A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models

A novel Automated Lazy Learning Quantitative Structure-Activity Relationship (ALL-QSAR) modeling approach has been developed based on the lazy learning theory. The activity of a test compound is predicted from locally weighted linear regression model using chemical descriptors and biological activity of the training set compounds most chemically similar to this test compound. The weights with which training set compounds are included in the regression depend on the similarity of those compounds to a test compound. We have applied the ALL-QSAR method to several experimental chemical datasets including 48 anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive binding affinities (Ki), and a Tetrahymena pyriformis dataset containing 250 phenolic compounds with toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents identified several known anticonvulsant compounds that were not only absent in the training set but highly chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be further exploited as a general tool for accurate bioactivity prediction and database screening in drug design and discovery. Due to its local nature, the ALL-QSAR approach appears to be especially well suited for the development of highly predictive models for the sparse or unevenly distributed datasets

Relational case-based reasoning for carcinogenic activity prediction

Lazy learning methods are based on retrieving a set of precedent cases similar to a new case. An important issue of these methods is how to estimate the similarity among a new case and the precedents. Usually, similarity measures require that cases have a prepositional representation. In this paper we present Shaud, a similarity measure useful to estimate the similarity among relational cases represented using feature terms. We also present results of the application of Shaud for solving classification tasks. Specifically we used Shaud for assessing the carcinogenic activity of chemical compounds in the Toxicology dataset.This work has been supported by the projects IBROW (IST-1999-19005) and SAMAP (TIC2002-04146-C05-01).Peer Reviewe

E.: Relational case-based reasoning for carcinogenic activity prediction

Lazy learning methods are based on retrieving a set of precedent cases similar to a new case. An important issue of these methods is how to estimate the similarity among a new case and the precedents. Usually, similarity measures require that cases have a propositional representation. In this paper we present Shaud, a similarity measure useful to estimate the similarity among relational cases represented using feature terms. We also present results of the application of Shaud for solving classification tasks. Specifically we used Shaud for assessing the carcinogenic activity of chemical compounds in the Toxicology dataset