INVESTIGADORES
TALEVI Alan
capítulos de libros
Título:
Clustering of Small Molecules
Autor/es:
ALAN TALEVI; LUCAS N ALBERCA; CAROLINA L BELLERA
Libro:
Computer-Aided and Machine Learning-Driven Drug Design
Editorial:
Springer
Referencias:
Año: 2025; p. 109 - 129
Resumen:
Clustering of small molecules finds a diversity of applications in chemistry and, in particular, in the fields of cheminformatics and drug discovery. It may be used directly as an unsupervised machine-learning strategy to identify existing patterns in a chemical data set or libraries or integrated into supervised machine-learning studies to partition a sample of compounds into representative subsamples (e.g., training and validation data). It may also be applied to select which in silico hits from a virtual screening campaign will be submitted to experimental confirmation, or to define which hits emerging from a wet screening campaign will be prioritized for further development or characterization. Here, we review general strategies to validate the output of a clustering algorithm and discuss current challenges and possible future directions in the field of small molecule clustering.
