Integrating Synthetic Accessibility Scoring and AI-Based Retrosynthesis Analysis to Evaluate AI-Generated Drug Molecules Synthesizability

Background: One of the challenges of applying artificial intelligence (AI) methods to drug discovery is the difficulty of laboratory synthesizability for many AI-discovered molecules. Often, in silico techniques and metrics such as the computationally enabled synthesizability score and AI-based retrosynthesis analysis are used. Methods: In this paper, we present a predictive synthesizability method that integrates the gains of synthetic accessibility scoring and the benefits of AI-driven retrosynthesis analysis tools to evaluate the synthesizability of AI-generated lead drug molecules. Results: We explored the proposed method by using it to analyze the synthesizability of a set of 123 novel molecules generated using AI models. The analysis of the synthesis route of the four best molecules from the set in terms of synthesizability, as identified using the proposed method, is presented. Conclusions: This strategy enables quick initial screening and more comprehensive actionable synthetic pathways, thereby balancing speed and detail, and favoring simple routes to avoid the risk of pursuing non-synthesizable compounds in the drug development pipeline.