Bissan Al‐Lazikani

PRIORITI uses language models to predict target-indication success in drug discovery, achieving high accuracy and robustness across different benchmarks.

The paper develops a chemoproteomics platform to map the human purine interactome and identifies a regioselective ligand for ACAT2 with potential therapeutic applications in cancer.

The paper develops TargetTrace, a resource linking drug targets to pharmacodynamic biomarkers, compiling 71,323 target-biomarker relationships for oncology drug development.

PocketBagger predicts druggable protein pockets using positive-unlabeled learning, achieving high recall and generalizability across protein families.

The paper uses machine learning to identify distinct biological processes and druggable targets in early-onset prostate cancer compared to late-onset prostate cancer.

This paper introduces a graph-based model to identify druggable vulnerabilities in solid cancers, finding 43 novel targets by leveraging cooperativity in molecular networks.

This study investigates Thio-2's mechanism of action in inhibiting AR signaling in CRPC, showing it acts independently of BAG-1 isoforms and with potential AR NTD binding.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

The paper introduces Probe Miner, a resource that objectively assesses chemical probes using public data to evaluate over 1.8 million compounds against 2,220 human targets.