Identification of thrombopoiesis inducer based on a hybrid deep neural network model

Thrombocytopenia is a common haematological problem worldwide. Currently, there are no relatively safe and effective agents for the treatment of thrombocytopenia. To address this challenge, we propose a computational method that enables the discovery of novel drug candidates with haematopoietic activities. Based on different types of molecular representations, three deep learning (DL) algorithms, namely recurrent neural networks (RNNs), deep neural networks (DNNs), and hybrid neural networks (RNNs+DNNs), were used to develop clas-sification models to distinguish between active and inactive compounds. The evaluation results illustrated that the hybrid DL model exhibited the best prediction performance, with an accuracy of 97.8 % and Matthews correlation coefficient of 0.958 on the test dataset. Subsequently, we performed drug discovery screening based on the hybrid DL model and identified a compound from the FDA-approved drug library that was structurally divergent from conventional drugs and showed a potential therapeutic action against thrombocytopenia. The novel drug candidate wedelolactone significantly promoted megakaryocyte differentiation in vitro and increased platelet levels and megakaryocyte differentiation in irradiated mice with no systemic toxicity. Overall, our work demonstrates how artificial intelligence can be used to discover novel drugs against thrombocytopenia.