2024 / 01

Next Paper
DOI: 10.24075/brsmu.2024.002

OPINION

Deep learning in modelling the protein–ligand interaction: new pathways in drug development

Barykin AD1,2, Chepurnykh TV1, Osipova ZM1,3
About authors

1 Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia

2 Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Russia

3 Pirogov Russian National Research Medical University, Moscow, Russia

Correspondence should be addressed: Zinaida Mikhailovna Osipova
Miklukho-Maklaya, 16/10, Moscow, 117997, Russia; ur.hcbi@avoksakz

About paper

Funding: the study was supported by the Russian Science Foundation grant, project № 22-44-02024 (https://rscf.ru/project/22-44-02024/).

Author contribution: Barykin AD — literature review, manuscript writing, Chepurnykh TV — concept, literature review, manuscript writing and editing, Osipova ZM — project management, manuscript editing.

Received: 2023-12-06 Accepted: 2024-01-22 Published online: 2024-02-08
|
Fig. 1. Molecular docking algorithms. А. Types of scoring functions for molecular docking. B. Variants of molecular docking algorithms and molecular dynamics
Fig. 2. Neural network operation algorithms. А. Neural network training methods. B. Major types of neural networks predicting the protein–ligand interactions