DLSSAffinity: protein–ligand binding affinity prediction <i>via</i> a deep learning model
Huiwen Wang, Haoquan Liu, Shangbo Ning, Chengwei Zeng, Yunjie Zhao
Abstract
= 0.79, RMSE = 1.40, and SD = 1.35 on the test set. Comparing DLSSAffinity with the existing state-of-the-art deep learning-based binding affinity prediction methods, the DLSSAffinity model outperforms other models. These results demonstrate that combining global sequence and local structure information as the input features of a deep learning model can improve the accuracy of protein-ligand binding affinity prediction.
Topics & Concepts
Ligand (biochemistry)Drug discoveryBenchmark (surveying)Protein ligandComputer scienceArtificial intelligenceSequence (biology)Computational biologyDeep learningTarget proteinBiological systemMachine learningChemistryBiologyBiochemistryGeographyReceptorGeodesyGeneProtein Structure and DynamicsComputational Drug Discovery MethodsBiochemical and Structural Characterization