Protein‐Ligand Structure and Affinity Prediction in CASP16 Using a Geometric Deep Learning Ensemble and Flow Matching
Article Swipe
Alex Morehead
,
Jian Liu
,
Pawan Neupane
,
Nabin Giri
,
Jianlin Cheng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/prot.26827
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/prot.26827
Predicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein‐ligand structure (poses) produced by the latest deep learning methods to identify the best poses and how to accurately estimate the binding affinity between a protein target and a list of ligand candidates. Further, a blind benchmarking and assessment of protein‐ligand structure and binding affinity prediction is necessary to ensure it generalizes well to new settings. Towards this end, we introduce MULTICOM_ ligand, a deep learning‐based protein‐ligand structure and binding affinity prediction ensemble featuring structural consensus ranking for unsupervised pose ranking and a new deep generative flow matching model for joint structure and binding affinity prediction. Notably, MULTICOM_ ligand ranked among the top‐5 ligand prediction methods in both protein‐ligand structure prediction and binding affinity prediction in the 16th Critical Assessment of Techniques for Structure Prediction (CASP16), demonstrating its efficacy and utility for real‐world drug discovery efforts. The source code for MULTI COM_ ligand is freely available on GitHub.
Related Topics
Concepts
Ligand (biochemistry)
Drug discovery
Computer science
Artificial intelligence
Ranking (information retrieval)
Protein structure prediction
Protein ligand
Matching (statistics)
Computational biology
Machine learning
Ligand efficiency
Protein structure
Chemistry
Mathematics
Biology
Biochemistry
Statistics
Receptor
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/prot.26827
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/prot.26827
- OA Status
- bronze
- Cited By
- 2
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409269934
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409269934Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/prot.26827Digital Object Identifier
- Title
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Protein‐Ligand Structure and Affinity Prediction in
CASP16 Using a Geometric Deep Learning Ensemble and Flow MatchingWork title - Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-08Full publication date if available
- Authors
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Alex Morehead, Jian Liu, Pawan Neupane, Nabin Giri, Jianlin ChengList of authors in order
- Landing page
-
https://doi.org/10.1002/prot.26827Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/prot.26827Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/prot.26827Direct OA link when available
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Ligand (biochemistry), Drug discovery, Computer science, Artificial intelligence, Ranking (information retrieval), Protein structure prediction, Protein ligand, Matching (statistics), Computational biology, Machine learning, Ligand efficiency, Protein structure, Chemistry, Mathematics, Biology, Biochemistry, Statistics, ReceptorTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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