AI-based generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. As a result the quality of the generated molecules suffers. Methods have been developed to estimate molecule synthesizability, but so far, there is no consensus on how to determine whether or not a molecule is synthesizable. We developed the Retro-Score (RScore), which computes a synthetic feasibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning tool Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We compared RScore with other synthetic scores from the literature, and developed and executed a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further investigated this method of generation by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables to obtain more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on https://github.com/iktos/generation-under-synthetic- constraint.

Molecule generation under the constraint of synthesizability

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