Virtual drug screening using neural networks
| Autor: | Martin T. Hagan, Daniel M. Hagan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Self-organizing map
010304 chemical physics Artificial neural network Computer science business.industry Monte Carlo method 010402 general chemistry Machine learning computer.software_genre 01 natural sciences Small molecule 0104 chemical sciences Data set ComputingMethodologies_PATTERNRECOGNITION 0103 physical sciences Network performance Artificial intelligence Receptor business computer |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2016.7727252 |
| Popis: | In this paper, we describe how neural networks can be used for high throughput screening of potential drug candidates. Individual small molecules (ligands) are assessed for their potential to bind to specific proteins (receptors). Committees of multilayer networks are used to classify protein-ligand complexes as good binders or bad binders, based on selected chemical descriptors. The novel aspects of this paper include the use of statistical analyses on the weights of single layer networks to select the appropriate descriptors, the use of Monte Carlo cross-validation to provide confidence measures of network performance (and also to identify problems in the data), and the use of Self Organizing Maps to analyze the performance of the trained network and identify anomalies. We demonstrate the procedures, on a large practical data set, and use them to discover a promising characteristic of the data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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