substituents at X position and the overall size of para substituents at Y position are the detrimental factors for the activity. The fluoro substituent at ortho position (Y) decreases the activity when compared to the corresponding unsubstituted congener. Presence of hydrogen bond donor groups at para position (Y) also reduces the activity. Additionally, presence of substituent at ortho position (X) and the presence of substituent at para position (Y) are conducive for the activity. Presence of fluorine at X and Y positions also increases the activity.