Developing and validating predictive decision tree models from mining chemical structural fingerprints and high–throughput screening data in PubChem
By Lianyi Han, Yanli Wang, Stephen H. Bryant. 2008
100 citations recorded by the source corpus.
BackgroundRecent advances in high-throughput screening (HTS) techniques and readily available compound libraries generated using combinatorial chemistry or derived from natural products enable the testing of millions of compounds in a matter of days. Due to the amount of information produced by HTS assays, it is a very challenging task to mine the HTS data for potential interest in drug development research. Computational approaches for the analysis of HTS results face great challenges due to the large quantity of information and significant amounts of erroneous data produced.ResultsIn this study, Decision Trees (DT) based models were developed to discriminate compound bioactivities by using their chemical structure fingerprints provided in the PubChem system http://pubchem.ncbi.nlm.nih.gov. The DT models were examined for filtering biological activity data contained in four assays deposited in the PubChem Bioassay Database including assays tested for 5HT1a agonists, antagonists, and HIV-1 RT-RNase H inhibitors. The 10-fold Cross Validation (CV) sensitivity, specificity and Matthews Correlation Coefficient (MCC) for the models are 57.2~80.5%, 97.3~99.0%, 0.4~0.5 respectively. A further evaluation was also performed for DT models built for two independent bioassays, where inhibitors for the same HIV RNase target were screened using different compound libraries, this experiment yields enrichment factor of 4.4 and 9.7.ConclusionOur results suggest that the designed DT models can be used as a virtual screening technique as well as a complement to traditional approaches for hits selection.
Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy
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Citations in the corpus
- Interpreting linear support vector machine models with heat map molecule coloring (2011, background): “[4] trained decision trees on several PubChem HTS data sets.”