Cancer is one of the greatest challenges that worry the minds of scientists and threatens human life. Despite the presence of several drugs on the market, their effectiveness remains limited by its resistance. In this research, the Monte Carlo approach was used for QSAR modelling applying the representation of the molecular structure by the SMILES and optimal molecular descriptors. Correlation Ideality (IIC) and Correlation Contradiction Index (CCI)) were introduced as validation parameters to further estimate the predictive ability of the developed models. The statistical quality of the model developed with (IIC) was good compared to those without (IIC). The best QSAR model of the following statistical parameters: (R²_train= 0.816, R²_valid= 0.825) was selected to generate the activity-increasing and decreasing promoters studied, and these are the basis for the design of seven new candidates as an antiproliferative inhibitory agent. Additionally, the newly designed molecules, the most active compound in the dataset, erlotinib and melphalan as control compounds were docked in the EGFR receptor binding site. The docking results discovered that the proposed candidates had the highest potential and energy affinity. Besides, Lipinski’s Rule of Five, Synthetic Accessibility and ADME/Tox were performed to assess bioavailability and drug-likeness of proposed compounds. In addition, MD simulation accompanied by MM-PBSA analysis discovered that compound A1 and the screened compounds were stable and did not show significant fluctuations throughout the simulation time. Generally, this research showed that the selected model well explains the antiproliferative activity and also that the proposed compounds have high activity, good binding affinity and stable conformation with the reported target protein.

A series of pyrrolidine derivatives have been used to study the main structural requirements for designing novel Mcl-1 inhibitors. For this purpose, three models CoMSIA, CoMFA and HQSAR were generated using QSAR molecular modeling techniques. The statistical results of the CoMFA (Q² = 0.689; R = 0.999; R²_pred = 0.986), CoMSIA (Q² = 0.614; R² = 0.923; R²_pred = 0.815) and HQSAR (Q²= 0.603; R² = 0.662; R²_pred = 0.743) models showed good stability and predictability. The results of the models were presented as contours and colored fragments indicating the favorable and unfavorable contribution to the inhibitory activity of Mcl-1. Based on the obtained results, four new compounds were designed with more potent predicted pIC₅₀ inhibitory activity. The ADME/Tox results and the pharmacokinetic properties revealed that these four compounds are orally bioavailable and show good permeability. In addition the four compounds showing non-inhibitors of CYP3A4 and CYP2D6 with the exception of Pred03. At the level of toxicity profile, the compounds Pred01, Pred02 and Pred03 showed interesting results and showed no AMES toxicity, no hERG inhibition and no skin sensitization. Molecular docking results were used to uncover the mode of interaction between the ligand and key residues of protein binding site. Molecular docking results were supported by molecular simulation and binding free energy estimation (MMPBSA). These results demonstrate the stability of the analyzed compounds in the target protein binding site during a 100 ns trajectory. Finally, all these results create a strong lead to develop promising new Pyrrolidine-based inhibitors against Mcl-1. Communicated by Ramaswamy H. Sarma