A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru
Abstract
:1. Introduction
2. Results and Discussion
2.1. Analysis of the Datasets
2.2. Feature Selection and Development of QSPR Models
2.3. Mechanism Interpretation
2.4. Applicability Domain of the SVM–RF–GBM Model
2.5. Comparative Analysis of the SVM–RF–GBM Model and the Other Models
2.6. Prediction of Log Papp Values for Natural Products from Peru
2.7. Evaluation of Drug-Likeness of Natural Products
- DLS_01 is derived from a modified version of Ro5 [36], comprising four rules, according to Equation (1);
- DLS_02 is defined by six rules, including a HBD ≤ 5, a HBA in the range 1–8, an MW in the range 200–450, an MlogP in the range −2.0–4.5, an RBN in the range from 1–9, and the number of rings ≤ 5 [39];
- DLS_03 incorporates criteria such as a HBD ≤ 5, a HBA ≤ 10, an MW from 200 to 500, an MlogP in the range −5.0–5.0, an RBN ≤ 8, and a formal charge in the range −2–2 [40];
- DLS_04 is defined by parameters including a HBD ≤ 5, a HBA in the range 2–10, an MW in the range 78–500, an MlogP in the range −0.5–5.0, the ratio of the number of Csp3 atoms to the total number of non-halogen atoms in the range 0.15–0.8, the ratio of the number of hydrogen atoms to the total number of non-halogen atoms in the range 0.6–1.6, and the ratio of molecular unsaturation (Unsat-p) in the range 0.10–0.45 [41];
- DLS_05 relies on criteria such as the ratio of the total number of oxygen and nitrogen atoms and the number of Csp3 atoms in the range 0.10–1.80, and a descriptor Unsat-p ≤ 0.43 [42];
- DLS_06 is based on a HBD ≤ 5, a HBA ≤ 10, an MW ≤ 500, an MlogP ≤ 5, an RBN ≤ 10, and a TPSA ≤ 140 [43];
- DLS_07 is based on the criteria of an RBN ≤ 10, and a TPSA ≤ 140 [37];
- DLS_cons represents the average drug-likeness score obtained from the previously described criteria.
3. Materials and Methods
3.1. Data Collection
3.2. The Calculation and Optimization of the 3D Structure
3.3. Molecular Descriptor Calculation
3.4. Splitting of the Dataset
3.5. Preprocessing
3.6. Feature Selection
3.7. Modeling
3.8. Model Validation
3.9. Applicability Domain
3.10. Computational Processing
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | RMSETrain | R2Train | RMSECV | R2CV | RMSETest | R2Test | Hyperparameters |
---|---|---|---|---|---|---|---|
MLR | 0.43 | 0.70 | 0.44 | 0.68 | 0.47 | 0.63 | - |
PLS | 0.43 | 0.70 | 0.44 | 0.68 | 0.47 | 0.63 | ncomp = 11 |
SVM | 0.28 | 0.87 | 0.40 | 0.74 | 0.40 | 0.73 | sigma = 0.015, C = 2 |
RF | 0.16 | 0.97 | 0.40 | 0.75 | 0.39 | 0.74 | mtry = 18 |
GBM | 0.19 | 0.94 | 0.40 | 0.74 | 0.39 | 0.74 | n.trees = 100, interaction.depth = 16 |
SVM–RF–GBM | 0.19 | 0.94 | 0.38 | 0.76 | 0.38 | 0.76 | - |
Molecular Descriptor | Group | r | Description |
---|---|---|---|
maxHBint7 | E-state | −0.50 | Maximum E-state descriptors of strength for potential hydrogen bonds of path length 7 |
ALogP | Constitucional | 0.46 | Ghose-Crippen LogKow |
SpMAD_Dzs | Barysz matrix | −0.43 | Spectral mean absolute deviation from Barysz matrix/weighted by I-state |
maxHBint5 | E-state | −0.48 | Maximum E-state descriptors of strength for potential hydrogen bonds of path length 5 |
maxHBint9 | E-state | −0.47 | Maximum E-state descriptors of strength for potential hydrogen Bonds of path length 9 |
Eta_D_epsiD | ETA index | −0.39 | Eta measure of hydrogen bond donor atoms |
maxHBint3 | E-state | −0.46 | Maximum E-state descriptors of strength for potential hydrogen bonds of path length 3 |
SHED_DL | Pharmacophore descriptor | −0.47 | SHED donor–lipophilic |
maxHBd | E-state | −0.35 | Maximum E-states for (strong) hydrogen bond donors |
Hypertens.80 | Drug-like index | 0.41 | Ghose–Viswanadhan–Wendoloski antihypertensive-like index at 80% |
Study | Method | Descriptors | N | RMSETest | R2Test | Reference |
---|---|---|---|---|---|---|
This study | SVM–RF–GBM | 2D and 3D descriptors from PaDEL-Descriptor and alvaDesc | 1817 | 0.38 | 0.76 | - |
Wang and Chen, 2020 | Dual-RBF neural network | 2D descriptors from PaDEL-Descriptor | 1827 | 0.39 | 0.76 | [28] |
Wang et al., 2016 | Boosting | 2D and 3D MOE descriptors | 1017 | 0.31 | 0.812 | [21] |
Lanevskij et al., 2019 | Non-linear least squares | log Do/w, pKa, NHD, Vx | 442 | 0.49 | 0.77 | [20] |
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Acuña-Guzman, V.; Montoya-Alfaro, M.E.; Negrón-Ballarte, L.P.; Solis-Calero, C. A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru. Pharmaceuticals 2024, 17, 750. https://doi.org/10.3390/ph17060750
Acuña-Guzman V, Montoya-Alfaro ME, Negrón-Ballarte LP, Solis-Calero C. A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru. Pharmaceuticals. 2024; 17(6):750. https://doi.org/10.3390/ph17060750
Chicago/Turabian StyleAcuña-Guzman, Victor, María E. Montoya-Alfaro, Luisa P. Negrón-Ballarte, and Christian Solis-Calero. 2024. "A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru" Pharmaceuticals 17, no. 6: 750. https://doi.org/10.3390/ph17060750