Andrew R. Leach

The paper uses Mendelian Randomization to predict adverse drug reactions by linking genetic variants to gene expression and protein abundance across large datasets.

This study systematically analyzes chemical probe literature to identify target-disease associations, revealing their utility in early therapeutic discovery and highlighting opportunities for repurposing.

RP3Net predicts recombinant protein production in E. coli using deep learning, achieving 77% accurate predictions and 92% correct identification of expressing constructs.

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

EnsembleFlex provides a tool for dual-scale flexibility analysis of protein structure ensembles, combining experimental and predictive methods for backbone and side-chain dynamics.

The paper identifies 995 genes with shared genetic links to age-related diseases using GWAS data and presents them in a web tool for drug target prioritization.

This study identifies novel genetic risk variants and potential drug targets for heart failure using GWAS and Mendelian randomization proteomics.

The paper prioritizes 3.7 million patents to identify those likely containing bioactivity data for understudied targets, aiming to curate them for the ChEMBL database.

This study integrates drug sensitivity data with CRISPR screens to identify drug mechanisms and biomarkers for cancer therapy.

This paper compares genomes of 81 nematode and platyhelminth species to identify gene families related to parasitism and prioritize new drug targets.