生成式AI在从头药物设计中的综述：分子与蛋白质生成的新前沿
A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation
AI总结：
本综述系统梳理生成式AI在从头药物设计中的应用，分为小分子与蛋白质生成两大主题，比较模型性能并指出未来方向。
摘要：
人工智能（AI）驱动的方法可以极大地改善历史上成本高昂的药物设计过程，各种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建新型生物化合物，代表了有前景的未来方向。该领域的快速发展，加上药物设计过程固有的复杂性，为新研究者进入该领域创造了困难的环境。在本综述中，我们将从头药物设计组织为两大主题：小分子生成和蛋白质生成。在每个主题中，我们识别了多种子任务和应用，强调了重要的数据集、基准和模型架构，并比较了顶级模型的性能。我们采用广泛的方法来研究AI驱动的药物设计，允许在每个子任务内进行各种方法的微观比较，以及跨不同领域的宏观观察。我们讨论了两种应用之间的并行挑战和方法，并强调了AI驱动的从头药物设计整体的未来方向。所有涵盖来源的有组织存储库可在 https://github.com/gersteinlab/GenAI4Drug 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
