Debiasing Algorithms for Protein Ligand Binding Data do not Improve Generalisation

17 May 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The structured nature of chemical data means machine learning models trained to predict protein-ligand binding risk overfitting the data, impairing their ability to generalise and make accurate predictions for novel candidate ligands. To address this limitation, data debiasing algorithms systematically partition the data to reduce bias. When models are trained using debiased data splits, the reward for simply memorising the training data is reduced, suggesting that the ability of the model to make accurate predictions for novel candidate ligands will improve. To test this hypothesis, we use distance-based data splits to measure how well a model can generalise. We first confirm that models perform better for randomly split held-out sets than for distant held-out sets. We then debias the data and find, surprisingly, that debiasing typically reduces the ability of models to make accurate predictions for distant held-out test sets. These results suggest that debiasing reduces the information available to a model, impairing its ability to generalise.


Protein Ligand Binding
Debiasing techniques
Virtual Screening
Machine Learning


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