Year: 2025
Type: article
Abstract: Protein-ligand docking is a computational method routinely used in many structural biology applications. It usually involves one receptor and one ligand. The docking of multiple ligands, however, can be important in several situations, such as the study of synergistic effects, substrate and product inhibition, or competitive binding. This can be a challenging and computationally demanding process. By integrating Particle Swarm Optimization into the established AutoDock Vina framework, we provided a powerful tool capable of accelerating drug discovery, and computational enzymology. Here we present Moldina (Multiple-Ligand Molecular Docking over AutoDock Vina), a new algorithm built upon AutoDock Vina. Through comprehensive testing against AutoDock Vina, the algorithm exhibited comparable accuracy in predicting ligand binding conformations while significantly reducing the computational time up to several hundred times. Moldina and the benchmark data are freely available at https://opencode.it4i.eu/permed/moldina-multiple-ligand-molecular-docking-over-autodock-vina and https://github.com/It4innovations/moldina-multiple-ligand-molecular-docking-over-autodock-vina .
Source: Journal of Cheminformatics
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
Awards: G1054376342, G1761812275, G1940663492, G2020983025, G208869049, CETOCOEN Excellence, G2601449437, National institute for Neurological Research, Czech National Infrastructure for Biological Data, G434337789, Center for Artificial Intelligence and Quantum Computing in System Brain Research, G8063554275, G941802975
OpenAlex ID: W4409870583