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.

Visualization of ‘chemical space’ has received particular attraction by medicinal chemists as it enables the intuitive and conceptual comprehension of pharmaceutically relevant molecular features. Recent advances in machine-learning and artificial intelligence have resulted in a transfer of methods to chemistry. We provide an overview of prominent visualization methods based on nonlinear dimensionality reduction, and their application in drug discovery.Figure optionsDownload high-quality image (367 K)Download as PowerPoint slideHighlights
► Visualization of compound distributions on maps can be used for systematic navigation in chemical space and molecular design.
► Nonlinear projection of chemical space can help identify “activity islands”.
► Nonlinear mapping of chemical data can reveal underlying structure–activity relationships.