Geometric Graph Learning for Protein Mutation Effect Prediction
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- Geometric Graph Learning for Protein Mutation Effect Prediction
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Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction
AbstractUnderstanding and accurately predicting protein-ligand binding affinity are essential in the drug design and discovery process. At present, machine learning-based methodologies are gaining popularity as a means of predicting binding ...
Highlights- Graph-based scoring for protein-ligand interactions, with extensive atom details.
Protein-ligand interaction prediction
Motivation: Predicting interactions between small molecules and proteins is a crucial step to decipher many biological processes, and plays a critical role in drug discovery. When no detailed 3D structure of the protein target is available, ligand-...
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![cover image ACM Conferences](/cms/asset/82db89b7-bd99-4844-b627-ad60fd85c042/3583780.cover.jpg)
- General Chairs:
- Ingo Frommholz,
- Frank Hopfgartner,
- Mark Lee,
- Michael Oakes,
- Program Chairs:
- Mounia Lalmas,
- Min Zhang,
- Rodrygo Santos
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Association for Computing Machinery
New York, NY, United States
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