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• W4214482316 abstract "As a key element in structure-based drug design, binding affinity prediction (BAP) for putative protein-ligand complexes can be efficiently achieved by the incorporation of structural descriptors and machine-learning models. However, developing concise descriptors that will lead to accurate and interpretable BAP remains a difficult problem in this field. Herein, we introduce the profiles of intermolecular contacts (IMCPs) as descriptors for machine-learning-based BAP. IMCPs describe each group of protein-ligand contacts by the count and average distance of the group members, and collaborate closely with classical machine-learning models. Performed on multiple validation sets, IMCP-based models often result in better BAP accuracy than those originating from other similar descriptors. Additionally, IMCPs are simple and concise, and easy to interpret in model training. These descriptors highly conclude the structural information of protein-ligand complexes and can be easily updated with personalized profile features. IMCPs have been implemented in the BAP Toolkit on github ( https://github.com/debbydanwang/BAP)." @default.
• W4214482316 created "2022-03-02" @default.
• W4214482316 creator A5032665783 @default.
• W4214482316 creator A5034998718 @default.
• W4214482316 date "2022-01-01" @default.
• W4214482316 modified "2023-10-10" @default.
• W4214482316 title "Protein-ligand binding affinity prediction based on profiles of intermolecular contacts" @default.
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• W4214482316 doi "https://doi.org/10.1016/j.csbj.2022.02.004" @default.
• W4214482316 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35317230" @default.
• W4214482316 hasPublicationYear "2022" @default.
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