Compound dataset and custom code for deep generative multi-target compound design

Abstract

Aim: Generating a data and software infrastructure for evaluating multi-target compound (MT-CPD) design via deep generative modeling. Methodology: The REINVENT 2.0 approach for generative modeling was extended for MT-CPD design and a large benchmark data set was curated. Exemplary results & data: Proof-of-concept for deep generative MT-CPD design was established. Custom code and the benchmark set comprising 2809 MT-CPDs, 61,928 single-target and 295,395 inactive compounds from biological screens are made freely available. Limitations & next steps: MT-CPD design via deep learning is still at its conceptual stages. It will be required to demonstrate experimental impact. The data and software we provide enable further investigation of MT-CPD design and generation of candidate molecules for experimental programs.Lay abstract Small molecules with well-defined activity against multiple biological targets are increasingly considered for therapy of complex diseases. Generating such compounds is far from being trivial. Therefore, deep machine learning, a form of artificial intelligence, is applied to aid in this process. For this purpose, we have generated a data set and software that we make freely available to further advance deep learning for designing multi-target compounds.Graphical abstract A group of three compounds with multi-or single-target activity or no activity (no target). For grouping of compounds according to a set of targets, confirmed activity or inactivity against all targets is taken into account.