人工智能方法用于药物反应预测中的分子表示
Cui-Xiang Lin1, Yuanfang Guan2, Hong-Dong Li3
1School of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, Hunan Province, PR China.
Current opinion in structural biology
|December 13, 2023
概括
本综述探讨了人工智能 (AI) 驱动的药物反应预测的分子表示方法. 它详细介绍了当前的技术,其局限性,以及药物发现的未来发展方向.
科学领域:
- 计算化学是一种计算化学.
- 人工智能的人工智能是人工智能.
- 药物发现 药物发现
背景情况:
- 准确的药物反应预测对于个性化药物和高效的药物开发至关重要.
- 分子表示是开发药物疗效预测模型的一个关键挑战.
- 人工智能 (AI) 最近的进展显著改善了分子表示技术.
研究的目的:
- 提供对人工智能用于药物反应预测的当前分子表示方法的全面概述.
- 批判性地分析现有表示技术的原则和局限性.
- 确定和讨论未来的研究方向,以提高药物发现中的分子表征.
主要方法:
- 基于AI的分子表示方法的文献综述.
- 对不同表示类型的关键原则和基础理论的分析.
- 在药物反应预测的背景下,讨论每个方法的优点和缺点.
主要成果:
- 分类分子表示方法,包括基于结构,基于属性和基于序列的方法.
- 识别诸如可扩展性,可解释性和捕获复杂化学性质等局限性.
- 突出深度学习和图形神经网络的潜力,以改善表示.
结论:
- 分子表示是人工智能驱动药物反应预测的关键瓶.
- 需要进一步的研究来开发更强大,可解释和可通用的表示方法.
- 本综述为旨在在药物开发中推进人工智能应用的研究人员提供指导.
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