Denoising Drug Discovery Data for Improved Absorption, Distribution, Metabolism, Excretion, and Toxicity Property Prediction

Predicting
absorption, distribution, metabolism, excretion, and
toxicity (ADMET) 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. Furthermore, ADMET predictors
are typically regression tasks due to the continuous nature of the
data, which makes it difficult to apply existing denoising methods
from other domains as they largely focus on classification tasks.
Here, we develop denoising schemes based on deep learning to address
this. We find that the training error (TE) can be used to identify
the noise in regression tasks while ensemble-based and forgotten event-based
metrics fail to detect the noise. The most significant performance
increase occurs when the original model is finetuned with the denoised
data using TE as the noise detection metric. Our method has the ability
to improve models with medium noise and does not degrade the performance
of models with noise outside this range (low noise and high noise
regimes). To our knowledge, our denoising scheme is the first to improve
model performance for ADMET data and has implications for improving
models for experimental assay data in general.