AI-Driven Drug Discovery Platform

Abstract

The process of drug discovery using traditional methods is time-consuming, expensive, and often inefficient in meeting urgent medical needs. This study presents an AI-driven drug discovery platform designed to assist healthcare professionals in identifying effective treatments quickly and accurately. The primary aim of this research is to reduce the time required for drug identification and improve decision-making in medical applications. The proposed system utilizes Artificial Intelligence techniques to analyze disease inputs and recommend the top three suitable drugs based on key parameters such as absorption and toxicity. In addition, the platform provides insights into possible deficiencies in the body that may lead to specific diseases. It also offers dosage recommendations tailored to different age groups, including infants, adults, and the elderly. Furthermore, the system evaluates the probability of side effects and presents detailed drug information, including physicochemical properties and 2D/3D structural representations. The model was trained and tested on relevant datasets, achieving an accuracy of 95%, demonstrating its effectiveness compared to conventional approaches. The results indicate that the system can significantly enhance the efficiency and reliability of drug discovery and recommendation processes. In conclusion, the proposed platform serves as a comprehensive and intelligent tool for supporting medical professionals, enabling faster diagnosis support, personalized treatment planning, and improved patient outcomes.