Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery.
Visualization of 'chemical space' 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.
Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Zurich, Switzerland.
Michael
M
Reutlinger
Gisbert
G
Schneider
D000465:Algorithms; D003198:Computer Simulation; D055808:Drug Discovery; D008956:Models, Chemical; D008968:Molecular Conformation; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship; D054852:Small Molecule Libraries
D054852:Small Molecule Libraries