Quantitative Prediction of Hemolytic Toxicity for Small Molecules and Their Potential Hemolytic Fragments by Machine Learning and Recursive Fragmentation Methods

Hemolytic toxicity,
as one of the key toxicity endpoints for small
molecules, can cause lysis of the erythrocyte membrane and subsequent
release of hemoglobin into blood plasma, leading to multiple acute
and chronic adverse effects. Hence, it is necessary to assess the
hemolytic toxicity of small molecules in an early stage of drug discovery
and development process, and it is more significant to quantitatively
predict the hemolytic toxicity of small molecules before costly and
time-consuming experiments. Nevertheless, this endpoint has never
been quantitatively predicted due to the lack of an appropriate dataset.
In this work, we manually collected a quantitative hemolytic toxicity
dataset containing 805 small molecules with experimental values of
HD50 (50% hemolytic dose) from a variety of literature,
built the first machine learning-based regression model to quantitatively
predict the hemolytic toxicity of small molecules, and developed a
pragmatic software for automatic prediction. Based on this model,
we further implemented an automatic recursive fragmentation module
to predict the hemolytic fragments with high fragment efficiency for
the given compound(s), which may be of particular interest to experimental
medicinal chemists. Therefore, we anticipate that this quantitative
model may help medicinal chemists boost the development of promising
lead compounds with low hemolytic toxicity or fuel the discovery of
highly hemolytic chemical probes to delve into the in-depth mechanism
of the hemolytic process.