Bridging between Structure-Based and Data-Driven Affinity Prediction.
TL;DR
This work introduces a method to smoothly transition from physics-based to knowledge-based predictions based on the uncertainty of each model and shows that combining structure-based and ML models significantly improves the prediction accuracy if training data is limited, whereas the weighting smoothly shifts from docking to ML as more data is acquired.
Abstract
Protein-ligand binding affinity prediction is central in computer-aided drug design, and a wide range of physics-based, empirical, and machine learning (ML) tools have been developed for this purpose. Scientists are often tasked with choosing an optimal tool for a given discovery problem, which can be difficult. Physical models typically perform best in the absence of target-specific data but are often outperformed by ML models as the amount of data on a given target grows. Here, we introduce a method to smoothly transition from physics-based to knowledge-based predictions based on the uncertainty of each model. We apply this framework to combine docking scores with predictions from a Gaussian Process model trained on binding affinities from two industrial data sets. We show that combining structure-based and ML models significantly improves the prediction accuracy if training data is limited, whereas the weighting smoothly shifts from docking to ML as more data is acquired. We also show that structure-based methods, being insensitive to the distribution of binders across chemical space, can improve generalizability to new chemistries and increase the hit rate in active learning screens where the binder density varies among scaffolds in a virtual library. Thus, integrating predictions from multiple tools not only optimizes the use of limited experimental data but also ensures more robust performance compared to reliance on a single model.