Manuela J Vanegas,¹ Sara Gómez,²* Chiara Cappelli³ and Gian Pietro Miscione¹*
A. Milne,¹ A. Lénard²
¹ COBO, Computational Bio-Organic Chemistry, Departamento de Química, Universidad de los Andes, Carrera 1 18A-12, Bogotá, 111711, Colombia
² Universidad Nacional de Colombia, Departamento de Química, Av. Cra 30 45-03, 111321, Bogotá, Colombia
³ Scuola Normale Superiore, Classe di Scienze, Piazza dei Cavalieri 7, 56126, Pisa, Italy
Corresponding gp.miscione57@uniandes.edu.co, sagomezam@unal.edu.co
Abstract:
Computational chemistry has revolutionized drug discovery, providing robust frameworks for understanding complex molecular interactions and facilitating the rational design of therapeutic compounds. This talk will explore the applications of computational techniques, such as molecular docking, molecular dynamics (MD), and free energy perturbation (FEP) in drug design, as well as the growing role of artificial intelligence (AI) in this field.
Taking advantage of these methods, researchers can explore uncharted territories and, particularly, predict binding affinities, optimize pharmacokinetic profiles, and design molecules with high therapeutic potential.
A key example of this approach is the study of cholesterol (CHOL) interactions with the CB1 receptor, a G protein-coupled receptor (GPCR) central to the endocannabinoid system. Using atomistic MD simulations, recent investigations have identified specific CHOL binding sites on CB1 and evaluated their residence times, offering insights into how CHOL may act as an endogenous allosteric modulator. Quantum mechanical analyses, such as Natural Bond Orbitals (NBO) and Quantum Theory of Atoms in Molecules (QTAIM), further reveal the strength and nature of CHOL interactions, primarily via hydrogen bonding and hydrophobic contacts. These findings provide a foundation for designing novel CB1 ligands and highlight how computational approaches can predict ligand residence times, informing the design of more effective therapeutics.
Keywords: Artificial intelligence, molecular simulation, drug discovery, CB1 receptor, QM/MM
Suggested Reading:
- Conflitti, P.; Raniolo, S.; Limongelli, V. J. Chem. Theory Comput. 2023, 19, 18, 6047–6061.
- Bassani, D.; Moro, S. Molecules 2023, 28, 3906-3926.
- Basavarajappa, B. S.; Shivakumar, M.; Joshi, V.; Subbanna, S. Journal of Neurochemistry, 2017, 142, 624–648.
Virtual Winter School on Computational Chemistry