Molecular machine learning with DeepChem
Abstract/Contents
- Abstract
- Machine learning has widely been applied to image, video, and speech datasets, but has not yet achieved broad penetration into chemistry, materials science, or other molecular design applications. However, over the last few years, machine learning and deep learning have achieved notable successes in predicting properties of molecular systems. In this thesis, I present a series of deep learning algorithms that demonstrate strong predictive improvements across a wide range of biochemical tasks such as assay activity modeling, toxicity prediction, protein-ligand binding affinity calculation, and chemical retrosynthesis. In addition to these algorithmic improvements, I introduce the comprehensive benchmark suite MoleculeNet for molecular machine learning algorithms (https://moleculenet.ai) and demonstrate how the technology of one-shot learning can be used for drug discovery applications. The work presented in this thesis culminated in my design and construction of DeepChem (https://deepchem.io), an open source package for molecular machine learning, which has achieved broad adoption among biotech startups, pharmaceutical companies, and research groups. DeepChem has attracted a thriving community of open source developers and looks to continue growing and expanding as a vibrant research tool.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Ramsundar, Bharath |
|---|---|
| Associated with | Stanford University, Computer Science Department. |
| Primary advisor | Guibas, Leonidas J |
| Thesis advisor | Guibas, Leonidas J |
| Thesis advisor | Dror, Ron, 1975- |
| Thesis advisor | Kundaje, Anshul, 1980- |
| Advisor | Dror, Ron, 1975- |
| Advisor | Kundaje, Anshul, 1980- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Bharath Ramsundar. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Bharath Ramsundar
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...