A novel approach for recommendation-based prioritization of growth vectors in hit-to-lead optimization

A new approach for the prioritized hit compound optimization directions at the hit-to-lead (H2L) stage is proposed in this study. The high variability in the level of expert assessment among researchers and inefficient resource management during the H2L stage exert a strong influence on the success probability of the overall drug discovery process, which highlights the demand of implementing decision support systems (DSS) designed to integrate the advantages of various commonly accepted practices. Within the framework of the study, an algorithm was developed that combines methods of structure-based drug design (SBDD) with a reverse fragment-based drug design (R-FBDD) approach, allowing for the formation of rationalized recommendations for hit molecule growth directions based on the analysis of fragment contributions to the binding energy and the estimation of fragment group efficiency (GE). Retrospective application of the method to a series of Notum carboxylesterase inhibitors demonstrated a good correlation between the prioritized growth vectors proposed by the algorithm and the experimentally implemented H2L optimization strategies, confirming the potential of the proposed approach as a decision support tool at the hit compound optimization stage.