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What is Predicting Protein-Ligand Affinity with MoLFormer about?

This research presents a machine learning model that predicts protein-ligand binding affinity, crucial for drug discovery, using transformer-based neural networks and embeddings from protein sequences and ligand structures. A dataset of approximately 14,000 protein-ligand interactions was utilized, and the model aims to enhance prediction accuracy and efficiency in drug development. The findings indicate that the XGBoost model outperformed other methods, achieving a mean absolute error of 0.1971 kJ/mol and

Author

shaswat.jain01

Language

EN