Quantitative Pharmacophore Models with Inductive Logic Programming

• Tipo: Artigo em Revista Científica Internacional
• Data: 2006

Autores

Ashwin Srinivasan (Autor) Outra; David Page (Autor) Outra; Rui Camacho (Autor) FEUP; Ross King (Autor) Outra

Revista

Título: Machine Learning

Vol. 64 Nº 1/2/3

Páginas: 65-90

ISSN: 0885-6125

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 10 Citações

ISI Web of Science

Scopus - 14 Citações

INSPEC

COMPENDEX

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências da Saúde > Ciências farmacológicas ; Ciências Físicas > Ciência de computadores

Outras Informações

ID Authenticus: P-004-HNH

DOI: 10.1007/s10994-006-8262-2

Abstract (EN): Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or `ligands'). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often dicult to \align" the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt dierent shapes (or `conformations') arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the diculties associated with the alignment and multiconformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is \active"; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special diculties in the use of any statistical procedure. We present the principal issues and some solutions. Specically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning.

Idioma: Inglês

Tipo (Avaliação Docente): Científica