PyTorch implementation of bimodal neural networks for drug-cell (pharmarcogenomics) and drug-protein (proteochemometrics) interaction prediction

Code pipeline for the PaccMann^RL in iScience: https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6

Tensorflow implementation of PaccMann (drug sensitivity prediction)

epigenome scale metabolic modeling for cancer metabolism

R scripts included in a bioinformatics pipeline for differential PGx analysis of cancer drug response

CTD-squared BeatAML DREAM Challenge

Graph Attention Network (GAT) that integrates genomics, transcriptomics & proteomics to predict cancer drug sensitivity. Built with PyTorch Geometric on TCGA data. Attention weights provide biological interpretability at gene level.

High Throughput Light Weight Regularized Regression Modeling for Molecular Data

Circadian clock subtypes and drug sensitivity in breast cancer cells (Mol Systems Biology 2025)

DpFrEP tool job executor for iPC VRE

A dataset and pre-production project for evaluating drug sensitivity scores across a network and identifying cancer dependency genes. Draws from NetMix and NETPHIX.

The Co-culture Efficacy Score (CES) is a quantitative framework for measuring drug activity in complex multicellular systems. It integrates cumulative activity and maximal efficacy into a single interpretable score, separating general toxicity from true effector-mediated cytotoxicity.

Drivers of time-of-day drug sensitivity in human cells (Communications Biology 2025)

Imagine a world where cancer treatment is as unique as your DNA.