Artikel ; Online: AQDnet: Deep Neural Network for Protein-Ligand Docking Simulation.
ACS omega
2023 Band 8, Heft 26, Seite(n) 23925–23935
Abstract: We have developed an innovative system, AI QM Docking Net (AQDnet), which utilizes the three-dimensional structure of protein-ligand complexes to predict binding affinity. This system is novel in two respects: first, it significantly expands the training ...
Abstract | We have developed an innovative system, AI QM Docking Net (AQDnet), which utilizes the three-dimensional structure of protein-ligand complexes to predict binding affinity. This system is novel in two respects: first, it significantly expands the training dataset by generating thousands of diverse ligand configurations for each protein-ligand complex and subsequently determining the binding energy of each configuration through quantum computation. Second, we have devised a method that incorporates the atom-centered symmetry function (ACSF), highly effective in describing molecular energies, for the prediction of protein-ligand interactions. These advancements have enabled us to effectively train a neural network to learn the protein-ligand quantum energy landscape (P-L QEL). Consequently, we have achieved a 92.6% top 1 success rate in the CASF-2016 docking power, placing first among all models assessed in the CASF-2016, thus demonstrating the exceptional docking performance of our model. |
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Sprache | Englisch |
Erscheinungsdatum | 2023-06-16 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article |
ISSN | 2470-1343 |
ISSN (online) | 2470-1343 |
DOI | 10.1021/acsomega.3c02411 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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