Daniel Ruyardi Restrepo Barbosa,¹ and Gian Pietro Miscione¹*
¹ COBO, Computational Bio-Organic Chemistry, Departamento de Química, Universidad de los Andes, Carrera 1 18A-12, Bogotá, 111711, Colombia
Corresponding gp.miscione57@uniandes.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 the identification of possible, selective inhibitors of the acetylcholinesterase enzyme (AChE) from Drosophila melanogaster (fruit fly) with higher affinity for this variant compared to the human enzyme, as a less toxic alternative to commercial insecticides. Four ligands, selected through virtual screening, were computationally evaluated for their interactions with three AChE variants: human, Drosophila melanogaster and electric eel, the latter serving as an experimental reference. The methodology combined tools such as docking, molecular dynamics, and free energy perturbation (FEP) calculations to estimate binding free energy values (Δ𝐺_binding) and identify key interactions between the ligands and enzymes. These analyses contribute to the development of more selective and safer inhibitors for potential application as insecticides.
Keywords: Free Energy Perturbation, molecular simulations, drug discovery, Acetylcholinesterase
Suggested Reading:
[1] Abel R., Wang L., Harder E. D., Berne B. J., Friesner R. A. Acc. Chem. Res. 2017, 50, 1625−1632
[2] Hollingsworth, S. A, Dror, R. O. Neuron 2018, 99, 1129-1143
[3] Zhang C-H., Stone E. A., Deshmukh M., Ippolito J.A., Ghahremanpour M. M., Julian Tirado-Rives J., Spasov K.A., Zhang S., Takeo Y., Kudalkar S.N., Liang Z., Isaacs F., Lindenbach B., Miller S. J., Anderson K. S., Jorgensen W.L. ACS Cent. Sci. 2021, 7, 467−475
Virtual Winter School on Computational Chemistry