  Chemical toxicity prediction using machine learning is important in drug
development to reduce repeated animal and human testing, thus saving cost and
time. It is highly recommended that the predictions of computational toxicology
models are mechanistically explainable. Current state of the art machine
learning classifiers are based on deep neural networks, which tend to be
complex and harder to interpret. In this paper, we apply a recently developed
method named contrastive explanations method (CEM) to explain why a chemical or
molecule is predicted to be toxic or not. In contrast to popular methods that
provide explanations based on what features are present in the molecule, the
CEM provides additional explanation on what features are missing from the
molecule that is crucial for the prediction, known as the pertinent negative.
The CEM does this by optimizing for the minimum perturbation to the model using
a projected fast iterative shrinkage-thresholding algorithm (FISTA). We
verified that the explanation from CEM matches known toxicophores and findings
from other work.

Quantitative Methods;Artificial Intelligence