Denoising Drug Discovery ADMET Data for Improved Regression Task Performance
Keywords: Denoising, noise filter, noise reduction, denoising for regression, ADMET prediction, drug discovery
TL;DR: We propose denoising schemes to reduce the noise in the drug discovery ADMET data and improve model predictions for regression tasks.
Abstract: Predicting ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of small molecules is a key task in drug discovery. A major challenge in building better ADMET models is the experimental error inherent in the data. Here, we develop denoising schemes based on deep learning to address this. The most significant performance increase occurs when the original model is finetuned with the denoised data using training error as the noise detection metric. Our denoising scheme outperforms other literature schemes for ADMET data and has implications for improving models for experimental assay data in general.
Primary Subject Area: Active learning, Data cleaning, acquisition for ML
Paper Type: Extended abstracts: up to 2 pages
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Submission Number: 25
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