Department of Computer Science, Yale University, New Haven, CT 06520, United States.
School of Medicine, Yale University, New Haven, CT 06520, United States.
Brief Bioinform. 2024 May 23;25(4). doi: 10.1093/bib/bbae338.
Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.
人工智能 (AI) 驱动的方法可以极大地改进历来昂贵的药物设计过程，各种生成模型已经得到广泛应用。特别是从头开始设计药物的生成模型，专注于完全从头开始创建新的生物化合物，代表了一个有前途的未来方向。该领域的快速发展，加上药物设计过程固有的复杂性，使得新研究人员难以进入。在这项调查中，我们将从头开始设计药物分为两个总体主题：小分子和蛋白质生成。在每个主题中，我们确定了各种子任务和应用，重点介绍了重要的数据集、基准和模型架构，并比较了顶级模型的性能。我们对 AI 驱动的药物设计采取了广泛的方法，既允许在每个子任务内对各种方法进行微观比较，也允许在不同领域进行宏观观察。我们讨论了这两个应用之间的平行挑战和方法，并强调了整体上 AI 驱动的从头开始药物设计的未来方向。所有涵盖来源的有组织存储库可在 https://github.com/gersteinlab/GenAI4Drug 上获得。
