Michael Keiser

Associate Professor

University of California, San Francisco

United States

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Michael Keiser is an Associate Professor at the University of California San Francisco, USA. His research focuses on machine learning applications in molecular docking, cryo-EM structures, and enzymology. He has contributed to various studies on deep learning in chemical sensitivity and drug profiling, including advancements in ligand design and chromatin organization analysis.

Articles (17)

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2025

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2020

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2020

Collaborators (7)

Matthew O'Meara

Assistant Professor

University of Michigan

UNITED STATES

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Michelle R. Arkin

University of California

UNITED STATES

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Matthew McCarroll

Professional Researcher

University of California, San Francisco

UNITED STATES

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Charles White

Professor

UNITED STATES

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Elena Caceres

University of California, San Francisco

UNITED STATES

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Maria L. Wei

University of California, San Francisco

UNITED STATES

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Hani Goodarzi

Associate Professor

University of California, San Francisco

UNITED STATES

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Michael Keiser

Research Interests

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Articles (17)

Autoparty: Machine Learning-Guided Visual Inspection of Molecular Docking Results

MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures

Evolutionary-scale enzymology enables exploration of a rugged catalytic landscape

Fuzz Testing Molecular Representation Using Deep Variational Anomaly Generation

ChromaFactor: Deconvolution of single-molecule chromatin organization with non-negative matrix factorization

Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks

Learning chemical sensitivity reveals mechanisms of cellular response

Deep phenotypic profiling of neuroactive drugs in larval zebrafish

Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds

Autoregressive fragment-based diffusion for pocket-aware ligand design

Prioritizing Virtual Screening with Interpretable Interaction Fingerprints

Deep learning from multiple experts improves identification of amyloid neuropathologies.

Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models.

Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction

Learning Molecular Representations for Medicinal Chemistry

Artificial Intelligence in Dermatology: A Primer

Validation of machine learning models to detect amyloid pathologies across institutions.

Collaborators (7)

Matthew O'Meara

Michelle R. Arkin

Matthew McCarroll

Charles White

Elena Caceres

Maria L. Wei

Hani Goodarzi

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