The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry

Abstract

This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures’ relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.

Discovery of Tetrahydropyrazolopyrazine Derivatives as Potent and Selective MYT1 Inhibitors for the Treatment of Cancer

“…To target the H-bond donor of Gln196, the substructure generation was performed using the structure-based drug design (SBDD). , Several five-fused six-membered N-heterocycles were selected to be introduced to the amide (Figure ). Both the MYT1 HTRF binding assay and the WEE1 ADP-Glo functional assay were conducted.…”

Accelerated chemical science with AI

“…Numerous recent review and perspective articles have extensively explored the role of data science, ML and AI in various domains of experimental chemistry, including general chemistry, 1 synthetic chemistry and chemical reactions, 2–5 as well as theoretical topics such as chemical compound space exploration 6 and force-field development. 7,8 Additionally, recent reviews have addressed the application of autonomous research systems in materials science, 9–16 organic chemistry, 17–19 inorganic chemistry, 20 porous materials, 21 nanoscience, 22,23 drug formulation 24,25 and biomaterials. 26 Reviews also exist on the topic of self-driving laboratories 27,28 and their low-cost incarnations.…”

Complexity of life sciences in quantum and AI era

“…ML and AI are currently revolutionizing many areas of life sciences from bioinformatics to drug discovery and clinical research 19,20,36–44 . However, as with any data‐driven approach it faces challenges in generalization, processing small data, and requiring huge resources for training.…”

“…ML and AI are currently revolutionizing many areas of life sciences from bioinformatics to drug discovery and clinical research. 19,20,[36][37][38][39][40][41][42][43][44] However, as with any data-driven approach it faces challenges in generalization, processing small data, and requiring huge resources for training. In living systems where the computational domain consists of a set of nested hierarchical models, 45 AI methods work well on each of the hierarchical scales but cannot capture the whole picture.…”