ARTIFICIAL INTELLIGENCE IN THE DISCOVERY OF NEW DRUGS: APPLICATIONS AND PERSPECTIVES FOR PHARMACEUTICAL RESEARCH

Abstract

Artificial Intelligence (AI) has significantly transformed drug discovery by enabling the analysis of large-scale chemical, biological, and clinical data with greater accuracy and efficiency. Techniques such as machine learning, deep learning, and generative neural networks are applied in QSAR modeling, virtual screening, toxicity prediction, and de novo molecular design. These approaches enhance pharmaceutical pipeline efficiency, reduce costs, and minimize failures in preclinical and clinical phases. ADMET prediction models allow early identification of pharmacokinetic and toxicological issues, improving safety and sustainability. In Brazil, particularly in the Amazon region, AI represents a strategic opportunity to integrate biodiversity with technological innovation. Its application can accelerate the screening of bioactive compounds derived from natural resources, strengthening regional research and national scientific competitiveness. However, challenges related to computational infrastructure, technical training, and interdisciplinary integration remain. The consolidation of AI in Brazilian pharmaceutical research depends on investment in education, institutional collaboration, and sustainable bioeconomy policies.