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数字炼金术:机器和深度学习在小分子药物发现中的起

Abdul Manan¹, Eunhye Baek², Sidra Ilyas³

¹Department of Molecular Science and Technology, Ajou University, Suwon 16499, Republic of Korea.

International journal of molecular sciences

|July 29, 2025

概括

人工智能 (AI) 和机器学习 (ML) 正在通过克服传统的局限性来彻底改变药物设计. 这些计算方法加快了发现时间表,提高了开发新药的成功率.

科学领域:

计算化学是一种计算化学.
制药科学制药科学
医学中的人工智能

背景情况:

传统的小分子药物设计面临挑战,包括长时间,高成本和频繁的临床失败.
基于结构的虚拟查 (SBVS) 和基于带的虚拟查 (LBVS) 历来遇到了阻碍有效药物开发的瓶.

研究的目的:

综合分析AI和ML对现代药物设计的影响.
探索AI/ML如何解决传统药物设计方法的局限性.
为人工智能驱动的制药创新提供前性视角.

主要方法:

审查当前的AI和ML范式,包括深度学习,生成模型和强化学习.
检查化学空间探索,结合亲和力预测和蛋白质灵活性建模中的应用.
分析现实世界的案例研究,展示AI对药物发现时间表和成功概率的影响.

主要成果:

人工智能和机器学习显著提高了化学空间探索和预测准确度.
这些技术自动化了关键的设计任务,导致了发现的可量化的加速.
案例研究表明,药物开发管道的成功概率有所提高.

结论:

人工智能和机器学习代表了药物设计的变革性转变,解决了传统方法的关键局限性.
数据质量,可解释性和监管框架等挑战需要持续关注.
制药创新的未来越来越受到人工智能和先进的计算技术的驱动.

关键词:

人工智能的人工智能是人工智能.大数据就是大数据.深度学习是一种深度学习.发现药物的发现.机器学习是机器学习.虚拟选虚拟选虚拟选

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