Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis

Abstract

Chemical creativity in the design of new synthetic chemical entities (NCEs) with drug-like properties has been the domain of medicinal chemists. Here, we explore the capability of a chemistry-savvy machine intelligence to generate synthetically accessible molecules. DINGOS (design of innovative NCEs generated by optimization strategies) is a virtual assembly method that combines a rule-based approach with a machine learning model trained on successful synthetic routes described in chemical patent literature. This unique combination enables a balance between ligand-similarity-based generation of innovative compounds by scaffold hopping and the forward-synthetic feasibility of the designs. In a prospective proof-of-concept application, DINGOS successfully produced sets of de novo designs for four approved drugs that were in agreement with the desired structural and physicochemical properties. Target prediction indicated more than 50% of the designs to be biologically active. Four selected computer-generated compounds were successfully synthesized in accordance with the synthetic route proposed by DINGOS. The results of this study demonstrate the capability of machine learning models to capture implicit chemical knowledge from chemical reaction data and suggest feasible syntheses of new chemical matter. Artificial intelligence approaches can aid medicinal chemists to creatively look for new chemical entities with drug-like properties. A rule-based approach combined with a machine learning model was trained on successful synthetic routes described in chemical patent literature. This process produced computer-generated compounds that mimic known medicines.