DISCOVERY OF NEW THERAPEUTIC MOLECULES USING ARTIFICIAL INTELLIGENCE AND COMPUTER MODELING

Résumé

The identification of potential therapeutic molecules is a critical stage in drug development, enabling the discovery of compounds that can selectively target disease-related biological systems. Advances in computer modeling, artificial intelligence (AI), and bioinformatics have accelerated this process, allowing researchers to predict the biological activity, toxicity, and pharmacokinetic properties of molecules before laboratory synthesis. This paper discusses the stages of potential therapeutic molecule identification, including target selection, virtual screening, molecular docking, laboratory testing, and chemical or biological synthesis, highlighting the importance of AI and computational tools in modern medicine and pharmaceutical sciences. The discovery of potential therapeutic molecules plays a pivotal role in modern medicine and pharmaceutical research. These molecules, which may include small chemical compounds, peptides, or biologically active proteins, interact with specific disease-related targets such as enzymes, receptors, or genes to modulate pathological processes. Recent technological advances, particularly in artificial intelligence (AI), machine learning, and big data analysis, have revolutionized the identification and optimization of such molecules. Computational techniques—such as virtual screening, molecular docking, and protein structure prediction—allow rapid assessment of molecular interactions, stability, solubility, and toxicity, reducing the time and cost associated with traditional experimental approaches. Moreover, integrating chemical and biological synthesis strategies ensures that promising molecules can be efficiently produced and further optimized for clinical applications.