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Development of a Deep Learning Model for Binding Affinity Prediction and Fragment-based de novo Drug Design

Shiwei Wang  Jianfeng Pei  Luhua Lai  
【摘要】:The traditional drug design and discovery methods were time-consuming and expensive,which largely reduced the efficiency of drug design process.Computer-based de novo drug design was applied to accelerate the procedure of drug discovery.As a complementary of high-throughput screening technology,de novo drug design method is confronted with a virtually infinite search space.New drug-like molecules can be discovered by exploring the chemical space with specific combinational search strategies,which provides a new approach to find novel drugs.However,challenges including combinatorial explosion,scoring function and so on hinder the development and application of de novo drug design.In this work,we hope to construct a reinforcement-learning model by combing Monto Carlo Tree Search method and deep neural network to solve the combinatorial explosion problems.In order to achieve our goals,we updated our Pocket program to extract more accurate pharmacophore models,which can help to extract features of 3 D structure information of protein-ligand complex.Meanwhile,we developed a new scoring function to predict binding affinity of protein-ligand complex based on the 3 D structure information.By further improving and optimizing,the new scoring function could be applied to binding affinity prediction and will be integrated to our fragment-based de novo drug design method.

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