# CASynergy: A causal attention model for interpretable prediction of cancer drug synergy

## TL;DR

CASynergy is a new model that improves prediction of effective cancer drug combinations by identifying true genetic causes and offering clearer insights.

## Contribution

CASynergy introduces a causal attention mechanism and cell line-specific gene networks for interpretable drug synergy prediction.

## Key findings

- CASynergy outperformed five state-of-the-art models on benchmark datasets DrugCombDB and Oncology-Screen.
- The model identifies significant drug-gene interactions and provides interpretable insights into drug synergy.
- CASynergy works well across different cancer types and enhances personalized treatment strategies.

## Abstract

Cancer drug combination therapies offer a promising strategy to overcome resistance and improve treatment efficacy, but identifying synergistic drug pairs is challenging due to complex biological interactions and tumor heterogeneity. Current machine learning algorithms for drug synergy prediction primarily rely on large-scale, multimodal datasets, yet suffer from critical limitations including poor interpretability, difficulty distinguishing causative biological relationships from correlations, and inadequate modeling of cancer-specific molecular interactions. To address these challenges, we propose CASynergy (Causal Attention and Cross-attention Synergy), a novel deep learning model for predicting cancer drug synergy that addresses limitations of prior approaches in accuracy and interpretability. CASynergy introduces a causal attention mechanism to distinguish true causal genomic features from spurious correlations, cell line-specific gene network construction to capture the unique molecular context of each cancer cell line, and a cross-attention module to integrate drug molecular features with cell line gene expression profiles. These improvements allow CASynergy to clearly identify significant drug-gene interactions and provides interpretable insights into why a combination is predicted to be synergistic. Experiments on two benchmark datasets (DrugCombDB and Oncology-Screen) suggests that CASynergy outperformed five state-of-the-art models. CASynergy offers a better and more reliable way to predict effective drug combinations. It works well across different cancer types and is easier to understand, which is important for personalized cancer treatment and finding new drugs.

Cancer often requires treatment with multiple drugs simultaneously to effectively combat resistance and improve patient outcomes. However, identifying which drug combinations work best together remains challenging because of the complex and diverse nature of tumors. To address this, we developed a new computational approach called CASynergy. Unlike traditional methods, CASynergy can clearly distinguish which genetic features are truly driving drug responses from those that are merely coincidental. This capability greatly enhances the accuracy and reliability of predicting effective drug combinations. Additionally, CASynergy uses detailed biological knowledge to build unique gene interaction maps for each cancer cell type, allowing our model to capture the individual characteristics of different tumors accurately. We tested CASynergy using well-established datasets and found it consistently outperformed existing methods. Moreover, it clearly shows researchers how different drugs affect specific genes, providing valuable insights into why certain drug combinations are effective. Our method thus not only improves prediction accuracy but also helps researchers and clinicians better understand drug interactions at the molecular level. Ultimately, this can guide more personalized and effective cancer treatments.