Statistical tools for virtual screening
| Autor: | Charles L. Lerman, Jennifer R. Krumrine, and Andrew T. Maynard |
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| Rok vydání: | 2005 |
| Předmět: |
Models
Molecular Virtual screening Binding Sites Databases Factual Chemistry business.industry Statistics as Topic Sampling (statistics) Proteins Machine learning computer.software_genre Ligands Task (project management) Pharmaceutical Preparations Drug Discovery Metric (mathematics) False positive paradox Molecular Medicine Artificial intelligence business computer Algorithms Probability |
| Zdroj: | Journal of medicinal chemistry. 48(23) |
| ISSN: | 0022-2623 |
| Popis: | In large-scale virtual screening (VS) campaigns, data are often computed for millions of compounds to identify leads, but there remains the task of prioritizing VS “hits” for experimental assays and the dilemma of assessing true/false positives. We present two statistical methods for mining large databases: (1) a general scoring metric based on the VS signal-to-noise level within a compound neighborhood; (2) a neighborhood-based sampling strategy for reducing database size, in lieu of property-based filters. |
| Databáze: | OpenAIRE |
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