Target-Specific Drug Design Method Combining Deep Learning and Water Pharmacophore

Following identification of a target protein, hit identification, which finds small organic molecules that bind to the target, is an important first step of a structure-based drug design project. In this study, we demonstrate a target-specific drug design method that can autonomously generate a series of target-favorable compounds. This method utilizes the seq2seq model based on a deep learning algorithm and a water pharmacophore. Water pharmacophore models are used to screen compounds that are favorable to a given target in a large compound database, and seq2seq compound generators are used to train the screened compounds and generate entirely new compounds based on the training model. Our method was tested through binding energy calculation studies of six pharmaceutically relevant targets in the directory of useful decoys (DUD) set with docking.