Abstract
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.