Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction
This work addresses a key challenge in drug discovery by enhancing prediction models for protein-ligand binding affinity, though it is incremental as it builds on existing deep learning methods with a new pre-training approach.
The paper tackles the problem of limited generalization in protein-ligand binding affinity prediction due to data scarcity and noisy labels from different bioassays, by proposing Multi-task Bioassay Pre-training (MBP) and constructing a dataset with over 300k affinity labels and 2.8M structures, resulting in improved performance as a general framework for structure-based prediction tasks.
Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional structure of protein-ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. In addition, different bioassays use varying affinity measurement labels (i.e., IC50, Ki, Kd), and different experimental conditions inevitably introduce systematic noise, which poses a significant challenge to constructing high-precision affinity prediction models. To address these issues, we (1) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (2) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked three-dimensional structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP as a general framework that can improve and be tailored to mainstream structure-based PLBA prediction tasks. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development.