Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets.

This study introduces the PocketCFDM generative diffusion model, aimed at improving the prediction of small molecule poses in the protein binding pockets. The model utilizes a novel data augmentation technique, involving the creation of numerous artificial binding pockets that mimic the statistical patterns of non-bond interactions found in actual protein-ligand complexes. An algorithmic method was developed to assess and replicate these interaction patterns in the artificial binding pockets built around small molecule conformers.

Computer-aided design of muscarinic acetylcholine receptor M3 inhibitors: Promising compounds among trifluoromethyl containing hexahydropyrimidinones/thiones.

The new high selective mAChRs M3 inhibitors with IC in nanomolecular ranges, which can be the prototypes for effective COPD and asthma treatment drugs, were discovered with computational approaches among trifluoromethyl containing hexahydropyrimidinones/thiones. Compounds [6-(4-ethoxy-3-methoxy-phenyl)-4-hydroxy-2-thioxo-4-(trifluoromethyl)hexahydropyrimidin-5-yl]-phenyl-methanone (THPT-1) and 5-benzoyl-6-(3,4-dimethoxyphenyl)-4-hydroxy-4-(trifluoromethyl)hexahydropyrimidin-2-one (THPO-4) have been proved to be a highly effective (with IC values of 1.62 ⋅ 10  M and 3.

3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs.

Accurate prediction of the drug-target affinity (DTA) is of critical importance for modern drug discovery. Computational methods of DTA prediction, applied in the early stages of drug development, are able to speed it up and cut its cost significantly. A wide range of approaches based on machine learning were recently proposed for DTA assessment.

Pharmacophore modeling, docking and molecular dynamics simulation for identification of novel human protein kinase C beta (PKCβ) inhibitors.

Unlabelled: Protein kinase Cβ (PKCβ) is considered as an attractive molecular target for the treatment of COVID-19-related acute respiratory distress syndrome (ARDS). Several classes of inhibitors have been already identified. In this article, we developed and validated ligand-based PKCβ pharmacophore models based on the chemical structures of the known inhibitors.

Identification of dual-targeted aminoacyl-tRNA synthetase inhibitors using machine learning.

The most serious challenge in the treatment of tuberculosis is the multidrug resistance of to existing antibiotics. As a strategy to overcome resistance we used a multitarget drug design approach. The purpose of the work was to discover dual-targeted inhibitors of mycobacterial LeuRS and MetRS with machine learning.