Demonstrating the Absence of Correlation Between Molecular Docking and in vitro Cytotoxicity in Anti-Breast Cancer...

In silico methods have significantly transformed the landscape of drug discovery by enabling rapid and cost-effective screening of prospective therapeutic compounds. However, these computational techniques remain limited in their ability to fully predict complex biological behavior, particularly within the constraints of quantum level interactions and simplified receptor-ligand models. As such, validation through experimental data remains critical.

This review aims to critically evaluate the correlation between molecular docking predictions specifically Gibbs free energy (ΔG) and in vitro cytotoxicity data (IC₅₀ values) obtained from MCF-7 breast cancer cell studies.

A structured methodology was employed, applying predefined inclusion and exclusion criteria to identify studies reporting both in silico molecular docking results and in vitro cytotoxicity data on the MCF-7 cell line, with a focus on compounds targeting breast cancer-related proteins.

Findings demonstrated that, contrary to theoretical expectations, no consistent linear correlation was observed between ΔG values and IC₅₀ across the analyzed compounds and targets. This discrepancy arises from several intertwined factors, including variability in protein expression within cell-based systems, compound-specific characteristics such as permeability and metabolic stability, and methodological limitations of docking approaches that rely on rigid receptor conformations and simplified scoring functions. In addition, the chemical diversity of the evaluated compounds further contributes to the inconsistency of cytotoxic outcomes. Nevertheless, when experimental and computational systems are uniformly controlled, a measurable and meaningful correlation between ΔG and IC₅₀ can be demonstrated.

This review underscores the need to move beyond single parameter docking predictions and adopt integrated strategies that combine computational models with empirical validations. Future studies should emphasize the use of standardized in vitro conditions, rational target selection, and complementary techniques such as molecular dynamics simulations, intracellular exposure assessment, and target engagement validation. These integrative approaches will enhance the predictive power of in silico methods and foster a more reliable foundation for anti-breast cancer drug development.