AffinityVAE: A multi-objective model for protein-ligand affinity prediction and drug design.In the prediction of protein-ligand affinity, the traditional methods require a large amount of computing resources, and have certain limitations in predicting and simulating the structural changes.  This paper proposes an affinity variational autoencoder (AffinityVAE) using interaction feature mapping and a variational autoencoder, which consists of a multi-objective model capable of end-to-end affinity prediction and drug discovery.  AffinityVAE is then retrained using this extended training set to further validate the protein-ligand binding affinity prediction.  Comparisons were conducted between the AffinityVAE and recent methods to demonstrate the high efficiency of the proposed model.  The experimental results show that AffinityVAE has very high prediction performance, and it has the potential to enhance the diversity and the amount of protein-ligand binding data, which promotes the drug development.
AffinityVAE
AffinityVAE: A multi-objective model for protein-ligand affinity prediction and drug design.In the prediction of protein-ligand affinity, the traditional methods require a large amount of computing resources, and have certain limitations in predicting and simulating the structural changes.  This paper proposes an affinity variational autoencoder (AffinityVAE) using interaction feature mapping and a variational autoencoder, which consists of a multi-objective model capable of end-to-end affinity prediction and drug discovery.  AffinityVAE is then retrained using this extended training set to further validate the protein-ligand binding affinity prediction.  Comparisons were conducted between the AffinityVAE and recent methods to demonstrate the high efficiency of the proposed model.  The experimental results show that AffinityVAE has very high prediction performance, and it has the potential to enhance the diversity and the amount of protein-ligand binding data, which promotes the drug development.
AffinityVAE