Fragment-guided new therapeutic molecule discovery and mapping of clinically relevant interactomes

Therapeutic intervention solutions for complex diseases depend on the targeted modulation of key pathways in pathology. While growing clinical needs continue to drive advancements in the drug discovery space, current strategies primarily rely on searching large volumes of chemical data without addressing the specific contributions of molecular features. Moreover, both clinicians and researchers recognize the need for improving drug discovery methods and characterization that could aid in clinical strategy selection. To address these challenges, we offer a new perspective on targeted therapy development as well as interactome mapping, utilizing molecular fragments. The present study focused on therapeutic areas that represent emerging targets, namely JAK2 and GLP-1R, both of which have broad clinical potential. We developed a new self-adjusting neural network to capture drug features, which helped discover novel therapeutic candidates for the selected targets with improved binding. In addition, fragment-guided chemical space exploration allowed us to identify new metabolic trajectories that could support drug repurposing efforts and improve the prediction of side effects. Importantly, our work revealed that even a small compound library can effectively generate lead candidates, expediting the search and exploration process. Furthermore, building a robust in silico pipeline with integrated screening data can significantly reduce costs and guide therapy adoption. Thus, our proposed strategy underscores promising avenues for the discovery of new therapeutics and the development of clinical interventions.