Structure-based, deep-learning models for protein-ligand binding affinity prediction
importance 7/10
ai assisted
Computational Drug Discovery Methods drug discovery stage: research
Problem
Predicting protein-ligand binding affinity from structure is a key open problem in computational drug discovery needing better methods.
Why it matters
Accurate binding affinity prediction could speed up drug discovery and reduce its cost.
What the AI did
A review of structure-based deep-learning approaches for binding affinity prediction, with a model taxonomy and uniform re-evaluation of representative models.
Results
not stated in source
Impact
Review organizes and compares existing models; benefit to drug discovery is described as potential, not demonstrated.
Record
| Venue | Journal of Cheminformatics |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Debby D. Wang, Wenhui Wu, Ran Wang |
| Citations | 49 (OpenAlex, at import time) |
| References | 69 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1186/s13321-023-00795-9 |
| Open access | gold |
| Keywords | deep learning, protein-ligand, binding affinity, drug discovery, review |
Importance rating
7
significant — thousands to millions affected
Binding affinity prediction is a key open problem in drug discovery, a foundational field; benefit described as potential, no scale stated.