Structure-activity relationship for Fe(III)-salen-like complexes as potent anticancer agents.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Fe(III)-salen complexes show promise as anticancer agents.
• Understanding the structural basis for their activity is crucial for developing new drugs.

Purpose of the Study:

• To establish a quantitative structure-activity relationship (QSAR) for the anticancer activity of Fe(III)-salen and related complexes.
• To identify key structural features influencing anticancer efficacy.

Main Methods:

• Density Functional Theory (DFT) calculations (B3LYP/LANL2DZ) for structure optimization.
• Calculation of 1497 theoretical descriptors, including quantum-chemical parameters, NMR shielding, and electronic descriptors.
• Application of Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for QSAR modeling.
• Utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS) to select optimal descriptors for the ANN model.

Main Results:

• An ANN-ANFIS model achieved high statistical significance (R²train = 0.99, RMSE = 0.138, Q²LOO = 0.82), demonstrating excellent predictive capability.
• The model effectively predicts the anticancer activity of novel Fe(III)-salen derivatives.
• Key determinants of anticancer activity were identified as geometrical parameters, substituent position, and the electronic nature of the salen ligand.

Conclusions:

• The developed ANN-ANFIS model provides a robust tool for predicting the anticancer potential of Fe(III)-salen complexes.
• Anticancer activity is strongly correlated with specific structural and electronic properties of the salen ligand.
• This research facilitates the rational design of more potent anticancer agents based on the Fe(III)-salen scaffold.