Exploring activity landscapes with extended similarity: is Tanimoto enough?

Understanding structure‐activity landscapes is essential in drug discovery. Similarly, it has been shown that the presence of activity cliffs in compound data sets can have a substantial impact not only on the design progress but also can influence the predictive ability of machine learning models....

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Veröffentlicht in:	Molecular informatics 2023-07, Vol.42 (7), p.e2300056-n/a
Hauptverfasser:	Dunn, Timothy B., López‐López, Edgar, Kim, Taewon David, Medina‐Franco, José L., Miranda‐Quintana, Ramón Alain
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms chemical space Datasets eSALI, similarity extended similarity Machine learning molecular fingerprints Similarity structure-activity relationships
Online-Zugang:	Volltext
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