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.