Figure 1.

AI and deep generative model applications in the drug discovery pipeline

Several successful applications of AI and deep generative models in various stage of the drug development pipeline: (A) AI-assistant target selection and validation, (B) molecular design, lead optimization, and chemical synthesis, (C) biological evaluation (in vitro and in vivo), clinical development, and post marketing surveillance, and (D) several successful preclinical and clinical molecules identified by AI and deep generative models. DDR1, discoidin domain receptor 1; DDR2, discoidin domain receptor tyrosine kinase 2; GSK3B, glycogen synthase kinase 3 beta; JNK3, c-Jun N-terminal kinase 3.