Machine learning in prediction of intrinsic aqueous solubility of drug‐like compounds: Generalization, complexity, or predictive ability?-Reference-Cited by-同舟云学术

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Machine learning in prediction of intrinsic aqueous solubility of drug‐like compounds: Generalization, complexity, or predictive ability?

Published:2021-05-07 Issue:7-8 Volume:35 Page:
ISSN:0886-9383
Container-title:Journal of Chemometrics
language:en
Short-container-title:Journal of Chemometrics

Author:

Lovrić Mario¹²^ORCID,Pavlović Kristina¹^ORCID,Žuvela Petar³^ORCID,Spataru Adrian¹^ORCID,Lučić Bono²,Kern Roman¹⁴^ORCID,Wong Ming Wah³^ORCID

Affiliation:

1. Knowledge Discovery Know‐Center Graz Austria

2. NMR Centre Ruđer Bošković Institute Zagreb Croatia

3. Department of Chemistry National University of Singapore Singapore

4. Institute of Interactive Systems and Data Graz University of Technology Graz Austria

Funder

European Regional Development Fund

European Commission

Publisher

Wiley

Subject

Applied Mathematics,Analytical Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cem.3349

Reference80 articles.

1. Influence of physicochemical properties on dissolution of drugs in the gastrointestinal tract1PII of original article: S0169-409X(96)00487-5. The article was originally published in Advanced Drug Delivery Reviews 25 (1997) 3–14.1

2. Insoluble drug delivery strategies: review of recent advances and business prospects

3. Solubility Challenge Revisited after Ten Years, with Multilab Shake-Flask Data, Using Tight (SD ∼ 0.17 log) and Loose (SD ∼ 0.62 log) Test Sets

4. Findings of the Challenge To Predict Aqueous Solubility

5. Is Experimental Data Quality the Limiting Factor in Predicting the Aqueous Solubility of Druglike Molecules?

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