Michael J. Keiser, Ph.D.

Affiliations: 
2009 Biological and Medical Informatics University of California, San Francisco, San Francisco, CA 
Area:
GPCR, computational chemistry
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Brian K. Shoichet grad student 2009 UCSF
 (Relating protein pharmacology by ligand chemistry.)
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Connell W, Garcia K, Goodarzi H, et al. (2023) Learning chemical sensitivity reveals mechanisms of cellular response. Biorxiv : the Preprint Server For Biology
Fassio AV, Shub L, Ponzoni L, et al. (2022) Prioritizing Virtual Screening with Interpretable Interaction Fingerprints. Journal of Chemical Information and Modeling. 62: 4300-4318
Cáceres EL, Mew NC, Keiser MJ. (2020) Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction. Journal of Chemical Information and Modeling
Chuang KV, Gunsalus L, Keiser MJ. (2020) Learning Molecular Representations for Medicinal Chemistry. Journal of Medicinal Chemistry
McCarroll MN, Gendelev L, Kinser R, et al. (2019) Zebrafish behavioural profiling identifies GABA and serotonin receptor ligands related to sedation and paradoxical excitation. Nature Communications. 10: 4078
Tang Z, Chuang KV, DeCarli C, et al. (2019) Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. Nature Communications. 10: 2173
Chuang KV, Keiser MJ. (2018) Comment on "Predicting reaction performance in C-N cross-coupling using machine learning". Science (New York, N.Y.). 362
Chuang KV, Keiser MJ. (2018) Adversarial Controls for Scientific Machine Learning. Acs Chemical Biology. 13: 2819-2821
Willsey AJ, Morris MT, Wang S, et al. (2018) The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders. Cell. 174: 505-520
Irwin JJ, Gaskins G, Sterling T, et al. (2017) Predicted Biological Activity of Purchasable Chemical Space. Journal of Chemical Information and Modeling
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