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Published on: January 26, 2024
Deep active learning with high structural discriminability for molecular mutagenicity prediction
Huiyan Xu1,2, Yanpeng Zhao2, Yixin Zhang2
1Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China.
Abstract:
The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecules. However, experimental mutagenicity testing can be time-consuming and costly. One solution to reduce the annotation cost is active learning, where the algorithm actively selects the most valuable molecules from a vast chemical space and presents them to the oracle (e.g., a human expert) for annotation, thereby rapidly improving the model's predictive performance with a smaller annotation cost. In this paper, we propose muTOX-AL, a deep active learning framework, which can actively explore the chemical space and identify the most valuable molecules, resulting in competitive performance with a small number of labeled samples. The experimental results show that, compared to the random sampling strategy, muTOX-AL can reduce the number of training molecules by about 57%. Additionally, muTOX-AL exhibits outstanding molecular structural discriminability, allowing it to pick molecules with high structural similarity but opposite properties.
Insights
Predicting mutagenicity is crucial for drug safety. A new active learning framework, muTOX-AL, efficiently identifies key molecules for testing, significantly reducing costs and improving accuracy in drug discovery.
Area of Science:
- Computational chemistry
- Toxicology
- Drug discovery
Background:
- Mutagenicity assessment is vital in drug discovery to prevent cancer and germ cell damage.
- In silico mutagenicity prediction is hampered by limited labeled molecular data.
- Experimental testing is costly and time-consuming, necessitating cost-effective annotation strategies.
Purpose of the Study:
- To introduce muTOX-AL, a deep active learning framework for efficient mutagenicity prediction.
- To reduce the cost of molecular annotation in drug discovery.
- To enhance the performance of in silico mutagenicity prediction models with limited data.
Main Methods:
- Development of a deep active learning framework (muTOX-AL).
- Active exploration of chemical space to identify valuable molecules for annotation.
- Utilizing an oracle (e.g., human expert) for targeted molecular labeling.
Main Results:
- muTOX-AL achieved competitive performance with a small number of labeled samples.
- Reduced the number of required training molecules by approximately 57% compared to random sampling.
- Demonstrated superior ability to select molecules with high structural similarity but differing mutagenic properties.
Conclusions:
- muTOX-AL offers an efficient solution for mutagenicity assessment in drug discovery.
- The framework significantly lowers annotation costs while maintaining high predictive performance.
- muTOX-AL's structural discriminability aids in identifying critical molecular features for toxicity prediction.
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