Pharmacophore modeling, atom based 3D-QSAR and Molecular docking approaches to screen C-X-C chemokine receptor type 4 antagonists as microbicides for human immunodeficiency virus-1

Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors

Histone deacetylase 3 (HDAC3) is an epigenetic regulator that involves gene expression, apoptosis, and cell cycle progression, and the overexpression of HDAC3 is accountable for several cancers, neurodegeneracy, and many other diseases. Therefore, HDAC3 emerged as a promising drug target for the novel drug design. Here, we carried out the pharmacophore modeling using 50 benzamide-based HDAC3 selective inhibitors and utilized it for PHASE ligand screening to retrieve the hits with similar pharmacophore features. The dataset inhibitors of best hypotheses used to build the 3D QSAR model and the generated 3D QSAR model resulted in good PLS statistics with a regression coefficient (R2) of 0.89, predictive coefficient (Q2) of 0.88, and Pearson-R factor of 0.94 indicating its excellent predictive ability. The hits retrieved from pharmacophore-based virtual screening were subjected to docking against HDAC3 for the identification of potential inhibitors. A total of 10 hitsM1 to M10 were ranked using their scoring functions and further subject to lead optimization. The Prime MM/GBSA, AutoDock binding free energies, and ADMET studies were implemented for the selection of lead candidates. The four ligand molecules M1, M2, M3, and M4 were identified as potential leads against HDAC3 after lead optimization. The top two leads M1 and M2 were subjected to MD simulations for their stability evaluation with HDAC3. The newly designed leads M11 and M12 were identified as HDAC3 potential inhibitors from MD simulations studies. Therefore, the outcomes of the present study could provide insights into the discovery of new potential HDAC3 inhibitors with improved selectivity and activity against a variety of cancers and neurodegenerative diseases.

Discovery of novel natural products as rhodesain inhibitors for human African trypanosomiasis using in silico techniques

Human African Trypanosomiasis (HAT) or sleeping sickness is caused by the Trypanosoma brucei rhodesiense, a subspecies of the Trypanosomatide family. The parasite is associated with high morbidity and mortality rate in both animals and humans, claimed to be more fatal than other vector-transmitted diseases such as malaria. The majority of existing medications are highly toxic, not effective in the late chronic phase of the disease, and require maximum dosages to fully eradicate the parasite. In this study, we used computational methods to find out natural products that inhibit the Rhodesain, a parasitic enzyme that plays an important role in the parasite's pathogenicity, multiplication, and ability to pass through the host's blood-brain barrier. A library of 270540 natural products from ZINC databases was processed by using e-pharmacophore hypnosis and screening procedures, molecular docking, ADMET processes, and MM-GBSA calculations. This led to the identification of 3 compounds (ZINC000096269390, ZINC000035485292, and ZINC000035485242) which were then subjected to molecular dynamics. The findings of this study showed excellent binding affinity and stability toward the Rhodesain and suggest they may be a hopeful treatment for HAT in the future if further clinical trials were performed.Communicated by Ramaswamy H. Sarma.

The rise of deep learning and transformations in bioactivity prediction power of molecular modeling tools

The search and design for the better use of bioactive compounds are used in many experiments to best mimic compounds' functions in the human body. However, finding a cost-effective and timesaving approach is a top priority in different disciplines. Nowadays, artificial intelligence (AI) and particularly deep learning (DL) methods are widely applied to improve the precision and accuracy of models used in the drug discovery process. DL approaches have been used to provide more opportunities for a faster, efficient, cost-effective, and reliable computer-aided drug discovery. Moreover, the increasing biomedical data volume in areas, like genome sequences, medical images, protein structures, etc., has made data mining algorithms very important in finding novel compounds that could be drugs, uncovering or repurposing drugs and improving the area of genetic markers-based personalized medicine. Furthermore, deep neural networks (DNNs) have been demonstrated to outperform other techniques such as random forests and SVMs for QSAR studies and ligand-based virtual screening. Despite this, in QSAR studies, the quality of different data sources and potential experimental errors has greatly affected the accuracy of QSAR predictions. Therefore, further researches are still needed to improve the accuracy, selectivity, and sensitivity of the DL approach in building the best models of drug discovery.

Computer-aided identification of human carbonic anhydrase isoenzyme VII inhibitors as potential antiepileptic agents

Human carbonic anhydrase (hCA) belongs to a superfamily of metalloenzymes that reversibly catalyse the hydration of carbon dioxide to give bicarbonate (HCO₃^-) and proton (H⁺). As HCO₃^- ions play an important role in neuronal signalling hence, hCA enzymes are an attractive target for antiepileptic drugs. Out of all the isoforms, hCA VII is predominantly expressed in the brain cortex and hippocampus region, which are the most affected area during seizure activity. Hence, we have identified some hCA VII inhibitors employing computational tools like atom-based 3D quantitative structure-activity relationship (QSAR), auto-QSAR, pharmacophore-based virtual screening, molecular docking, and molecular dynamics (MD) simulations. Atom-based 3D QSAR modelling outperformed auto-QSAR with an R² and Q² value of 0.9634 and 0.9646, respectively. A four-feature pharmacophore model (AADR_1) was developed and a focussed library of around 3,00,000 compounds was screened. Compounds with a phase screen score >2.40 were selected for docking studies. The activity of the selected hits was predicted employing the developed 3D QSAR model. Finally, three compounds were taken up for the MD simulation studies which also suggest that the identified hits might form a stable complex with hCA VII enzyme. A comparative docking study was also done with other hCA isoforms like I, II, IV, IX, and XII to examine the selectivity of the identified hits towards hCA VII. Based on these studies, three hits have been identified as potential hCA VII inhibitor which is drug-like molecules. Further, in vitro studies are required to develop leads from these identified hits.Communicated by Ramaswamy H. Sarma.