  There are numerous peptides discovered through past decades, which exhibit
antimicrobial and anti-cancerous tendencies. Due to these reasons, peptides are
supposed to be sound therapeutic candidates. Some peptides can pose low
metabolic stability, high toxicity and high hemolity of peptides. This
highlights the importance for evaluating hemolytic tendencies and toxicity of
peptides, before using them for therapeutics. Traditional methods for
evaluation of toxicity of peptides can be time-consuming and costly. In this
study, we have extracted peptides data (Hemo-DB) from Database of Antimicrobial
Activity and Structure of Peptides (DBAASP) based on certain hemolity criteria
and we present a machine learning based method for prediction of hemolytic
tendencies of peptides (i.e. Hemolytic or Non-Hemolytic). Our model offers
significant improvement on hemolity prediction benchmarks. we also propose a
reliable clustering-based train-tests splitting method which ensures that no
peptide in train set is more than 40% similar to any peptide in test set. Using
this train-test split, we can get reliable estimated of expected model
performance on unseen data distribution or newly discovered peptides. Our model
tests 0.9986 AUC-ROC (Area Under Receiver Operating Curve) and 97.79% Accuracy
on test set of Hemo-DB using traditional random train-test splitting method.
Moreover, our model tests AUC-ROC of 0.997 and Accuracy of 97.58% while using
clustering-based train-test data split. Furthermore, we check our model on an
unseen data distribution (at Hemo-PI 3) and we recorded 0.8726 AUC-ROC and
79.5% accuracy. Using the proposed method, potential therapeutic peptides can
be screened, which may further in therapeutics and get reliable predictions for
unseen amino acids distribution of peptides and newly discovered peptides.
