Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction

TL;DR

This paper introduces SQRL, a novel framework that improves molecular activity prediction by leveraging similarity-aware relative differences, significantly enhancing accuracy especially in low-data scenarios.

Contribution

The paper presents a new similarity-quantized relative learning method that reformulates activity prediction as relative difference learning, improving performance across various models.

Findings

Enhanced prediction accuracy in low-data regimes

Broad applicability demonstrated on public and proprietary datasets

Significant generalization improvements over existing methods

Abstract

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.