Machine Learning to Predict Potential Energy Surface of Resveratrol Drug: A Quantum-Level Calculation

IF 3.5 3区医学 Q2 CHEMISTRY, MEDICINAL ACS Medicinal Chemistry Letters Pub Date : 2024-11-01 DOI:10.1021/acsmedchemlett.4c0038210.1021/acsmedchemlett.4c00382

Hossein Shirani*, and , Seyed Majid Hashemianzadeh*,

引用次数: 0

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-trans-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.

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机器学习预测白藜芦醇药物的势能面：量子级计算

我们研究了在密度泛函理论数据集上训练的 ANI-1x 神经网络势能，它是一种量子级机器学习计算方法，可在极短的计算时间内预测白藜芦醇（3,5,4′-三羟基-反式二苯乙烯）抗帕金森病药物的势能面。在 wB97X/6-31G(d) 理论水平上，使用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制药计算研究、药物化学、药物发现和设计提供重要见解，从而做出宝贵贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Medicinal Chemistry Letters CHEMISTRY, MEDICINAL-

CiteScore

7.30

自引率

2.40%

发文量

328

审稿时长

1 months

期刊介绍： ACS Medicinal Chemistry Letters is interested in receiving manuscripts that discuss various aspects of medicinal chemistry. The journal will publish studies that pertain to a broad range of subject matter, including compound design and optimization, biological evaluation, drug delivery, imaging agents, and pharmacology of both small and large bioactive molecules. Specific areas include but are not limited to: Identification, synthesis, and optimization of lead biologically active molecules and drugs (small molecules and biologics) Biological characterization of new molecular entities in the context of drug discovery Computational, cheminformatics, and structural studies for the identification or SAR analysis of bioactive molecules, ligands and their targets, etc. Novel and improved methodologies, including radiation biochemistry, with broad application to medicinal chemistry Discovery technologies for biologically active molecules from both synthetic and natural (plant and other) sources Pharmacokinetic/pharmacodynamic studies that address mechanisms underlying drug disposition and response Pharmacogenetic and pharmacogenomic studies used to enhance drug design and the translation of medicinal chemistry into the clinic Mechanistic drug metabolism and regulation of metabolic enzyme gene expression Chemistry patents relevant to the medicinal chemistry field.

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