Author

A. M. Hayward

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 2 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Approach:machine learning (classical)· 2 Approach:experimental assay· 1 Organism:mouse· 1

Frequent Co-Authors

G. Traverso· 3 shared K. Ishida· 3 shared A. Lopes· 2 shared D. Reker· 2 shared K. Hess· 2 shared A. A. Alshareef· 1 shared A. Gardner· 1 shared A. Hwang· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersBioengineering

G. Traverso

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A Vibrating Ingestible BioElectronic Stimulator Modulates Gastric Stretch Receptors for Illusory Satiety

S. Srinivasan, A. A. Alshareef, A. Hwang, C. Bryne, J. Kuosmann, K. Ishida, J. Jenkins, S. Liu, W. A. M. Madani, A. M. Hayward, N. Fabian, G. Traverso

The paper develops an ingestible device that activates gastric stretch receptors to reduce food intake and weight gain in swine.

bioRxiv · Jul 17, 2023Pathology

A machine learning liver-on-a-chip system for safer drug formulation

Y. Shi, C.-H. Lin, D. Reker, C. Steiger, K. Hess, J. E. Collins, S. Tamang, K. Ishida, A. Lopes, J. Wainer, A. M. Hayward, C. Walesky, W. Goessling, G. Traverso

This paper develops a liver-on-a-chip system combined with machine learning to model drug metabolism and identify excipients that reduce hepatotoxicity in vivo.

bioRxiv · Sep 5, 2022 · PreprintBioengineering

Computationally guided high-throughput design of self-assembling drug nanoparticles

D. Reker, Y. Rybakova, A. R. Kirtane, R. Cao, J. W. Yang, N. Navamajiti, A. Gardner, R. M. Zhang, T. Esfandiary, J. L'Heureux, T. von Erlach, E. M. Smekalova, D. Leboeuf, K. Hess, A. Lopes, , G. Traverso

This paper develops a high-throughput screening platform with machine learning to design self-assembling drug nanoparticles, identifying 101 novel formulations from 2.1 million combinations.

bioRxiv · Sep 30, 2019Bioengineering