The Drug Response Prediction 2022 project in Computational Biology and Artificial Intelligence (COMBINE) Laboratory, McGill University.

CaDRReS-Sc is a framework for analyzing drug response heterogeneity based on single-cell RNA-seq data

Tensorflow implementation of PaccMann (drug sensitivity prediction)

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

DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks

Drug-Target Indication Prediction by Integrating End-to-End Learning and Fingerprints

CrossTx: Cross-cell line Transcriptomic Signature Predictions

This model was trained on six existing medical Q&A datasets (NedQA, MedMCQA, PubMedQA, LiveQA, MedicationQA, and MMLU), and the developer teams also created their own HealthSearchQA, using questions about medical conditions and the associated symptoms.

DeepResponse: Large Scale Prediction of Cancer Cell Line Drug Response with Deep Learning Based Pharmacogenomic Modelling

Cancer Drug Response prediction using a Recommender System (CaDRReS) is based on the matrix factorization approach to predict which drugs are sensitive for an unseen cell line. CaDRReS can also be used for studying drug response mechanisms including classes of drugs, subtypes of cell lines based on drug response profile, and drug-pathway associations.

Python implementation of TRANSACT, a versatile framework for subspace-based transfer learning and a tool to transfer non-linear predictors of drug response from model systems to tumors.

DEEPScreen: Virtual Screening with Deep Convolutional Neural Networks Using Compound Images