Fragment Linker Prediction Using Deep Encoder-Decoder Network for PROTAC Drug Design

Drug discovery and development pipeline is a prolonged and complex process and remains challenging for both computational methods and medicinal chemists. Deep learning has shed lights in various fields and achieved tremendous success in designing novel molecules in pharmaceutical industry. We utilize state-of-the-art techniques to propose a deep neural network for rapid designing and generating meaningful drug-like Proteolysis Targeting Chimeras (PROTAC) analogs. Our method, AIMLinker, takes the structural information from the corresponding fragments and generates linkers to incorporate them. In this model, we integrate filters for excluding non-druggable structures guided by protein-protein complexes while retaining molecules with potent chemical properties. The novel PROTACs subsequently pass through molecular docking, taking root-mean square deviation (RMSD), the change of Gibbs free energy (ΔG), and the “Rule of Three” as the measurement criteria for testing the robustness and feasibility of the model. The generated novel PROTAC molecules possess similar structural information with superior binding affinity to the binding pockets in comparison to existing CRBN-dBET6-BRD4 ternary complexes. We demonstrate the effectiveness of AIMLinker having the power to design compounds for PROTAC molecules with better chemical properties.