Summary of Deep Learning For Protein-ligand Docking: Are We There Yet?, by Alex Morehead et al.
Deep Learning for Protein-Ligand Docking: Are We There Yet?
by Alex Morehead, Nabin Giri, Jian Liu, Pawan Neupane, Jianlin Cheng
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning study explores the performance of deep learning methods for protein-ligand docking and structure prediction in various scenarios. The researchers introduce PoseBench, a comprehensive benchmark that evaluates these methods’ ability to predict accurate structures for proteins with novel sequences. They find that co-folding methods generally outperform traditional docking baselines but struggle with chemical specificity when predicting new or multi-ligand targets. The study highlights the importance of understanding the limitations and strengths of deep learning models in protein-ligand docking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Protein-ligand docking is important for drug discovery and biomedical research. Scientists have developed deep learning methods to predict how proteins bind with ligands. But these methods don’t always work well, especially when dealing with new or unknown proteins. A team of researchers created a special benchmark called PoseBench to test these methods. They found that some methods are better than others at predicting protein structures and binding patterns. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
