Prediction of antimicrobial peptides using machine learning classifiers

I created machine learning classifiers to predict the antimicrobial activity of peptides. It was an end-to-end project in which I did the data pre-processing, feature engineering, training, hyperparameter tuning, and deployment of the best model as a web application with Streamlit.

Antiparasitic peptides discovery

This project was about the exploration of the antiparasitic peptides’ chemical space and prediction of new potential compounds through a new approach based on network science and similarity searching.

Exploring the Chemical Space of Antiparasitic Peptides and Discovery of New Promising Leads through a Novel Approach based on Network Science and Similarity Searching

Oral presentation in English about the Antiparasitic peptides discovery project.

Exploring the Chemical Space of Antiparasitic Peptides and Discovery of New Promising Leads through a Novel Approach based on Network Science and Similarity Searching

Oral presentation in English about the Antiparasitic peptides discovery project.

Prediction of potential drug ligands that bind to Beta-Lactamases

In this project, I created machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) to evaluate the activity of molecules that have been experimentally tested to bind or not bind to Beta-Lactamases. Also, I implemented a web service with the best model.