Miquel Duran‐Frigola

Integration of diverse bioactivity data into the Chemical Checker compound universe

The paper introduces a protocol to integrate diverse bioactivity data into the Chemical Checker compound universe, enabling the generation of novel bioactivity signatures for small molecules.

Learning and actioning general principles of cancer cell drug sensitivity

The paper develops interpretable models to predict cancer drug sensitivity using transcriptomics data and enhances predictions with large language models, validated on clinical samples.

First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa

ZairaChem, an AI/ML tool, increases hit rates in drug discovery by 40% through automated virtual screening at a low-resource African research center.

Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque

The paper introduces Bioteque, a large-scale biomedical knowledge graph resource that provides pre-calculated descriptors for downstream machine learning tasks.

Identification and drug-induced reversion of molecular signatures of Alzheimers disease onset and progression in AppNL-G-F, AppNL-F and 3xTg-AD mouse models

The paper identifies molecular signatures of Alzheimer's disease in AD mouse models and finds drugs that reverse these signatures and cognitive impairment.

Bioactivity descriptors for uncharacterized compounds

The paper introduces deep neural networks to infer bioactivity signatures for uncharacterized compounds, improving activity prediction across various biological endpoints.

Extending the small molecule similarity principle to all levels of biology

The paper introduces Chemical Checker, a resource that integrates bioactivity data on 800,000 small molecules and demonstrates its use in drug discovery and disease modeling.

A reference map of the human protein interactome

The paper presents HuRI, a comprehensive human protein-protein interaction map, and demonstrates its utility in understanding cellular function and disease mechanisms.

Encircling the regions of the pharmacogenomic landscape that determine drug response

The paper develops a network-based method to identify gene modules associated with drug response in cancer cell lines, improving interpretability and reducing noise in transcriptomic signatures.