A Survey of Generative AI for De Novo Drug Design: New Frontiers in Molecule and Protein Generation

• DOI: 10.48550/arxiv.2402.08703
• Publication Date: 2024-02-13
• Authors: Xiangru Tang; Howard Dai; Elizabeth Knight; Fang Wu; Yunyang Li; Tianxiao Li; Mark Gerstein
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Artificial Intelligence; Computational Biology; Proteins; Biomolecules (q-bio.BM); Review; Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Quantitative Biology - Biomolecules; Artificial Intelligence; Drug Design; FOS: Biological sciences; Humans

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative for de novo design, particular, focus on creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development field, combined inherent complexity creates difficult landscape new researchers to enter. In this survey, we organize into two overarching themes: small molecule and protein generation. Within each theme, identify variety subtasks applications, highlighting important datasets, benchmarks, model architectures comparing performance top models. We take broad approach AI-driven allowing both micro-level comparisons within subtask macro-level observations across different fields. discuss parallel challenges approaches between applications highlight directions as whole. An organized repository all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.