Integrating Multiview Information for Enhanced Deep Learning-Based Acute Dermal Toxicity Prediction
Wei Lin, Chi Chung Alan Fung
TL;DR
This paper introduces MVIToxNet, a deep learning model that improves acute dermal toxicity prediction by integrating multiview molecular data and using a weighted model averaging strategy.
Contribution
MVIToxNet is a novel model that integrates multiview features and uses weighted model averaging to enhance toxicity prediction.
Findings
MVIToxNet significantly outperforms existing baselines in acute dermal toxicity prediction.
The use of multiview features and weighted model averaging improves generalization on small and imbalanced datasets.
The proposed methods show potential for data-driven model design in toxicity prediction.
Abstract
Accurate prediction of acute dermal toxicity is vital for the safe and effective development of contact drugs. While numerous deep learning models have been created to replace costly and ethically challenging animal toxicity tests, most approaches overlook the multiview information on molecules. To overcome this limitation, we introduce a novel model named MVIToxNet, which integrates multiview features from both molecular fingerprints and SMILES sequences. To capture the multiview information on SMILES, MVIToxNet incorporates character-level and atom-level features. In addition, byte-pair encoding tokenization is utilized to capture substructural details within molecules, allowing the model to differentiate similar SMILES by assigning distinct tokens to different substructures. Since the data sets in this study are small and imbalanced, we argue that selecting a single model based…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsComputational Drug Discovery Methods · Cholinesterase and Neurodegenerative Diseases · Machine Learning in Bioinformatics