To deal with the tasks of molecular generation and the increasing amount of available bioactivity data, artificial intelligence has been applied to automated de novo design. Taking into account the scoring of molecules, ML approaches like target prediction that classifies compounds into active and inactive or quantitative structure-activity relationships (QSAR) could be applied Inverse QSAR or inverse quantitative structure-property relationships (QSPR) are also related with de novo design. These methodologies seek to correlate desired properties, including biological activity, to molecular structural feature.