Is chemical synthetic accessibility computationally predictable for drug and lead-like molecules? A comparative assessment between medicinal and computational chemists.

The design of lead and drug-like molecules with expected desired properties and feasible chemical synthesis is one of the main objectives of computational and medicinal chemists. Prediction of synthetic feasibility of de novo molecules is often achieved by the use of in-silico tools or by advices received from medicinal and to a lesser extent from computational chemists. However, the validation of predictive tools is often performed on selection of compounds from external databases. In this study, we compare the synthetic accessibility (SA) score predicted by SYLVIA and the score estimated by medicinal chemists who synthesized the molecules. Therefore, we solicited 11 bench-based medicinal and computational chemists to score 119 lead-like molecules synthesized by same medicinal chemists. Their scores were compared with score calculated from SYLVIA software. Irrespective of the starting material database, we obtained a good agreement between average of medicinal and computational chemist scores for the ensemble of compounds; as well as between all chemists and SYLVIA SA scores with a correlation of 0.7. Furthermore, analysis of the marketed drugs since 1970 shows some consistency in average SYLVIA SA scores. Compounds entered in different phases of clinical trials show some large variation in synthetic accessibility scores due to natural-derived molecular scaffolds. Here, we proposed that the selection of compounds based on synthetically accessibility should not be done solely by one individual chemist to avoid personal gut-feeling appreciation from its experience but by a group of medicinal and computational chemists. By assessing synthetic accessibility of hundreds of compounds synthesized by medicinal chemists, we show that SYLVIA can be used efficiently to rank and prioritize virtual compound libraries in drug discovery processes.