Chemical compound databases with annotated bioactivity data provide an essential basis for various applications in drug design. Through computer-aided drug design (CADD) and recently with new artificial intelligence (AI) techniques, it has been possible to accelerate the generation of knowledge from big data in biological, chemical and pharmaceutical medicine. The methods developed in CADD, which have been optimized with machine learning (ML) algorithms, can use the vast chemical space combined with its biological information to obtain compounds with safety, efficacy, and low toxicity, a goal in many drug design projects.