In Silico Prediction of Hemolytic Toxicity on the Human Erythrocytes for Small Molecules by Machine-Learning and Genetic Algorithm

Hemolytic toxicity of small molecules,
as one of the important
ADMET end points, can cause the lysis of erythrocytes membrane and
leaking of hemoglobin into the blood plasma, which leads to various
side effects. Thus, it is very crucial to assess the hemolytic potential
of small molecules during the early stage of drug development process.
However, so far there is no computational model to predict the human
hemolytic toxicity of small molecules. To this end, we manually curate
the hemolytic toxicity data set for the small molecules experimentally
evaluated on the human erythrocytes, develop the first machine-learning
(ML) based models to predict the human hemolytic toxicity of small
molecules, harness the genetic algorithm (GA) and ML based model to
optimize human hemolytic toxicity based on the molecular fingerprint
to derive “optimal virtual fingerprints (OVFs)” with
the desired hemolytic/nonhemolytic property, and finally implement
a free software for the users to predict/optimize the human hemolytic
toxicity with ML and GA in the automatic manner.