机器学习预测白藜芦醇药物的势能面：量子水平计算
Machine Learning to Predict Potential Energy Surface of Resveratrol Drug: A Quantum-Level Calculation.
作者信息
Shirani Hossein, Hashemianzadeh Seyed Majid
机构信息
Molecular Simulation Research Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran 16846-13114, Iran.
出版信息
ACS Med Chem Lett. 2024 Nov 1;15(11):1979-1986. doi: 10.1021/acsmedchemlett.4c00382. eCollection 2024 Nov 14.
Abstract
The ANI-1x neural network potential, trained on the density functional theory data set, as a quantum-level machine learning calculation has been investigated to forecast the potential energy surfaces of the Resveratrol (3,5,4'-trihydroxy--stilbene) antiparkinsonian drug in a very short computing time. A comprehensive validation of the ANI-1x deep learning technique was provided on the Resveratrol molecule using density functional theory at the wB97X/6-31G(d) level of theory. The results showcased in this study will offer significant insights into pharmaceutical computational research, medicinal chemistry, drug discovery and design, thereby making a valuable contribution.
摘要
基于密度泛函理论数据集训练的ANI-1x神经网络势作为一种量子级机器学习计算方法，已被研究用于在极短的计算时间内预测白藜芦醇（3,5,4'-三羟基茋）抗帕金森病药物的势能面。使用理论水平为wB97X/6-31G(d)的密度泛函理论对白藜芦醇分子进行了ANI-1x深度学习技术的全面验证。本研究展示的结果将为药物计算研究、药物化学、药物发现与设计提供重要见解，从而做出有价值的贡献。
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