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Bioresour BioprocessPaul D, Ghosh M, Mandal M.
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Evaluating molecular docking for binding affinity predictions: a systematic analysis of key parameters and the utility of AlphaFold2 structures for the Schrödinger dataset. [PDF]
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Breaking the barriers of data scarcity in drug-target affinity prediction.
Briefings in Bioinformatics, 2023Abstract Accurate prediction of drug–target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive,
Haiguang Liu, Yingce Xia, Lijun Wu
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Hierarchical graph representation learning for the prediction of drug-target binding affinity
Information Sciences, 2022 The identification of drug-target binding affinity (DTA) has attracted increasing attention in the drug discovery process due to the more specific interpretation than binary interaction prediction. Recently, numerous deep learning-based computational methods have been proposed to predict the binding affinities between drugs and targets benefiting from ...
Shichao Liu, Yuan Quan, Xiong-Hui Zhou
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SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network
International Journal of Molecular Sciences, 2021 The prediction of drug–target affinity (DTA) is a crucial step for drug screening and discovery. In this study, a new graph-based prediction model named SAG-DTA (self-attention graph drug–target affinity) was implemented.
Mingjian Jiang, Zhiqiang Wei
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A Framework for Improving the Generalizability of Drug–Target Affinity Prediction Models
Journal of Computational Biology, 2023Statistical models that accurately predict the binding affinity of an input ligand-protein pair can greatly accelerate drug discovery. Such models are trained on available ligand-protein interaction data sets, which may contain biases that lead the predictor models to learn data set-specific, spurious patterns instead of generalizable relationships ...
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Modality-DTA: Multimodality Fusion Strategy for Drug–Target Affinity Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations.
Bosheng Song +2 more
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A Mutual Attention Model for Drug Target Binding Affinity Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022Vrious machine learning approaches have been developed for drug-target interaction (DTI) prediction. One class of these approaches, DTBA, is interested in Drug-Target Binding Affinity strength, rather than focusing merely on the presence or absence of interaction. Several machine learning methods have been developed for this purpose.
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