Author

Paul‐Michael Agapow

Imperial College LondonORCID 0000-0003-1126-1479Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:drug response / drug discovery· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Assay:ChIP-seq· 1 Approach:deep learning· 1 Assay:electronic health records· 1 Assay:imaging· 1 Organism:multiple species· 1

Frequent Co-Authors

Alexandr A. KalininiD· 1 shared Amr M. AlexandariiD· 1 shared Anne Elizabeth CarpenteriD· 1 shared Anshul Bharat KundajeiD· 1 shared Anthony GitteriD· 1 shared Austin HuangiD· 1 shared Avanti ShrikumariD· 1 shared Benjamin J. LengerichiD· 1 shared

Activity by Year

2018-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Alexandr A. KalininORCID

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2 papers

Recent·Most cited

NeurosciencebioRxivPreprintMay 26, 2026

From APOE Genetics to AI-Designed Drug Candidates: An Integrated Pipeline for Oral, Brain-Penetrant ACAT1 Inhibitors in Alzheimer's Disease

Sharad Agarwal, Rory Popert, Paul‐Michael Agapow…·Shikta Gupta

This paper presents an AI-driven drug discovery platform for designing oral, brain-penetrant ACAT1 inhibitors for Alzheimer's disease, identifying promising lead candidates with favorable properties.

DOI 10.64898/2026.05.21.727002

humanmachine learning (classical)

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BioinformaticsJournal of The Royal Society InterfacePeer-reviewedApr 1, 2018

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Opportunities And Obstacles For Deep Learning In Biology And Medicine

Travers H. Ching, Daniel Himmelstein, Brett K. Beaulieu‐Jones…·Casey S. Greene

Deep learning rivals or beats baselines across imaging, health records, genomics and drug discovery but is not yet transformative; scarce labels, privacy and opacity remain hurdles.

2,300 citationstop 1%

humanmultiple species

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