Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery

详细信息	查看全文 | 推荐本文 |

• 作者：Michael Reutlinger ; Gisbert Schneider ; ^{gisbert.schneider@pharma.ethz.ch}
• 关键词：2D ; two-dimensional ; 3D ; three-dimensional ; PCA ; principle component analysis ; PDB ; Protein Data Bank ; PPAR ; peroxisome proliferator-activated receptor ; QSAR ; quantitative structure&ndash ; activity relationship ; SNE ; stochastic neighbor embedding ; SOM ; sel
• 刊名：Journal of Molecular Graphics and Modelling
• 出版年：2012
• 期刊代码：77_10933263
• 类别：cp
• 出版时间：April, 2012
• 卷：34
• 期：Complete
• 页码：108-117
• 文件大小：1824 K

摘要

Visualization of 鈥榗hemical space鈥?and compound distributions has received much attraction by medicinal chemists as it may help to intuitively comprehend pharmaceutically relevant molecular features. It has been realized that for meaningful feature extraction from complex multivariate chemical data, such as compound libraries represented by many molecular descriptors, nonlinear projection techniques are required. Recent advances in machine-learning and artificial intelligence have resulted in a transfer of such methods to chemistry. We provide an overview of prominent visualization methods based on nonlinear dimensionality reduction, and highlight applications in drug discovery. Emphasis is on neural network techniques, kernel methods and stochastic embedding approaches, which have been successfully used for ligand-based virtual screening, SAR landscape analysis, combinatorial library design, and screening compound selection.