PLASMOpred is a machine learning-based web application which aids in identifying potential inhibitors targeting the AMA-1/RON2 invasion complex. This interaction is crucial for tight junction formation, a key step in red blood cell invasion by Plasmodium falciparum merozoites. PLASMOpred facilitates the discovery of potential novel anti-malarial compounds. The robust machine learning models include Random Forest, Gradient Boost Machines and CatBoost. PLASMOpred was trained using data obtained from qHTS for inhibitors of AMA1-RON (PubChem AID 720542).