Automated design and optimization of multitarget schizophrenia drug candidates by deep learning

Complex neuropsychiatric diseases such as schizophrenia require drugs that can target multiple G protein-coupled receptors (GPCRs) to modulate complex neuropsychiatric functions. Here, we report an automated system comprising a deep recurrent neural network (RNN) and a multitask deep neural network (MTDNN) to design and optimize multitarget antipsychotic drugs. The system has successfully generated novel molecule structures with desired multiple target activities, among which high-ranking compound 3 was synthesized, and demonstrated potent activities against dopamine D2, serotonin 5-HT1A and 5-HT2A receptors. Hit expansion based on the MTDNN was performed, 6 analogs of compound 3 were evaluated experimentally, among which compound 8 not only exhibited specific polypharmacology profiles but also showed antipsychotic effect in animal models with low potential for sedation and catalepsy, highlighting their suitability for further preclinical studies. The approach can be an efficient tool for designing lead compounds with multitarget profiles to achieve the desired efficacy in the treatment of complex neuropsychiatric diseases. [Display omitted] •An automated deep learning workflow was developed for multitarget drug design.•A MTDNN model was established to predict the activity of D2/5-HT1A/5-HT2A receptors.•A RNN model was established to generate a focused compound library.•Using this method, 7 molecules were discovered with desired multitarget activities.•Compound 8 is a promising antipsychotic drug candidate.