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Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty.
Mervin, Lewis H; Trapotsi, Maria-Anna; Afzal, Avid M; Barrett, Ian P; Bender, Andreas; Engkvist, Ola.
Affiliation
  • Mervin LH; Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK. lewis.mervin1@astrazeneca.com.
  • Trapotsi MA; Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
  • Afzal AM; Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
  • Barrett IP; Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
  • Bender A; Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
  • Engkvist O; Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
J Cheminform ; 13(1): 62, 2021 Aug 19.
Article in En | MEDLINE | ID: mdl-34412708
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Clinical_trials / Prognostic_studies / Risk_factors_studies Language: En Journal: J Cheminform Year: 2021 Document type: Article Affiliation country: United kingdom Country of publication: United kingdom
Fulltext
Add to My VHL
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Clinical_trials / Prognostic_studies / Risk_factors_studies Language: En Journal: J Cheminform Year: 2021 Document type: Article Affiliation country: United kingdom Country of publication: United kingdom