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In this paper, multiple linear regression (MLR) was used to build quantitative structure property relationship (QSPR) of n-octanol-water partition coefficient (logPo/w) of 195 substituted aromatic drugs. The molecular descriptors were calculated for each compound by the VLifeMDS. By applying genetic algorithm/multiple linear regressions (GA/MLR) the most relevant descriptors were selected to build a QSPR model. The robustness of the model was characterized by the statistical validation and applicability domain (AD). The prediction results from MLR are in good agreement with the experimental values. The R2 and Q2 LOO for MLR are 0.9433, 0.9341. The AD of the model was analyzed based on the Williams plot. The effects of different selected descriptors are described.

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Sorafenib tosylate (SORt) is an oral multikinase inhibitor used for treatment of advanced renal cell, liver, and thyroid cancers. In this study, this drug was synthesized and its antiproliferative activities against HCT116 and CT26 cells were assessed. The interaction of SORt with β‐lactoglobulin (BLG) was studied using different fluorescence techniques, circular dichroism (CD), zeta potential measurements, and docking simulation. The results of infrared (IR), mass, HNMR, and CNMR spectra demonstrated that the drug was produced with high quality, purity, and efficiency. SORt showed potent cytotoxicity against HCT116 and CT26 cells with IC50 of 8.12 and 5.42 μM, respectively. For BLG binding of SORt, the results showed that static quenching was the cause of the high affinity drug–protein interaction. Three‐dimensional fluorescence and synchronous spectra indicated that SORt conformation was changed at different levels. CD suggested that the α‐helix content remained almost constant in the BLG–SORt complex, whereas random coil content decreased. Zeta potential values of BLG were more positive after binding with SORt, due to electrostatic interactions between BLG and SORt. Thermodynamic parameters confirmed van der Waals and hydrogen bond interactions in the complex formation. Molecular modelling predicted the presence of hydrogen bonds and electrostatic forces in the BLG–SORt system, which was consistent with the experimental results.

Early diagnosis and effective treatment of cancer are challenging. To diagnose and treat cancer effectively and to overcome these challenges, fundamental innovations in traditional diagnosis and therapy are necessary. Peptides can be very helpful in this regard due to their potential and diversity. To enhance the therapeutic potential of peptides, their limitations must be properly identified and their structures engineered and modified for higher efficiency. Promoting the bioavailability and stability of peptides is one of the main concerns. Peptides can also be effective in different areas of targeting, alone or with the help of other therapeutic agents. There has been a lot of research in this area, and the potential for variability of peptides will continue to improve this process. Another promising area in which peptides can help treat cancer is peptide vaccines, which are undergoing promising research, and high throughput technologies can lead to fundamental changes in this area. Peptides have been effective in almost all areas of cancer treatment, and some have even gone through clinical phases. However, many barriers need to be overcome to reach the desired point. The purpose of this review is to evaluate the mechanisms associated with peptides in the diagnosis and treatment of cancer. Therefore, related studies in this area will be discussed.

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