Pisces: A multi-modal data augmentation approach for drug combination synergy prediction

Abstract

Drug combination therapy is a promising solution to many complicated diseases. Since experimental measurements cannot be scaled to millions of candidate combinations, many computational approaches have been developed to identify synergistic drug combinations. While most of the existing approaches either use SMILES-based features or molecular-graph- based features to represent drugs, we found that neither of these two feature modalities can comprehensively characterize a pair of drugs, necessitating the integration of these two types of features. Here, we propose Pisces, a cross-modal contrastive learning approach for synergistic drug combination prediction. The key idea of our approach is to model the combination of SMILES and molecular graphs as four views of a pair of drugs, and then apply contrastive learning to embed these four views closely to obtain high-quality drug pair embeddings. We evaluated Pisces on a recently released GDSC-Combo dataset, including 102,893 drug combinations and 125 cell lines. Pisces outperformed five existing drug combination prediction approaches under three settings, including vanilla cross validation, stratified cross validation for drug combinations, and stratified cross validation for cell lines. Our case study and ablation studies further confirmed the effectiveness of our novel contrastive learning framework and the importance of integrating the SMILES-based features and the molecular-graph-based features. Pisces has obtained the state-of-the-art results on drug synergy prediction and can be potentially used to model other pairs of drugs applications, such as drug-drug interaction. Availability: Implementation of Pisces and comparison approaches can be accessed at https://github.com/linjc16/Pisces.

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations

“…These are linear notations (e.g., SMILES and international chemical identifier), molecular fingerprints (structural keys and circular fingerprints), and graph notations (graph representations and message passing neural network-based representations). The complementarity of these representations improves model performance. , Multiomics data are used to describe different aspects of biological systems. Xia et al designed a multimodal DL model for predicting cell line response to a given drug pair.…”