Finding needles in a haystack : molecular similarity and machine learning for drug discovery applications
- We are in the midst of a machine learning revolution. From self-driving cars to clinical diagnostics, machine learning promises to change the way we live our lives and make decisions. Applied to drug discovery, machine learning enables us to build upon existing experimental data and more effectively explore the vastness of chemical space for new therapeutics. In particular, virtual screening allows us to evaluate many more compounds and biological targets than we can test experimentally, helping to identify starting points for further development. In this dissertation, I present several applications of machine learning to drug discovery. Much of the work presented here focuses on multitask neural networks---variants of the models that have transformed computer vision and beaten some of the world's best Go players. Applications of these models to ligand-based virtual screening demonstrate improvements over standard machine learning methods such as random forest and logistic regression. I also describe neural network models built on simple encodings of the molecular graph, moving beyond traditional fingerprint-based screening methods to a richer and more flexible input representation.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Kearnes, Steven Michael
|Stanford University, Department of Structural Biology.
|Statement of responsibility
|Steven Michael Kearnes.
|Submitted to the Department of Structural Biology.
|Thesis (Ph.D.)--Stanford University, 2016.
- © 2016 by Steven Michael Kearnes
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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