Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads
2.1. GA-MLR QSAR Models
2.1.1. Model-1.1 (Divided Set: Training Set–80% and Prediction Set–20%)
2.1.2. Model-1.2 (Divided Set: Training Set–80% and Prediction Set–20%)
- all_MSA3, fHringC4B, fOH5B and com_sp2N_2A: All of these molecular descriptors have positive values of the coefficient and increase in the values of these molecular descriptors, which increases MDA-MB-231 anti-proliferative activity.
- com_Splus_7A, com_don_6A and fringNC8B: These three molecular descriptors have negative coefficients and hence decrease in their values possibly will increase MDA-MB-231 anti-proliferative activity.
4. Materials and Methods
4.1. Dataset Selection
4.2. Molecular Structure Drawing and Optimization
4.3. Molecular Descriptor Calculation and Molecular Descriptor Pruning
4.4. QSAR Model–Development and Validation
- As per OECD guidelines, thorough internal as well as external validation of the developed QSAR model(s), for example, is necessarily mandatory. Hence, some molecules from the dataset were randomly kept aside as a prediction set, and remaining molecules (training set) were subjected to SFS treatment to develop the QSAR model. The QSAR model(s) generated is validated using molecules in the prediction set.Random splitting of the dataset using random splitting option in QSARINS v.2.2.4 into an 80% training set (175 molecules in training set) and a 20% prediction set (44 molecules in prediction set) was achieved. The training set was used for QSAR model development, and the prediction set was utilized for external validation.
- QSARINS v2.2.4 with default settings and Q2LOO as a fitness function for feature selection was deployed in genesis of the GA-MLR-based QSAR models with double cross validation. Up to six variables, there was a generous increase in the Q2LOO value, but minor augmentation was observed thereafter. Consequently, the selection of the molecular descriptor was confined to a set of six descriptors to foil the danger of over-fitting, and this additionally helped to derive easy and informative QSAR models (see supplementary information Table S3 values for all the selected molecular descriptors present in QSAR models).
- Abide by OECD guidelines; for ensured proper validation, all the models were subjected to internal and external validation, Y-randomization and model applicability domain (AD) analysis using QSARINS 2.2.4. Robustness of the GA-MLR-based QSAR model was adjudicated on the basis of (a) internal validation based on Leave-One-Out (LOO) and Leave-Many-Out (LMO) procedure; (b) external validation; (c) Y-randomization and (d) fulfilling of the respective threshold value for the statistical parameters: R2 ≥ 0.6, Q2LOO ≥ 0.5, Q2LMO ≥ 0.6, R2 > Q2, R2ex ≥ 0.6, RMSEtr < RMSEcv, ΔK ≥ 0.05, CCC ≥ 0.80, Q2-Fn ≥ 0.60, r2m ≥ 0.6, 0.9 ≤ k ≤ 1.1, 0.9 ≤ k’ ≤ 1.1 with RMSE and MAE close to zero. All QSAR models which failed to meet any of these criteria were omitted. Two QSAR models (1.1 and 1.2) with best values of these parameters and with best predicative ability (Q2-Fn > 0.71) were selected.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
|CADD||Computer-Aided Drug Designing|
|SMILES||Simplified Molecular-Input Line-Entry System|
|MLR||Multiple Linear Regression|
|QSAR||Quantitative Structure–Activity Relationship|
|OLS||Ordinary Least Square|
|OECD||Organization for Economic Co-operation and Development|
|OFS||Objective Feature Selection|
|SFS||Subjective Feature Selection|
|HER2||Human Epidermal growth factor Receptor 2|
|TNBC||Triple Negative Breast Cancer|
|BCCL||Breast Cancer Cell Line|
|LOF||Lack of Fit (Friedmann Parameter)|
|RMSE||Root Mean Square Error|
|MAE||Mean Absolute Error|
|RSS||Residual Sum of Squares|
|CCC||Concordance Correlation Coefficient|
|PRESS||Predictive Residual Sum of Squares|
|LOO||Leave One Out|
|LMO||Leave Many Out|
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Gandhi, A.; Masand, V.; Zaki, M.E.A.; Al-Hussain, S.A.; Ghorbal, A.B.; Chapolikar, A. Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads. Molecules 2021, 26, 4795. https://doi.org/10.3390/molecules26164795
Gandhi A, Masand V, Zaki MEA, Al-Hussain SA, Ghorbal AB, Chapolikar A. Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads. Molecules. 2021; 26(16):4795. https://doi.org/10.3390/molecules26164795Chicago/Turabian Style
Gandhi, Ajaykumar, Vijay Masand, Magdi E. A. Zaki, Sami A. Al-Hussain, Anis Ben Ghorbal, and Archana Chapolikar. 2021. "Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads" Molecules 26, no. 16: 4795. https://doi.org/10.3390/molecules26164795