New Approach to Pharmacophore Mapping and QSAR Analysis Using Inductive Logic Programming. Application to Thermolysin Inhibitors and Glycogen Phosphorylase b Inhibitors

Abstract

A key problem in QSAR is the selection of appropriate descriptors to form accurate regression equations for the compounds under study. Inductive logic programming (ILP) algorithms are a class of machine-learning algorithms that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives ILP the advantages of not requiring explicit superimposition of individual compounds in a dataset, of dealing naturally with multiple conformations, and of using a language much closer to that used normally by chemists. We unify ILP and standard regression techniques to give a QSAR method that has the strength of ILP at describing steric structure with the familiarity and power of regression methods. Complex pharmacophores, correlating with activity, were identified and used as new indicator variables, along with the comparative molecular field analysis (CoMFA) prediction, to form predictive regression equations. We compared the formation of 3D-QSARs using standard CoMFA with the use of ILP on the well-studied thermolysin zinc protease inhibitor dataset and a glycogen phosphorylase inhibitor dataset. In each case the addition of ILP variables produced statistically better results (P < 0.01 for thermolysin and P < 0.05 for GP datasets) than the CoMFA analysis. Moreover, the new ILP variables were not found to increase the complexity of the final QSAR equations and gave possible insight into the binding mechanism of the ligand-protein complex under study.

A Novel Logic-Based Approach for Quantitative Toxicology Prediction

“…A step toward generating quantitative SAR using ILP-based SAR was introduced by King and co-workers. 21 By inspection, they identified a few ILP-derived rules which were used as features in a linear regression to obtain a quantitatiVe SAR. 21 However, their approach did not encapsulate the diversity of logic-learned rules and therefore would be difficult to apply to a diverse dataset, such as the one required for toxicity prediction.…”

Quantitative pharmacophore models with inductive logic programming

“…Figure 13 shows the correlation coefficients obtained by the leave-one-out procedure described. Also tabulated are the best results obtained with the standard QSAR 3-D technique CoMFA (comparative molecular field analysis) and a two-stage approach by King and colleagues (Marchand-Geneste et al, 2002). The models obtained with linear and kernel regression are not as good as those with CoMFA or King.…”

“…Zinc-containing proteases like Thermolysin play an important role in physiological processes like digestion and blood pressure regulation. Data on a number of inhibitors of Thermolysin are readily available in the literature: we use the molecules studied by Marchand-Geneste et al (2002). The data consist of crystal structures and corresponding activity values (pKi = −logKi) of 31 inhibitors.…”

Application of Inductive Logic Programming to Structure-Based Drug Design

“…A Prolog example of generating building blocks facts from molecular structure is illustrated in [21]. The pharmacophore elements, available for the present SAR analysis (figure 2) can be divided in the following two categories:…”

“…For such problems, logic provides a unified way of representing the relations between objects (atoms and bonds). ILP systems have progressively been shown to be capable of handling 1 [12], 2 [16] and 3 dimensional [8,21] descriptions of the molecular structures, allowing the development of compact and comprehendable theories. Moreover, ILP has achieved the same predictive power or has significantly improved the traditional QSAR (Quantitative SAR) built using standard propositional learners and statistical methods [15,16].…”