Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning

In drug design, the design and manufacture of safe and effective compounds is a long-term, complex, and complicated process. Therefore, developing a new rapid and generalizable drug design method is of great value. This study aimed to propose a general model based on reinforcement learning combined with drug-target interaction, which could be used to design new molecules according to different protein targets. The method adopted recurrent neural network molecular modeling and took the drug-target affinity model as the reward function of optimal molecular generation. It did not need to know the three-dimensional structure and active sites of protein targets but only required the information of a one-dimensional amino acid sequence. This approach was demonstrated to produce drugs highly similar to marketed drugs and design molecules with a better binding energy.

Automated summary

In drug design, developing a new rapid and generalizable drug design method is of great value. This study proposed a general model based on reinforcement learning combined with drug-target interaction to design new molecules based on different protein targets. The method used recurrent neural network molecular modeling and the drug-target affinity model as the reward function. It only required the information of a one-dimensional amino acid sequence, without the need for the three-dimensional structure and active sites of protein targets. This approach showed promising results in producing drugs similar to marketed drugs and designing molecules with better binding energy.