GNINA 1.3: the next increment in molecular docking with deep learning DOI Creative Commons
Andrew T. McNutt, Yanjing Li, Rocco Meli

et al.

Journal of Cheminformatics, Journal Year: 2025, Volume and Issue: 17(1)

Published: March 2, 2025

Abstract Computer-aided drug design has the potential to significantly reduce astronomical costs of development, and molecular docking plays a prominent role in this process. Molecular is an silico technique that predicts bound 3D conformations two molecules, necessary step for other structure-based methods. Here, we describe version 1.3 open-source software Gnina . This release updates underlying deep learning framework PyTorch, resulting more computationally efficient paving way seamless integration methods into pipeline. We retrained our CNN scoring functions on updated CrossDocked2020 v1.3 dataset introduce knowledge-distilled facilitate high-throughput virtual screening with Furthermore, add functionality covalent docking, where atom ligand covalently receptor. update expands scope further positions as user-friendly, framework. available at https://github.com/gnina/gnina Scientific contributions : GNINA open source tool enhanced support models effective screening.

Language: Английский

Functionally active modulators targeting the LRRK2 WD40 repeat domain identified by FRASE-bot in CACHE Challenge #1 DOI Creative Commons
Akhila Mettu,

Marta Glavatskikh,

Xiaowen Wang

et al.

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenges emerged as real-life stress tests for computational hit-finding strategies. In CACHE Challenge #1, 23 participants contributed their original workflows to identify small-molecule ligands the WD40 repeat (WDR) LRRK2, a promising Parkinson's target. We applied FRASE-based robot (FRASE-bot), platform interaction-based screening allowing drastic reduction explorable chemical space and concurrent detection putative ligand-binding sites. two rounds, 84 compounds were procured experimental testing 8 confirmed bind LRRK2-WDR with dissociation constants (K d) ranging from 3 41 μM. To investigate functional effect WDR ligands, they tested ability modify LRRK2 activity markers in HEK293T cells. Two showed statistically significant increases kinase WT affected conformation major mutants.

Language: Английский

Citations

0
GNINA 1.3: the next increment in molecular docking with deep learning DOI Creative Commons
Andrew T. McNutt, Yanjing Li, Rocco Meli

et al.

Journal of Cheminformatics, Journal Year: 2025, Volume and Issue: 17(1)

Published: March 2, 2025

Abstract Computer-aided drug design has the potential to significantly reduce astronomical costs of development, and molecular docking plays a prominent role in this process. Molecular is an silico technique that predicts bound 3D conformations two molecules, necessary step for other structure-based methods. Here, we describe version 1.3 open-source software Gnina . This release updates underlying deep learning framework PyTorch, resulting more computationally efficient paving way seamless integration methods into pipeline. We retrained our CNN scoring functions on updated CrossDocked2020 v1.3 dataset introduce knowledge-distilled facilitate high-throughput virtual screening with Furthermore, add functionality covalent docking, where atom ligand covalently receptor. update expands scope further positions as user-friendly, framework. available at https://github.com/gnina/gnina Scientific contributions : GNINA open source tool enhanced support models effective screening.

Language: Английский

Citations

0