Artificial intelligence methods for molecular property prediction
- Evan N. Feinberg.
- [Stanford, California] : [Stanford University], 2018.
- Copyright notice
- Physical description
- 1 online resource.
Also available at
At the library
Limited on-site access
Researchers in the Stanford community can request to view these materials in the Special Collections Reading Room. Entry to the Reading Room is by appointment only.
|3781 2018 F||In-library use|
- This dissertation covers work discussed in the following papers: "Spatial Graph Convolutions for Drug Discovery" describes new deep neural network architectures for modeling drug-receptor interactions. We argue that the future of predicting the interactions between a drug and its prospective target demands more than simply applying deep learning algorithms from other domains, like vision and natural language, to molecules. "Machine Learning Harnesses Molecular Dynamics to Discover New Opioid Chemotypes" describes an algorithm that leverages protein motion to enrich the search for active molecules. We then applied the method to find a new chemical scaffold that we experimentally verified is an agonist for the μ Opioid Receptor. "Kinetic Machine Learning Unravels Ligand-Directed Conformational Change of Opioid Receptor" describes differential pathways of deactivation and differential conformational states sampled by the μ Opioid Receptor in response to different opioid ligands.
- Publication date
- Copyright date
- Submitted to the Biophysics Program.
- Thesis Ph.D. Stanford University 2018.
Browse related items
Start at call number: