A SOFTWARE SYSTEM FOR PREDICTING AND RESEARCHING ANTIFUNGAL ANTIBIOTICS’ PROPERTIES
In this work we present a software system that enables antifungal antibiotic drug candidate toxicity and likelihood of drug binding prediction. The system is composed of a number of machine learning models and deterministic algorithms. Its implementation utilizes modern software development practices including a client-server architecture with a thin web-client. Testing showed the models’ accuracy and viability for predicting antifungal antibiotics’ properties.