ArticleScience advances2024
Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Advances in high-throughput drug screening based on pharmacotranscriptomics.Journal of advanced research · 2026Review
- Steering semi-flexible molecular diffusion model for structure-based drug design with reinforcement learning.Science advances · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Nitidine chloride suppresses polo-like kinase 1 via MYCN-associated transcriptional regulation in colorectal cancer: a multi-omics and spatial transcriptomics study.Frontiers in oncology · 2026Article
- molIEreVIS: exploring and interpreting the evidence behind drug repurposing predictions.Frontiers in bioinformatics · 2026Article
- Nitidine chloride inhibits colorectal cancer by targeting BUB1: mechanistic insights from molecular dynamics simulation, spatial transcriptomics, and single-cell RNA sequencing.BMC gastroenterology · 2025Article
- Some Aspects and Convergence of Human and Veterinary Drug Repositioning.Molecules (Basel, Switzerland) · 2024Review
- Fluoxetine as an anti-inflammatory therapy in SARS-CoV-2 infection.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2021Article
- Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
We present an in silico approach for drug discovery, dubbed connectivity enhanced structure activity relationship (ceSAR). Building on the landmark LINCS library of transcriptional signatures of drug-like molecules and gene knockdowns, ceSAR combines cheminformatic techniques with signature concordance analysis to connect small molecules and their targets and further assess their biophysical compatibility using molecular docking. Candidate compounds are first ranked in a target structure-independent manner, using chemical similarity to LINCS analogs that exhibit transcriptomic concordance with a target gene knockdown. Top candidates are subsequently rescored using docking simulations and machine learning-based consensus of the two approaches. Using extensive benchmarking, we show that ceSAR greatly reduces false-positive rates, while cutting run times by multiple orders of magnitude and further democratizing drug discovery pipelines. We further demonstrate the utility of ceSAR by identifying and experimentally validating inhibitors of BCL2A1, an important antiapoptotic target in melanoma and preterm birth-associated inflammation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.