AbstractHere, chemoinformatics is considered as a theoretical chemistry discipline complementary to quantum chemistry and force‐field molecular modeling. These three fields are compared with respect to molecular representation, inference mechanisms, basic concepts and application areas. A chemical space, a fundamental concept of chemoinformatics, is considered with respect to complex relations between chemical objects (graphs or descriptor vectors). Statistical Learning Theory, one of the main mathematical approaches in structure‐property modeling, is briefly reviewed. Links between chemoinformatics and its “sister” fields – machine learning, chemometrics and bioinformatics are discussed.