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Online OCHEM multi-task model for solubility and lipophilicity prediction of platinum complexes.
J. Inorg. Biochem. 269:112890 (2025)
Predicting the solubility and lipophilicity of platinum(II, IV) complexes is essential for prioritizing potential anticancer candidates in drug discovery. This study introduces the first publicly available online model for predicting the solubility of platinum complexes, addressing the lack of literature and models in this regard. Using a time-split dataset, we developed a consensus model with a Root Mean Squared Error (RMSE) of 0.62 through 5-cross-validation on a training set of 284 historical compounds (solubility data reported prior to 2017). However, the RMSE increased to 0.86 when applied to a prospective test set of 108 compounds reported after 2017. Further analysis of the high prediction errors revealed that these inaccuracies are primarily attributed to the underrepresentation of novel chemical scaffolds, particularly Pt(IV) derivatives, in the training sets. For instance, a series of eight phenanthroline-containing compounds, not covered by the training set's chemical space, had an RMSE of 1.3. When the model was redeveloped using a combined dataset, the RMSE of this series significantly decreased to 0.34 under the same validation protocol. Additionally, we developed an interpretable linear model to identify structural features and functional groups that influence the solubility of platinum complexes. We further validated the correlation between solubility and lipophilicity, consistent with the Yalkowsky General Solubility Equation. Building on these insights, we developed a final multitask model that simultaneously predicts solubility and lipophilicity as two endpoints with RMSE = 0.62 and 0.44, respectively. The data and final developed model is available at https://ochem.eu/article/31.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Consensus Model ; Lipophilicity ; Neural Networks ; Platinum Pt(ii)/pt(iv) Complexes ; Representation Learning ; Water Solubility; Biological-activity; Molecular Descriptors; Aqueous Solubility; Pt(ii) Complexes; In-vitro; Cytotoxicity; Prodrugs; Behavior; Therapy
ISSN (print) / ISBN
0162-0134
e-ISSN
0162-0134
Quellenangaben
Volume: 269,
Article Number: 112890
Publisher
Elsevier
Publishing Place
Ste 800, 230 Park Ave, New York, Ny 10169 Usa
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Structural Biology (STB)
Grants
Marie Sklodowska-Curie Innovative Training Network European Industrial Doctorate grant "Advanced machine learning for Innovative Drug Discovery" (AIDD)