Computational functional group mapping for drug discovery

Abstract

Computational functional group mapping (cFGM) is emerging as a high-impact complement to existing widely used experimental and computational structure-based drug discovery methods. cFGM provides comprehensive atomic-resolution 3D maps of the affinity of functional groups that can constitute drug-like molecules for a given target, typically a protein. These 3D maps can be intuitively and interactively visualized by medicinal chemists to rapidly design synthetically accessible ligands. Given that the maps can inform selection of functional groups for affinity, specificity, and pharmacokinetic properties, they are of utility for both the optimization of existing drug candidates and creating novel ones. Here, I review recent advances in cFGM with emphasis on the unique information content in the approach that offers the potential of broadly facilitating structure-based ligand design.

Perspectives towards antiviral drug discovery against Ebola virus

“…Another useful in silico method, particularly beneficial for lead optimization, is quantitative structure‐property relationship (QSPR) modeling which is useful for the identification of key structural features responsible for interacting with the target protein. For many ADME endpoints measured in the pharmaceutical industry, QSPR models have prospectively shown their ability to extract knowledge from a wide variety of chemical scaffolds proving their utility as predictive models . QSPR models, based on machine learning techniques, are desirable to achieve the optimal potency and ADME properties.…”

Descubrimiento de fármacos basado en imagenología de células vivas

“…Es posible implementar el diseño estructural computacional de compuestos como herramienta para determinar dicho equilibrio de interacción [23,29]. Esta herramienta predice la energía libre de unión a partir de la flexibilidad estructural entre el compuesto candidato y su diana cuando se da una interacción entre ambos.…”

“…Dentro de las consideraciones estructurales se encuentra que el tamaño molecular puede determinar el grado de interacción entre el candidato y el blanco [31]. Compuestos pequeños, con peso molecular <500 Da o menos de 20 átomos sin contar átomos de hidrógeno [32], presentan enlaces C-H que aportan significativamente a la energía libre de unión [30]; siempre y cuando estos enlaces pertenezcan a los grupos funcionales más próximos a la superficie del blanco terapéutico [29]; por lo tanto, a mayor energía libre de unión, mayor afinidad de unión entre candidato-blanco.…”

“…El área de superficie polar, medida en A o2 , es importante para la predicción de permeabilidad y solubilidad a partir de la polaridad; esta es asociada a la formación de puentes de hidrógeno para valores entre 50 y 100 A o2 , los cuales también contribuyen a la energía libre de unión [29,31]. Sin embargo, esta propiedad fisicoquímica no considera átomos electronegativos diferentes a oxígeno y nitrógeno; los cuales también aportan a la polaridad global del compuesto [33].…”

Defining a Global Map of Functional Group Based 3D Ligand-binding Motifs

“…Despite the functional and structural diversity of different protein-binding ligands, many of them share same or similar functional groups (FGs) that mediate the interactions with the target proteins. Therefore, identification of conserved 3D binding motifs for FGs shared by different small molecules may extend our understanding of protein-ligand interactions to higher resolution and broader scope (Guvench, 2016). Previous studies have shown that conserved 3D motifs do exist in proteins binding different ligands with the same FG.…”