Predicting blood-brain barrier (BBB) permeation remains a challenge in drug design. Since it is impossible to determine experimentally the BBB partitioning of large numbers of preclinical candidates, alternative evaluation methods based on computerized models are desirable. The present study was conducted to demonstrate the value of descriptors derived from 3D molecular fields in estimating the BBB permeation of a large set of compounds and to produce a simple mathematical model suitable for external prediction. The method used (VolSurf) transforms 3D fields into descriptors and correlates them to the experimental permeation by a discriminant partial least squares procedure. The model obtained here correctly predicts more than 90% of the BBB permeation data. By quantifying the favorable and unfavorable contributions of physicochemical and structural properties, it also offers valuable insights for drug design, pharmacological profiling, and screening. The computational procedure is fully automated and quite fast. The method thus appears as a valuable new tool in virtual screening where selection or prioritization of candidates is required from large collections of compounds.

Predicting Blood-Brain Barrier Permeation from Three-Dimensional Molecular Structure

CRUCIANI, Gabriele;
2000

Abstract

Predicting blood-brain barrier (BBB) permeation remains a challenge in drug design. Since it is impossible to determine experimentally the BBB partitioning of large numbers of preclinical candidates, alternative evaluation methods based on computerized models are desirable. The present study was conducted to demonstrate the value of descriptors derived from 3D molecular fields in estimating the BBB permeation of a large set of compounds and to produce a simple mathematical model suitable for external prediction. The method used (VolSurf) transforms 3D fields into descriptors and correlates them to the experimental permeation by a discriminant partial least squares procedure. The model obtained here correctly predicts more than 90% of the BBB permeation data. By quantifying the favorable and unfavorable contributions of physicochemical and structural properties, it also offers valuable insights for drug design, pharmacological profiling, and screening. The computational procedure is fully automated and quite fast. The method thus appears as a valuable new tool in virtual screening where selection or prioritization of candidates is required from large collections of compounds.
2000
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/152941
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 459
  • ???jsp.display-item.citation.isi??? 400
social impact