The work described here is aimed at developing QSAR models capable of predicting in vitro human plasma lability/stability. They were built based on a dataset comprising about 200 known compounds. 3D structures of the molecules were drawn, optimized and submitted to the calculation of molecular descriptors that enabled selecting different TR/TS set pairs, subsequently exploited to develop QSAR models. Several 'machine learning' algorithms were explored in order to obtain suitable classification models, which were then validated on the relevant TS sets. Moreover the predictive ability of the best performing models was assessed on a Prediction set (PS) comprising about 40 molecules, not strictly related, from a structural point of view, to the initial dataset, but (obviously) comprised within the validity domain of the QSAR models obtained. The study allowed selecting predictive models enabling the classification of New Chemical Entities with regard to hydrolysis rate, that may be exploited for soft-drug design.

QSAR models for predicting enzymatic hydrolysis of new chemical entities in 'soft-drug' design

MACCHIA, MARCO;MINUTOLO, FILIPPO;BIANUCCI, ANNA MARIA PAOLA
2009

Abstract

The work described here is aimed at developing QSAR models capable of predicting in vitro human plasma lability/stability. They were built based on a dataset comprising about 200 known compounds. 3D structures of the molecules were drawn, optimized and submitted to the calculation of molecular descriptors that enabled selecting different TR/TS set pairs, subsequently exploited to develop QSAR models. Several 'machine learning' algorithms were explored in order to obtain suitable classification models, which were then validated on the relevant TS sets. Moreover the predictive ability of the best performing models was assessed on a Prediction set (PS) comprising about 40 molecules, not strictly related, from a structural point of view, to the initial dataset, but (obviously) comprised within the validity domain of the QSAR models obtained. The study allowed selecting predictive models enabling the classification of New Chemical Entities with regard to hydrolysis rate, that may be exploited for soft-drug design.
Massarelli, Ilaria; Macchia, Marco; Minutolo, Filippo; Prota, Giovanni; Bianucci, ANNA MARIA PAOLA
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/11568/202484
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 5
social impact