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.

翻译：人工智能驱动的方法能够大幅改善历史上成本高昂的药物设计过程，各类生成模型已得到广泛应用。特别是用于从头药物设计的生成模型，专注于完全从零开始创造新型生物化合物，代表着一个前景广阔的未来方向。该领域的快速发展，加上药物设计过程固有的复杂性，为新研究人员进入该领域造成了困难。在本综述中，我们将从头药物设计归纳为两大主题：小分子生成与蛋白质生成。在每个主题下，我们识别了多种子任务与应用，重点介绍了重要的数据集、基准测试、模型架构，并比较了顶尖模型的性能。我们对人工智能驱动的药物设计采取广泛的研究视角，既允许对各子任务内的多种方法进行微观比较，也支持跨不同领域的宏观观察。我们讨论了两类应用之间的共同挑战与应对方法，并强调了人工智能驱动的从头药物设计作为一个整体的未来发展方向。所有涵盖文献的系统整理资源库可在 https://github.com/gersteinlab/GenAI4Drug 获取。