Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods
| Autor: | Eugene N. Muratov, Sankalp Jain, Marc C. Nicklaus, Nicole Kleinstreuer, Alexey V. Zakharov, Vishal B. Siramshetty, Anton Simeonov, Alexander Tropsha, Vinicius M. Alves |
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| Rok vydání: | 2021 |
| Předmět: |
Consensus
Databases Factual Computer science General Chemical Engineering Multi-task learning Library and Information Sciences Machine learning computer.software_genre 01 natural sciences Convolutional neural network Article Deep Learning 0103 physical sciences 010304 chemical physics business.industry Deep learning General Chemistry 0104 chemical sciences Computer Science Applications Random forest Data set 010404 medicinal & biomolecular chemistry Graph (abstract data type) Neural Networks Computer Artificial intelligence business computer Scale model Predictive modelling |
| Zdroj: | J Chem Inf Model |
| ISSN: | 1549-960X 1549-9596 |
| DOI: | 10.1021/acs.jcim.0c01164 |
| Popis: | Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple single- and multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications. |
| Databáze: | OpenAIRE |
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