MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction

Drug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces. Computational approaches using machine learning have...

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Vydáno v:Journal of chemical information and modeling Ročník 64; číslo 13; s. 4980
Hlavní autoři: Ranjan, Amit, Bess, Adam, Alvin, Chris, Mukhopadhyay, Supratik
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 08.07.2024
Témata:
Deep Learning
Drug Discovery - methods
Machine Learning
Pharmaceutical Preparations - chemistry
Pharmaceutical Preparations - metabolism
Protein Binding
Proteins - chemistry
Proteins - metabolism
ISSN:1549-960X, 1549-960X
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Shrnutí:Drug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces. Computational approaches using machine learning have emerged to narrow down the drug candidate search space. However, most of these prediction models focus on single feature encoding of drugs and targets, ignoring the importance of integrating different dimensions of these features. We propose a deep learning-based approach called Multi-Dimensional Fusion for Drug Target Affinity Prediction (MDF-DTA) incorporating different dimensional features. Our model fuses 1D, 2D, and 3D representations obtained from different pretrained models for both drugs and targets. We evaluated MDF-DTA on two standard benchmark data sets: DAVIS and KIBA. Experimental results show that MDF-DTA outperforms many state-of-the-art techniques in the DTA task across both data sets. Through ablation studies and performance evaluation metrics, we evaluate the importance of individual representations and the impact of each representation on MDF-DTA.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1549-960X
1549-960X
DOI:10.1021/acs.jcim.4c00310