小分子表示和人工智能驱动药物研究的进步:弥合理论和应用之间的差距
Junxi Liu1, Shan Chang2, Qingtian Deng3
1Shenzhen University of Advanced Technology, Southern University of Science and Technology, Shenzhen 518055, China; Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen 518107, China.
Chinese journal of natural medicines
|November 19, 2025
概括
人工智能 (AI) 和化学信息学使用数字分子表示加速药物发现. 本综述涵盖了分子表示和下游任务的AI方法,包括传统中医药 (TCM) 的潜力.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 生物信息学是一种生物信息学.
背景情况:
- 药物发现依赖于识别有效的前体,优化成本和加速开发.
- 数字分子表示是机器可读分子的关键,提高了预测准确性和决策.
- 人工智能 (AI) 和化学信息学对于推进这些过程至关重要.
研究的目的:
- 提供对小分子表示和人工智能驱动的药物发现任务的全面审查.
- 分析分子表示如何被学习和应用在各种下游应用中.
- 突出AI在分子表征和药物发现方面的挑战和机遇.
主要方法:
- 小分子数据库的编制.
- 分析基本分子表示和机器学习模型的分析.
- 检查人工智能驱动的药物发现下游任务:药物向相互作用 (DTI),药物向亲和力 (DTA),药物特性 (DP) 预测和药物生成.
主要成果:
- 学习的分子表示增强了跨化学空间的预测任务的准确性.
- 人工智能方法在药物向相互作用,亲和力,属性预测和新药生成方面显示出显著的潜力.
- 该研究确定了机器学习在分子表示和下游任务执行方面的挑战和机会.
结论:
- 数字分子表示对于人工智能驱动的药物发现至关重要.
- 人工智能和学习表现为识别传统中医药 (TCM) 的物质和目标提供了巨大的潜力.
- 对机器学习方法的进一步研究可以改善分子表示和下游任务效率.
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