Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules
The advent of machine learning (ML) in computational chemistry heralds a transformative approach to one of the quintessential challenges in computer-aided drug design (CADD): the accurate and cost-effective calculation of atomic interactions. By leveraging a neural network (NN) potential, we address...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Research |
| Online Access: | https://spj.science.org/doi/10.34133/research.0837 |
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| _version_ | 1849223262627692544 |
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| author | Manyi Yang Duo Zhang Xinyan Wang BoWen Li Linfeng Zhang Weinan E Tong Zhu Han Wang |
| author_facet | Manyi Yang Duo Zhang Xinyan Wang BoWen Li Linfeng Zhang Weinan E Tong Zhu Han Wang |
| author_sort | Manyi Yang |
| collection | DOAJ |
| description | The advent of machine learning (ML) in computational chemistry heralds a transformative approach to one of the quintessential challenges in computer-aided drug design (CADD): the accurate and cost-effective calculation of atomic interactions. By leveraging a neural network (NN) potential, we address this balance and push the boundaries of the NN potential’s representational capacity. Our work details the development of a robust general-purpose NN potential, architected on the framework of DPA-2, a deep learning potential with attention, which demonstrates remarkable fidelity in replicating the interatomic potential energy surface for drug-like molecules comprising 8 critical chemical elements: H, C, N, O, F, S, Cl, and P. We employed state-of-the-art molecular dynamic (MD) techniques, including temperature acceleration and enhanced sampling, to construct a comprehensive dataset to ensure exhaustive coverage of relevant configurational spaces. Our rigorous testing protocols, including torsion scanning, structure relaxation, and high-temperature MD simulations across various organic molecules, have culminated in an NN model that achieves chemical precision commensurate with the highly regarded density functional theory model while substantially outstripping the accuracy of prevalent semi-empirical methods. This study presents a leap forward in the predictive modeling of molecular interactions, offering extensive applications in drug development and beyond. |
| format | Article |
| id | doaj-art-c7c12a19ab3e434d86f70be30ec96691 |
| institution | Kabale University |
| issn | 2639-5274 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Research |
| spelling | doaj-art-c7c12a19ab3e434d86f70be30ec966912025-08-25T19:50:27ZengAmerican Association for the Advancement of Science (AAAS)Research2639-52742025-01-01810.34133/research.0837Ab Initio Accuracy Neural Network Potential for Drug-Like MoleculesManyi Yang0Duo Zhang1Xinyan Wang2BoWen Li3Linfeng Zhang4Weinan E5Tong Zhu6Han Wang7The Institute of Green Chemistry and Engineering, Nanjing University, Suzhou, Jiangsu 215163, China.AI for Science Institute, Beijing 100080, China.DP Technology, Beijing 100080, China.Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.AI for Science Institute, Beijing 100080, China.AI for Science Institute, Beijing 100080, China.Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.National Key Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100088, China.The advent of machine learning (ML) in computational chemistry heralds a transformative approach to one of the quintessential challenges in computer-aided drug design (CADD): the accurate and cost-effective calculation of atomic interactions. By leveraging a neural network (NN) potential, we address this balance and push the boundaries of the NN potential’s representational capacity. Our work details the development of a robust general-purpose NN potential, architected on the framework of DPA-2, a deep learning potential with attention, which demonstrates remarkable fidelity in replicating the interatomic potential energy surface for drug-like molecules comprising 8 critical chemical elements: H, C, N, O, F, S, Cl, and P. We employed state-of-the-art molecular dynamic (MD) techniques, including temperature acceleration and enhanced sampling, to construct a comprehensive dataset to ensure exhaustive coverage of relevant configurational spaces. Our rigorous testing protocols, including torsion scanning, structure relaxation, and high-temperature MD simulations across various organic molecules, have culminated in an NN model that achieves chemical precision commensurate with the highly regarded density functional theory model while substantially outstripping the accuracy of prevalent semi-empirical methods. This study presents a leap forward in the predictive modeling of molecular interactions, offering extensive applications in drug development and beyond.https://spj.science.org/doi/10.34133/research.0837 |
| spellingShingle | Manyi Yang Duo Zhang Xinyan Wang BoWen Li Linfeng Zhang Weinan E Tong Zhu Han Wang Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules Research |
| title | Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules |
| title_full | Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules |
| title_fullStr | Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules |
| title_full_unstemmed | Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules |
| title_short | Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules |
| title_sort | ab initio accuracy neural network potential for drug like molecules |
| url | https://spj.science.org/doi/10.34133/research.0837 |
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