Is Multitask Deep Learning Practical for Pharma?
Abstract
Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.
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DNNs can model more than one activity at a time (multi-task models).
“They can straightforwardly model more than one activity at a time (multi-task models);68”
From QSAR without borders (2020) · cites this paper as [68]
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- Authors
- Ramsundar, B., Liu, B., Wu, Z., Verras, A., Tudor, M., Sheridan, R. P., & Pande, V.
- Affiliations
- Stanford University (US)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Corresponding author
- Pande, Vijay
- ORCID
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-08-01
- Volume
- 57
- Issue
- 8
- Pages
- 2068-2076
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2734982589
- Cited by
- 263
- References
- 17
- Citations by year
- 2026: 17 · 2025: 22 · 2024: 20 · 2023: 23 · 2022: 28 · 2021: 49 · 2020: 36 · 2019: 37 · 2018: 26 · 2017: 5
- FWCI
- 21.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Funders
- National Institute of Allergy and Infectious Diseases (U19 AI109662)
- Hertz Foundation
- National Institute of General Medical Sciences (R01 GM062868)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- multi-task deep learning, computational drug discovery, DeepChem, open source software, model robustness, pharmaceutical data
- MeSH terms
- Machine Learning, Software, Ultraviolet Rays, Serine Proteinase Inhibitors, Inhibitory Concentration 50, Protein Kinase Inhibitors, Drug Discovery, Absorption, Radiation
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