Combining Machine Learning and Pharmacophore-Based Interaction Fingerprint for in Silico Screening

In this study, we developed a new pharmacophore-based interaction fingerprint (Pharm-IF) and examined its usefulness for in silico screening using machine learning techniques such as support vector machine (SVM) and random forest (RF) instead of similarity-based ranking. Using the docking results of...

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Bibliographic Details
Published in:Journal of chemical information and modeling 2010-01, Vol.50 (1), p.170-185
Main Authors: Sato, Tomohiro, Honma, Teruki, Yokoyama, Shigeyuki
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Biological and medical sciences
Comparative analysis
Computational Biology
Computer science
control theory
systems
Crystal structure
Data processing. List processing. Character string processing
Drug Evaluation, Preclinical - methods
Enzyme Inhibitors - chemistry
Enzyme Inhibitors - metabolism
Enzyme Inhibitors - pharmacology
Exact sciences and technology
General pharmacology
HIV
Human immunodeficiency virus
Humans
Ligands
Medical sciences
Medical screening
Memory organisation. Data processing
Models, Molecular
Pharmaceutical Modeling
Pharmaceutical technology. Pharmaceutical industry
Pharmacology. Drug treatments
Proteases
Protein Binding
Protein Conformation
Software
Structure-Activity Relationship
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Summary:In this study, we developed a new pharmacophore-based interaction fingerprint (Pharm-IF) and examined its usefulness for in silico screening using machine learning techniques such as support vector machine (SVM) and random forest (RF) instead of similarity-based ranking. Using the docking results of PKA, SRC, cathepsin K, carbonic anhydrase II, and HIV-1 protease, the screening efficiencies of the Pharm-IF models were compared to GLIDE score and the residue-based IF (PLIF) models. The combination of SVM and Pharm-IF demonstrated a higher enrichment factor at 10% (5.7 on average) than those of GLIDE score (4.2) and PLIF (4.3). In terms of the size of the training sets, learning more than five crystal structures enabled the machine learning models to stably achieve better efficiencies than GLIDE score. We also employed the docking poses of known active compounds, in addition to the crystal structures, as positive samples of training sets. The enrichment factors of the RF models at 10% using the docking poses for SRC and cathepsin K showed significantly higher values (6.5 and 6.3) than those using only the crystal structures (3.9 and 3.2), respectively.
ISSN:1549-9596
1549-960X
DOI:10.1021/ci900382e