Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)

Madrigal: Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

An R package for Bayesian semi-parametric modelling of in-vitro drug combination experiments

Graph convolutional neural network for multirelational link prediction

This repository contains the graph neural networks built with PyTorch Geometric that are used in my bachelor thesis: "Drug-Drug GNN for Drug Combination Prediction and Explanations".

High-throughput Drug Synergy Prediction from Minimal Combination Data via Radial Basis Function

A statistical framework for detecting significant drug combination synergies in cancer. By leveraging tissue-specific reference null distributions across multiple synergy metrics, we compute empirical p-values to standardize synergy detection, uncover novel interactions, and enable rigorous evaluation of drug combinations.

Code for Null Models used in Sinzger et al. (2019) Scientific Reports, 9, 3002

Code implementation of the paper "A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer"

Enhancing Drug Combination Safety: A Comprehensive Solution

Official code repository for the KDD 2026 AI4Science paper ‘Expanding Knowledge Boundaries via LLM-Grounded Alignment for Drug Combination Recommendation’. It develops LLM-augmented representations for drugs and cell lines to improve drug combination prediction, especially for long-tail and cold-start cell lines on graph- and feature-based model.

An R package for Bayesian semi-parametric modelling of in-vitro drug combination experiments