基于结构特征的药物靶点亲和力预测OA

Predicting the binding affinity between drugs and their target proteins is a key steps in developing new drugs.Traditional wet experiments are time-consuming and expensive.With the rapid development of artificial intelligence technology,the application of Deep Learning technology in the drug screening phase has the potential to significantly enhance research and development efficiency.A method for predicting drug target binding affinity based on Convolutional Neural Networks is proposed to address the above issues.The structural features of proteins and small molecules are transformed into corresponding three-dimensional matrices,these matrices are fed into respective three-dimensional Convolutional Neural Networks for training.Then,feature values are extracted through several layers of fully connected neural networks to obtain the final binding affinity value.The experimental results indicate that the model can effectively predict the binding affinity of drug targets and has good application prospects.