Augmenting bioactivity by docking-generated multiple ligand poses to enhance machine learning and pharmacophore modelling: discovery of new TTK inhibitors as case study.

Al-Imam, Amenah M; Daoud, Safa; Hatmal, Ma'mon M; Taha, Mutasem Omar

Al-Imam, Amenah M; Daoud, Safa; Hatmal, Ma'mon M; Taha, Mutasem Omar.

Afiliación

Al-Imam AM; Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, 11492, Jordan.
Daoud S; Department of Pharmaceutical Chemistry and Pharmacognosy, Faculty of Pharmacy, Applied Sciences Private University, Amman, Jordan.
Hatmal MM; Department of Medical Laboratory Sciences, Faculty of Applied Health Sciences, The Hashemite University, PO. Box, 330127, Zarqa 13133, Jordan.
Taha MO; Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, 11492, Jordan.

Mol Inform ; 42(6): e2300022, 2023 Jun.

Article en En | MEDLINE | ID: mdl-37222400

Asunto(s)

Neoplasias; Farmacóforo; Humanos; Ligandos; Aprendizaje Automático

Palabras clave

QSAR; Shapley values; TTK; docking; machine learning; scoring

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Farmacóforo / Neoplasias Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Mol Inform Año: 2023 Tipo del documento: Article País de afiliación: Jordania Pais de publicación: Alemania

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