Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms Article Swipe
Ao Yang
,
Shirui Sun
,
Qi Lu
,
Zong Yang Kong
,
Jaka Sunarso
,
Weifeng Shen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.gce.2024.07.003
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.gce.2024.07.003
This study aims to significantly improve existing quantitative structure-property relationship (QSPR) models for predicting the octanol-water partition coefficient (KOW). This is because accurate predictions of KOW are crucial for assessing the environmental behavior and bioaccumulation potential of chemicals. Previous models have reported determination coefficient (R2) values between 0.9451 and 0.9681, and this research seeks to exceed these benchmarks. Three machine learning (ML) models are explored, i.e., feed-forward neural networks (FNN), extreme gradient boosting (XGBoost), and random forest (RF). Using a dataset of 14,610 solvents (14,580 after data cleaning) and 21 molecular descriptors derived from SMILES representations, we rigorously evaluate these models based on R2, mean absolute error (MAE), root mean squared error (RMSE), and mean relative error (MRE). Notably, the best model developed, the XGBoost-based QSPR, demonstrated exceptional performance, exhibiting an impressive R2 value of 0.9772, surpassing benchmarks set by prior research models. Additionally, shapley additive explanation (SHAP) analysis is also employed for model interpretation, and it is revealed that the top five influential input features include SMR_VSA8, SMR_VSA3, Kappa2, HeavyAtomCount, and fr_furan. This study not only sets a new benchmark for KOW prediction accuracy but also enhances the interpretability of QSPR models.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.gce.2024.07.003
- OA Status
- diamond
- Cited By
- 3
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400875490
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400875490Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.gce.2024.07.003Digital Object Identifier
- Title
-
Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-22Full publication date if available
- Authors
-
Ao Yang, Shirui Sun, Qi Lu, Zong Yang Kong, Jaka Sunarso, Weifeng ShenList of authors in order
- Landing page
-
https://doi.org/10.1016/j.gce.2024.07.003Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.gce.2024.07.003Direct OA link when available
- Concepts
-
Quantitative structure–activity relationship, Interpretability, Mean squared error, Artificial neural network, Random forest, Partition coefficient, Artificial intelligence, Octanol, Mathematics, Machine learning, Computer science, Statistics, Chemistry, ChromatographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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46Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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