Data-driven targetome discovery and database requirements: insights from the therapeutic target database

Target discovery is pivotal in cutting-edge drug development and impacts translational outcomes and efficacy, the current state of research depends heavily on empirical approaches that can be costly, while there is no publicly available database of drug-target for polypharmacological correlates incorporating relevant clinical data. In the present perspective, 3 Therapeutic Target Database (TTD) exports are utilized: (1) counts of unique target classes having at least one approved drug, (2) approved-drug counts per target class, and (3) the top 20 de-duplicated drug-driven target co-occurrence pairs. These data enabled development of a data-driven map of the targetome. Through comparison of class richness versus translational yield, rating of frequency of drug driven co-occurrence target pairs, we identified emerging, high-yield and biologically plausible but understudied target families, and guide rational combination therapies. The next generation of research should update drug-target databases with more clinical information, quantitative polypharmacology, and provenance metadata to advance combination therapy.