Natural Product-likeness Score and Its Application for Prioritization of Compound Libraries

By Peter Ertl, S. Roggo, Ansgar Schuffenhauer. Journal of Chemical Information and Modeling, 2008

323 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/b08e313ec40e1d47c6e386e730b1a7624e68293e

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Citations in the corpus

The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods (2023, methodology): “The new NP likeness score is based on the method published by Ertl et al. ( 37 ) and has been calculated based on an open-source implementation in RDKit ( https://github.com/ rdkit/ rdkit/ tree/ master/ Contrib/ NP _ Score ).”
Randomized SMILES strings improve the quality of molecular generative models (2019, methodology): “To prove that the molecules sampled from the randomized SMILES model are at least as diverse as those in the canonical, several physicochemical properties and metrics (as used in the MOSES benchmark [37]), such as molecular weight, logP, Synthetic Accessibility Score (SA) [38], Quantitative Estimate”
Natural products in drug discovery. (2008, methodology): “There is also a description of computational methods to compare the natural product likeness of compound libraries [ 40 ].”
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models (2018, unknown): “This score is calculated from several substructure descriptors and comparing them to the properties of other distributions of molecules [76].”
Natural product agonists of peroxisome proliferator-activated receptor gamma (PPARγ): a review (2014, background): “Natural products possess a high chemical scaffold diversity and are evolutionary optimized to serve different biological functions, conferring them a high drug-likeness and making them an excellent source for identification of new drug leads [117–119].”
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions (2009, background): “Lead candidates are normally prioritized according to criteria such as drug-likeness [1,2], natural-product likeness [3], predicted activity or Published: 10 June 2009”