Research On A New Method Of Generating Potential Anti-HIV Active Molecules Based On Deep Learning

AIDS is one of the severest diseases to humans,which is caused by HIV infection.Nowadays,there is still no effective way to cure AIDS globally,and anti-HIV drugs are one of the most effective means to prevent and cure AIDS.However,HIV is drug-fast.Therefore,it is necessary to keep developing more anti-HIV drugs through discovering new anti-HIV active molecules.This research improves existing design approaches of new drugs,and adopts two disparate methods to generate potential anti-HIV active molecules to expand the potential anti-HIV active molecule library.This research provides ideas for the discovery of new anti-HIV active molecules.The main innovations and work include the following aspects:(1)Building a deep molecule generation model DGMM,aiming to generate molecules with effective structures,novelty and unbiased properties.DGMM is constructed based on three recurrent units which are MLSTM,SRU,and QRNN.It uses a large molecular data set derived from ChEMBL for training.After training,the MLSTM-based DGMM achieves the best results.The average validity of its generated molecules is 98.31%,the uniqueness is 99.93%,and the novelty is 89.33%,which is better than the existing chemical language models comprehensively.The properties of the molecules generated by the optimal DGMM and those of the training set compared,the experiment shows that the molecules generated by the DGMM can restore the properties distribution of the molecules of the training set,which verifies the unbiased properties of the molecules generated by the DGMM.(2)Building a deep transfer molecule generation model T-DGMM to generate potential anti-HIV active molecules and amplify the potential anti-HIV active molecule library;building an anti-HIV activity prediction model AAPM to verify whether the molecules generated by T-DGMM have potential anti-HIV activity.T-DGMM was trained on anti-HIV activity data sets of different scales to verify the effectiveness of the transfer learning method.Finally,molecules with known anti-HIV activity were detected in molecules generated by T-DGMM trained on the data set of extremely small scale.AAPM is built with different deep learning architectures.The scale of the training set consists of 10,000 positive and 10,000 negative samples.The final accuracy of the AAPM based on DNN is 88.90% in external validation set.Finally,on the basis of AAPM,the anti-HIV activity of T-DGMM-generated molecules was predicted,the highest 68.29% of which were judged as anti-HIV activity,verifying the effectiveness of T-DGMM.(3)Building a deep reinforcement molecule generation model R-DGMM to perform two distinct tasks.The first task is to generate analogues of Rilpivirine,and RDGMM generated nine anti-HIV active molecules including Dapivirine.The second task designed a combined scoring function,aiming to generate molecules with potential anti-HIV activity,expected synthetic accessibility,and expected drug-likeness.Finally,R-DGMM generated two molecules with known anti-HIV activity.The two tasks indicate that R-DGMM is suitable for generating potential anti-HIV active molecules.