Biomedical subjects

Kevin P Cross

Publications and source records attributed to Kevin P Cross.

4 recordsLinked to original sources

PubMed2006

Comparison of methods for sequential screening of large compound sets.

Sequential screening is an iterative procedure that can greatly increase hit rates over random screening or non-iterative procedures. We studied the effects of three factors on enrichment rates: the method used to rank compounds, the molecular descriptor set and the selection of initial training set. The primary factor influencing recovery rates was the method of selecting the initial training set. Rates for recovering active compounds were substantially lower with the diverse training sets than they were with training sets selected by other methods. Because structure-activity information is incrementally enhanced in intermediate training sets, sequential screening provides significant improvement in the average rate of recovery of active compounds when compared with non-iterative selection procedures.

Chemistry, Pharmaceutical↗

PubMed2006

Decision tree methods in pharmaceutical research.

Decision trees are among the most popular of the new statistical learning methods being used in the pharmaceutical industry for predicting quantitative structure-activity relationships. This article reviews applications of decision trees in drug discovery research and extensions to the basic algorithm using hybrid or ensemble methods that improve prediction accuracy.

Chemistry, Pharmaceutical↗

PubMed2004

Systematic analysis of large screening sets in drug discovery.

Each year large pharmaceutical companies produce massive amounts of primary screening data for lead discovery. To make better use of the vast amount of information in pharmaceutical databases, companies have begun to scrutinize the lead generation stage to ensure that more and better qualified lead series enter the downstream optimization and development stages. This article describes computational techniques for end to end analysis of large drug discovery screening sets. The analysis proceeds in three stages: In stage 1 the initial screening set is filtered to remove compounds that are unsuitable as lead compounds. In stage 2 local structural neighborhoods around active compound classes are identified, including similar but inactive compounds. In stage 3 the structure-activity relationships within local structural neighborhoods are analyzed. These processes are illustrated by analyzing two large, publicly available databases.

Algorithms↗

PubMed2003

Finding discriminating structural features by reassembling common building blocks.

We present a new method for constructing discriminating substructures by reassembling common medicinal chemistry building blocks. The algorithm can be parametrized to meet differing objectives: (1) to build features that discriminate for biological activity in a local structural neighborhood, (2) to build scaffolds for R-group analysis, (3) to construct cluster signatures that discriminate for membership in the cluster and provide a graphical representation for its members, and (4) to identify substructures that characterize major classes in a heterogeneous compound set. We illustrated the results of the algorithm on a literature dataset is of 118 compounds with in vitro inhibition data against recombinant human protein tyrosine phosphatase 1B (PTP-1B).

Algorithms↗