Activity prediction with more than biological endpoints simultaneously (e.g., in multi-target drug discovery and design) and performing structure-multiple activity relationships (SMARTs) is, of course, harder than predicting the activity for single endpoints e.g., single-target drug design, but it is becoming more and more necessary to develop drugs that have clinical efficacy . In the last ten years, polypharmacology has demonstrated its valuable contribution to drug design and development campaigns, offering new perspectives and methodologies to exploit available biological data e.g., in vitro, in vivo, clinical, and postmarketing data . The combination of multiparametric datasets has contributed to developing an algorithm capable of predicting side and off-target effects and drug-drug interactions more efficiently .