Low Data Drug Discovery with One-Shot Learning
Open accessAbstract
Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. MODEL: 2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016).
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Networks trained on Tox21 data were tested on SIDER, but none of the one-shot networks achieved any predictive power, highlighting a potential limitation in translation from toxic in vitro assays into the human clinic.
“networks trained on Tox21 data were tested on SIDER, however none of the one-shot networks achieved any predictive power, highlighting the potential limitation in translation from toxic in vitro assays into the human clinic.”
From Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2018) · cites this paper as [51]
Iterative Refinement LSTMs outperformed other models on most Tox21 assays and SIDER side effect.
“Iterative Refinement LSTMs outperformed other models on most of the Tox21 assays and SIDER side effect.”
From Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2018) · cites this paper as [51]
DNNs have rarely been used for prospective prediction, and this is a limitation of many machine learning drug discovery studies published.
“but to date this algorithm has rarely been used for prospective prediction and in some respects this is a limitation of many of the machine learning drug discovery studies published.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [32]
Recent efforts have broadened applicability of supervised AI/ML models to low-data and no-data scenarios, i.e., under-studied and unliganded proteins.
“Indeed, recent efforts have broadened the applicability of supervised AI/ML models to low-data and no-data scenarios,112,113 i.e. to under-studied and unliganded proteins.”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [112]
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- Authors
- Altae-Tran, H., Ramsundar, B., Pappu, A. S., & Pande, V.
- Affiliations
- Massachusetts Institute of Technology (US)
- Stanford University (US)
- Corresponding author
- Pande, Vijay
- Venue
- ACS Central Science
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-04-03
- Volume
- 3
- Issue
- 4
- Pages
- 283-293
- ISSN
- 2374-7943 (ISSN-L)
- 2374-7951
- PMC
- PMC5408335
- OpenAlex
- W2565684601
- Cited by
- 799
- References
- 27
- Citations by year
- 2026: 58 · 2025: 90 · 2024: 83 · 2023: 110 · 2022: 102 · 2021: 103 · 2020: 96 · 2019: 77 · 2018: 60 · 2017: 20
- FWCI
- 51.44
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- ACS Central Science (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Hertz Foundation
- 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
- one-shot learning, drug discovery, graph convolutional networks, distance measures, DeepChem
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