Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction
This addresses the problem of limited and noisy datasets for drug discovery researchers, offering a novel framework that enhances prediction performance.
The paper tackled the challenge of molecular activity prediction in drug discovery by introducing Similarity-Quantized Relative Learning (SQRL), which reformulates the problem as relative difference learning between similar compounds, resulting in improved accuracy and generalization in low-data regimes.
Accurate prediction of molecular activities is crucial for efficient drug discovery, yet remains challenging due to limited and noisy datasets. We introduce Similarity-Quantized Relative Learning (SQRL), a learning framework that reformulates molecular activity prediction as relative difference learning between structurally similar pairs of compounds. SQRL uses precomputed molecular similarities to enhance training of graph neural networks and other architectures, and significantly improves accuracy and generalization in low-data regimes common in drug discovery. We demonstrate its broad applicability and real-world potential through benchmarking on public datasets as well as proprietary industry data. Our findings demonstrate that leveraging similarity-aware relative differences provides an effective paradigm for molecular activity prediction.