小分子药物发现计算方法的现状

IF 6.8 1区 医学 Q1 CHEMISTRY, MEDICINAL
Weijun Xu
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引用次数: 0

摘要

2024 年是计算科学令人兴奋的一年,"人工神经网络 "获得了诺贝尔物理学奖,"蛋白质结构预测和设计 "获得了诺贝尔化学奖。鉴于计算机辅助药物设计(CADD)和药物发现中的人工智能(AIDD)的快速发展,编写一份总结其现状和未来发展方向的文件对于《药物化学杂志》的读者来说是非常及时和有意义的。本评论旨在强调这两个领域的最新发展、主要挑战和潜在协同作用,为文献和科学博客中正在进行的讨论做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Current Status of Computational Approaches for Small Molecule Drug Discovery

Current Status of Computational Approaches for Small Molecule Drug Discovery
2024 has been an exciting year for computational sciences, with the Nobel Prize in Physics awarded for “artificial neural network” and the Nobel Prize in Chemistry presented for “protein structure prediction and design”. Given the rapid advancements in Computer-Aided Drug Design (CADD) and Artificial Intelligence in Drug Discovery (AIDD), a document summarizing their current standing and future directions would be timely and relevant to the readership of Journal of Medicinal Chemistry. This piece of commentary aims to highlight recent developments, key challenges, and potential synergies between these fields, contributing to ongoing discussions in the literature and scientific blogs.
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来源期刊
Journal of Medicinal Chemistry
Journal of Medicinal Chemistry 医学-医药化学
CiteScore
4.00
自引率
11.00%
发文量
804
审稿时长
1.9 months
期刊介绍: The Journal of Medicinal Chemistry is a prestigious biweekly peer-reviewed publication that focuses on the multifaceted field of medicinal chemistry. Since its inception in 1959 as the Journal of Medicinal and Pharmaceutical Chemistry, it has evolved to become a cornerstone in the dissemination of research findings related to the design, synthesis, and development of therapeutic agents. The Journal of Medicinal Chemistry is recognized for its significant impact in the scientific community, as evidenced by its 2022 impact factor of 7.3. This metric reflects the journal's influence and the importance of its content in shaping the future of drug discovery and development. The journal serves as a vital resource for chemists, pharmacologists, and other researchers interested in the molecular mechanisms of drug action and the optimization of therapeutic compounds.
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