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Effectiveness of molecular fingerprints for exploring the chemical space of natural products.
Boldini, Davide; Ballabio, Davide; Consonni, Viviana; Todeschini, Roberto; Grisoni, Francesca; Sieber, Stephan A.
Afiliación
  • Boldini D; TUM School of Natural Sciences, Department of Bioscience, Technical University of Munich, Center for Functional Protein Assemblies (CPA), 85748, Garching bei München, Germany. davide.boldini@tum.de.
  • Ballabio D; Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.zza Della Scienza, 1, 20126, Milan, Italy.
  • Consonni V; Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.zza Della Scienza, 1, 20126, Milan, Italy.
  • Todeschini R; Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.zza Della Scienza, 1, 20126, Milan, Italy.
  • Grisoni F; Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
  • Sieber SA; Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Utrecht, Netherlands.
J Cheminform ; 16(1): 35, 2024 Mar 25.
Article en En | MEDLINE | ID: mdl-38528548
ABSTRACT
Natural products are a diverse class of compounds with promising biological properties, such as high potency and excellent selectivity. However, they have different structural motifs than typical drug-like compounds, e.g., a wider range of molecular weight, multiple stereocenters and higher fraction of sp3-hybridized carbons. This makes the encoding of natural products via molecular fingerprints difficult, thus restricting their use in cheminformatics studies. To tackle this issue, we explored over 30 years of research to systematically evaluate which molecular fingerprint provides the best performance on the natural product chemical space. We considered 20 molecular fingerprints from four different sources, which we then benchmarked on over 100,000 unique natural products from the COCONUT (COlleCtion of Open Natural prodUcTs) and CMNPD (Comprehensive Marine Natural Products Database) databases. Our analysis focused on the correlation between different fingerprints and their classification performance on 12 bioactivity prediction datasets. Our results show that different encodings can provide fundamentally different views of the natural product chemical space, leading to substantial differences in pairwise similarity and performance. While Extended Connectivity Fingerprints are the de-facto option to encoding drug-like compounds, other fingerprints resulted to match or outperform them for bioactivity prediction of natural products. These results highlight the need to evaluate multiple fingerprinting algorithms for optimal performance and suggest new areas of research. Finally, we provide an open-source Python package for computing all molecular fingerprints considered in the study, as well as data and scripts necessary to reproduce the results, at https//github.com/dahvida/NP_Fingerprints .
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Cheminform Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Cheminform Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido