On the application of artificial intelligence in virtual screening DOI
Thanawat Thaingtamtanha, R Ravichandran, Francesco Gentile

et al.

Expert Opinion on Drug Discovery, Journal Year: 2025, Volume and Issue: unknown

Published: May 19, 2025

Artificial intelligence (AI) has emerged as a transformative tool in drug discovery, particularly virtual screening (VS), which is crucial initial step identifying potential candidates. This article highlights the significance of AI revolutionizing both ligand-based (LBVS) and structure-based (SBVS) approaches, streamlining enhancing discovery process. The authors provide an overview applications with focus on LBVS SBVS approaches utilized prospective cases where new bioactive molecules were identified experimentally validated. Discussion includes use quantitative structure-activity relationship (QSAR) modeling for LBVS, well its role techniques such molecular docking dynamics simulations. based literature searches all studies published up to March 2025. rapidly transforming VS by leveraging increasing amounts experimental data expanding scalability. These innovations promise enhance efficiency precision across yet challenges curation, rigorous validation models, efficient integration methods remain critical realizing AI's full discovery.

Language: Английский

Artificial intelligence for RNA–ligand interaction prediction: advances and prospects DOI
Jing Li, Ying Tan, Ruiqiang Lu

et al.

Drug Discovery Today, Journal Year: 2025, Volume and Issue: unknown, P. 104366 - 104366

Published: April 1, 2025

Language: Английский

Citations

0
Integrating Machine Learning-Based Pose Sampling with Established Scoring Functions for Virtual Screening DOI Creative Commons
Thahir Vu, Hosein Fooladi, Johannes Kirchmair

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Physics-based docking methods have long been the cornerstone of structure-based virtual screening (VS). However, emergence machine learning (ML)-based approaches has opened new possibilities for enhancing VS technologies. In this study, we explore integration DiffDock-L, a leading ML-based pose sampling method, into workflows by combining it with Vina, Gnina, and RTMScore scoring functions. We assess integrated approach in terms its effectiveness, quality, complementarity to traditional physics-based methods, such as AutoDock Vina. Our findings from DUDE-Z benchmark dataset show that DiffDock-L performs competitively both performance cross-docking settings. most cases, generates physically plausible biologically relevant poses, establishing itself viable alternative algorithms. Additionally, found choice function significantly influences success.

Language: Английский

Citations

0
Deep learning methods for protein representation and function prediction: A comprehensive overview DOI
Mingqing Wang, Zhiwei Nie,

Yonghong He

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 155, P. 110977 - 110977

Published: May 14, 2025

Language: Английский

Citations

0
On the application of artificial intelligence in virtual screening DOI
Thanawat Thaingtamtanha, R Ravichandran, Francesco Gentile

et al.

Expert Opinion on Drug Discovery, Journal Year: 2025, Volume and Issue: unknown

Published: May 19, 2025

Artificial intelligence (AI) has emerged as a transformative tool in drug discovery, particularly virtual screening (VS), which is crucial initial step identifying potential candidates. This article highlights the significance of AI revolutionizing both ligand-based (LBVS) and structure-based (SBVS) approaches, streamlining enhancing discovery process. The authors provide an overview applications with focus on LBVS SBVS approaches utilized prospective cases where new bioactive molecules were identified experimentally validated. Discussion includes use quantitative structure-activity relationship (QSAR) modeling for LBVS, well its role techniques such molecular docking dynamics simulations. based literature searches all studies published up to March 2025. rapidly transforming VS by leveraging increasing amounts experimental data expanding scalability. These innovations promise enhance efficiency precision across yet challenges curation, rigorous validation models, efficient integration methods remain critical realizing AI's full discovery.

Language: Английский

Citations

0