A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem

<p>Abstract</p> <p>Background</p> <p>The inverse-QSAR problem seeks to find a new molecular descriptor from which one can recover the structure of a molecule that possess a desired activity or property. Surprisingly, there are very few papers providing solutions to this problem. It is a difficult problem because the molecular descriptors involved with the inverse-QSAR algorithm must adequately address the forward QSAR problem for a given biological activity if the subsequent recovery phase is to be meaningful. In addition, one should be able to construct a feasible molecule from such a descriptor. The difficulty of recovering the molecule from its descriptor is the major limitation of most inverse-QSAR methods.</p> <p>Results</p> <p>In this paper, we describe the reversibility of our previously reported descriptor, the vector space model molecular descriptor (VSMMD) based on a vector space model that is suitable for kernel studies in QSAR modeling. Our inverse-QSAR approach can be described using five steps: (1) generate the VSMMD for the compounds in the training set; (2) map the VSMMD in the input space to the kernel feature space using an appropriate kernel function; (3) design or generate a new point in the kernel feature space using a kernel feature space algorithm; (4) map the feature space point back to the input space of descriptors using a pre-image approximation algorithm; (5) build the molecular structure template using our VSMMD molecule recovery algorithm.</p> <p>Conclusion</p> <p>The empirical results reported in this paper show that our strategy of using kernel methodology for an inverse-Quantitative Structure-Activity Relationship is sufficiently powerful to find a meaningful solution for practical problems.</p

Shortest Substring Ranking (MultiText Experiments for TREC-4)

To address the TREC-4 topics, we used a precise query language that yields and combines arbitrary intervals of text rather than pre-defined units like words and documents. Each solution was scored in inverse proportion to the length of the shortest interval containing it. Each document was scored by the sum of the scores of solutions within it. Whenever the above strategy yielded less than 1000 documents, documents satisfying successively weaker queries were added with lower rank. Our results for the ad-hoc topics compare favourably with the median average precision for all groups. 1 Introduction The central concern of the MultiText project at the University of Waterloo is the management of data in large-scale distributed text database systems [10]. A major component of this work has been the development of a query language that is suitable for expressing queries over the heterogeneous data that is present in a very large text database. The query language developed for the MultiText p..