Co-VAE: Drug-Target Binding Affinity Prediction by Co-Regularized Variational Autoencoders

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 12; pp. 8861 - 8873
• Main Authors: Li, Tianjiao, Zhao, Xing-Ming, Li, Limin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affinity; Binding; co-regularized VAE; Computational modeling; drug-target binding affinity; Drugs; Feature extraction; Lower bounds; Mathematical models; Predictive models; Probabilistic logic; Proteins; Regularization; Variational autoencoders
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2021.3120428

Identifying drug-target interactions has been a key step in drug discovery. Many computational methods have been proposed to directly determine whether drugs and targets can interact or not. Drug-target binding affinity is another type of data which could show the strength of the binding interaction between a drug and a target. However, it is more challenging to predict drug-target binding affinity, and thus a very few studies follow this line. In our work, we propose a novel co-regularized variational autoencoders (Co-VAE) to predict drug-target binding affinity based on drug structures and target sequences. The Co-VAE model consists of two VAEs for generating drug SMILES strings and target sequences, respectively, and a co-regularization part for generating the binding affinities. We theoretically prove that the Co-VAE model is to maximize the lower bound of the joint likelihood of drug, protein and their affinity. The Co-VAE could predict drug-target affinity and generate new drugs which share similar targets with the input drugs. The experimental results on two datasets show that the Co-VAE could predict drug-target affinity better than existing affinity prediction methods such as DeepDTA and DeepAffinity, and could generate more new valid drugs than existing methods such as GAN and VAE.