Machine learning
(ML) models to predict the toxicity of small molecules
have garnered great attention and have become widely used in recent
years. Computational toxicity prediction is particularly advantageous
in the early stages of drug discovery in order to filter out molecules
with high probability of failing in clinical trials. This has been
helped by the increase in the number of large toxicology databases
available. However, being an area of recent application, a greater
understanding of the scope and applicability of ML methods is still
necessary. There are various kinds of toxic end points that have been
predicted in silico. Acute oral toxicity, hepatotoxicity,
cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are
among the most commonly investigated. Machine learning methods exhibit
different performances on different data sets due to dissimilar complexity,
class distributions, or chemical space covered, which makes it hard
to compare the performance of algorithms over different toxic end
points. The general pipeline to predict toxicity using ML has already
been analyzed in various reviews. In this contribution, we focus on
the recent progress in the area and the outstanding challenges, making
a detailed description of the state-of-the-art models implemented
for each toxic end point. The type of molecular representation, the
algorithm, and the evaluation metric used in each research work are
explained and analyzed. A detailed description of end points that
are usually predicted, their clinical relevance, the available databases,
and the challenges they bring to the field are also highlighted.
The new methodology named Pose/Ranking Consensus (PRC) combines both pose and ranking consensus strategies. It displays an enhanced performance in terms of enrichment factor and hit rate, ensuring the recovery of a suitable number of ligands.
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