Artem Cherkasov

TL;DR: In this paper, the authors provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive quantitative structure-activity relationship models.

TL;DR: The benefits, challenges, and opportunities of using antimicrobial peptides against multidrug-resistant pathogens are identified, advances in the deployment of novel promising antimacterial peptides are highlighted, and the needs and priorities in designing focused development strategies taking into account the most advanced tools available are underlined.

TL;DR: The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling and to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community.

TL;DR: Deep Docking is applied to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS‐CoV‐2 Mpro protein, and the compounds are made publicly available for further characterization and development by scientific community.

TL;DR: This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed inQSAR to a wide range of research areas outside of traditional QSar boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics.