Machine learning gains a lot of focus these days. The wide spectrum of tools and rushing growth of database volumes cause machine learning to influence every aspect of life . When searching for machine learning applications in chemistry one may get an impression that the subject is dominated by drug discovery. Due to growing attention of machine learning, novel applications and prospects emerge along with literature covering the subject. Quantitative structure -activity/property relation (QSAR/QSPR) methods and algorithms are founded on an assumption that molecular structure is correlated with molecule's properties. In order to translate chemical structures to computer representations a wide range of molecular descriptors were developed , alongside with molecular fingerprints like Morgan fingerprint and MACCS keys. Cherkasov et al , Varnek and Baskin give a wide overview of the core of QSAR/QSPR and cheminformatics, their history, advances and perspectives.