Structure-based protein–ligand interaction fingerprints for binding affinity prediction
Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037021004839 |
| _version_ | 1818974378722852864 |
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| author | Debby D. Wang Moon-Tong Chan Hong Yan |
| author_facet | Debby D. Wang Moon-Tong Chan Hong Yan |
| author_sort | Debby D. Wang |
| collection | DOAJ |
| description | Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks. |
| first_indexed | 2024-12-20T15:39:07Z |
| format | Article |
| id | doaj.art-0eb163c916dc4917b73286811f8c9bd6 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-12-20T15:39:07Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-0eb163c916dc4917b73286811f8c9bd62022-12-21T19:35:16ZengElsevierComputational and Structural Biotechnology Journal2001-03702021-01-011962916300Structure-based protein–ligand interaction fingerprints for binding affinity predictionDebby D. Wang0Moon-Tong Chan1Hong Yan2School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd, Shanghai 200093, China; Corresponding author.School of Science and Technology, Hong Kong Metropolitan University, 30 Good Shepherd St, Ho Man Tin, Hong KongDepartment of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong KongBinding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.http://www.sciencedirect.com/science/article/pii/S2001037021004839Interaction fingerprintProtein–ligand binding affinityScoring functionMachine learningComputer-aided drug design |
| spellingShingle | Debby D. Wang Moon-Tong Chan Hong Yan Structure-based protein–ligand interaction fingerprints for binding affinity prediction Computational and Structural Biotechnology Journal Interaction fingerprint Protein–ligand binding affinity Scoring function Machine learning Computer-aided drug design |
| title | Structure-based protein–ligand interaction fingerprints for binding affinity prediction |
| title_full | Structure-based protein–ligand interaction fingerprints for binding affinity prediction |
| title_fullStr | Structure-based protein–ligand interaction fingerprints for binding affinity prediction |
| title_full_unstemmed | Structure-based protein–ligand interaction fingerprints for binding affinity prediction |
| title_short | Structure-based protein–ligand interaction fingerprints for binding affinity prediction |
| title_sort | structure based protein ligand interaction fingerprints for binding affinity prediction |
| topic | Interaction fingerprint Protein–ligand binding affinity Scoring function Machine learning Computer-aided drug design |
| url | http://www.sciencedirect.com/science/article/pii/S2001037021004839 |
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