A Survey of Generative AI for De Novo Drug Design: New Frontiers in
  Molecule and Protein Generation
Xiangru Tang Howard Dai Elizabeth Knight Fang Wu Yunyang Li Tianxiao Li Mark Gerstein
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.