Lee, Changsoo Won, Jonghun Ryu, Seongok Yang, Jinsol Jung, Nuri Park, Hahnbeom Seok, Chaok 2024-08-16T02:30:51Z 2024-08-16T02:30:51Z 2024-08-16 2024-08 1549-9618 https://pubs.kist.re.kr/handle/201004/150445 With the recent introduction of deep learning techniques into the prediction of biomolecular structures, structure prediction performance has significantly improved, and the potential for biomedical applications has increased considerably. The prediction of protein-ligand complex structures, applicable to the atomistic understanding of biomolecular functions and the effective design of drug molecules, has also improved with the introduction of deep learning. In this paper, it is demonstrated that docking performance can be greatly enhanced by training an energy function that encapsulates physical effects using deep learning within the framework of the traditional protein-ligand docking method. The advantage of this method, called GalaxyDock-DL, lies in its minimal overfitting to the training data compared to several existing deep learning-based protein-ligand docking methods. Unlike some recent deep learning methods, it does not use information about known binding pocket center positions. Instead, the results of this docking method show a systematic dependence on the physical properties of the target protein-ligand complexes such as atomic thermal fluctuations and binding affinity. GalaxyDock-DL utilizes the global optimization technique of the conventional protein-ligand docking method, GalaxyDock, and a neural network energy function trained to stabilize the native state compared to non-native states, just as physical free energy does. This physical principle-based approach suggests directions not only for future structure prediction involving structurally flexible biomolecular complexes but also for predicting binding affinity, thereby providing guidance for the effective design of biofunctional ligands. English American Chemical Society GalaxyDock-DL: Protein-Ligand Docking by Global Optimization and Neural Network Energy Article 10.1021/acs.jctc.4c00385 1 Journal of Chemical Theory and Computation, v.20, no.16, pp.7370 - 7382 Journal of Chemical Theory and Computation 20 16 7370 7382 N scie scopus 001286282700001 2-s2.0-85200806582 Chemistry, Physical Physics, Atomic, Molecular & Chemical Chemistry Physics Article MOLECULAR DOCKING SCORING FUNCTION EFFICIENT