Considerable attention has been given on the search for novel anticancer drugs with respect to the disease sequelae on human health and well-being. Triazole is considered to be an attractive scaffold possessing diverse biological activities. Structural modification on the privileged structures is noted as an effective strategy towards successful design and development of novel drugs. The quantitative structure–activity relationships (QSAR) is well-known as a powerful computational tool to facilitate the discovery of potential compounds. In this study, a series of thirty-two 1,2,3-triazole derivatives (1–32) together with their experimentally measured cytotoxic activities against four cancer cell lines i.e., HuCCA-1, HepG2, A549 and MOLT-3 were used for QSAR analysis. Four QSAR models were successfully constructed with acceptable predictive performance affording R CV ranging from 0.5958 to 0.8957 and RMSECV ranging from 0.2070 to 0.4526. An additional set of 64 structurally modified triazole compounds (1A–1R, 2A–2R, 7A–7R and 8A–8R) were constructed in silico and their predicted cytotoxic activities were obtained using the constructed QSAR models. The study suggested crucial moieties and certain properties essential for potent anticancer activity and highlighted a series of promising compounds (21, 28, 32, 1P, 8G, 8N and 8Q) for further development as novel triazole-based anticancer agents.

Estrogen is an important component for the sustenance of normal physiological functions of the mammary glands, particularly for growth and differentiation. Approximately, two-thirds of breast cancers are positive for estrogen receptor (ERs), which is a predisposing factor for the growth of breast cancer cells. As such, ERα represents a lucrative therapeutic target for breast cancer that has attracted wide interest in the search for inhibitory agents. However, the conventional laboratory processes are cost- and time-consuming. Thus, it is highly desirable to develop alternative methods such as quantitative structure–activity relationship (QSAR) models for predicting ER-mediated endocrine agitation as to simplify their prioritization for future screening. In this study, we compiled and curated a large, non-redundant data set of 1231 compounds with ERα inhibitory activity (pIC₅₀). Using comprehensive validation tests, it was clearly observed that the model utilizing the substructure count as descriptors, performed well considering two objectives: using less descriptors for model development and achieving high predictive performance (R_Tr ² = 0.94, Q_CV ² = 0.73, and Q_Ext ² = 0.73). It is anticipated that our proposed QSAR model may become a useful high-throughput tool for identifying novel inhibitors against ERα.

Sulfur-containing compounds are considered as attractive pharmacophores for discovery of new drugs regarding their versatile properties to interact with various biological targets. Quantitative structure-activity relationship (QSAR) modeling is one of well-recognized in silico tools for successful drug discovery. In this work, a set of 38 sulfur-containing derivatives (Types I–VI) were evaluated for their in vitro anticancer activities against 6 cancer cell lines. In vitro findings indicated that compound 13 was the most potent cytotoxic agent toward HuCCA-1 cell line (IC₅₀ = 14.47 μM). Compound 14 exhibited the most potent activities against 3 investigated cell lines (i.e., HepG2, A549, and MDA-MB-231: IC₅₀ range = 1.50–16.67 μM). Compound 10 showed the best activity for MOLT-3 (IC₅₀ = 1.20 μM) whereas compound 22 was noted for T47D (IC₅₀ = 7.10 μM). Subsequently, six QSAR models were built using multiple linear regression (MLR) algorithm. All constructed QSAR models provided reliable predictive performance (training sets: R_tr range = 0.8301–0.9636 and RMSE_tr = 0.0666–0.2680; leave-one-out cross validation sets: R_CV range = 0.7628–0.9290 and RMSE_CV = 0.0926–0.3188). From QSAR modeling, chemical properties such as mass, polarizability, electronegativity, van der Waals volume, octanol-water partition coefficient, as well as frequency/presence of C–N, F–F, and N–N bonds in the molecule are essential key predictors for anticancer activities of the compounds. In summary, a series of promising fluoro-thiourea derivatives (10, 13, 14, 22) were suggested as potential molecules for future development as anticancer agents. Key structure-activity knowledge obtained from the QSAR modeling was suggested to be advantageous for suggesting the effective rational design of the related sulfur-containing anticancer compounds with improved bioactivities and properties.

P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors.

Coumarins are well-known for their antioxidant effect and aromatic property, thus, they are one of ingredients commonly added in cosmetics and personal care products. Quantitative structure-activity relationships (QSAR) modeling is an in silico method widely used to facilitate rational design and structural optimization of novel drugs. Herein, QSAR modeling was used to elucidate key properties governing antioxidant activity of a series of the reported coumarin-based antioxidant agents (1-28). Several types of descriptors (calculated from 4 softwares i.e., Gaussian 09, Dragon, PaDEL and Mold2 softwares) were used to generate three multiple linear regression (MLR) models with preferable predictive performance (Q2LOO-CV = 0.813-0.908; RMSELOO-CV = 0.150-0.210; Q2Ext = 0.875-0.952; RMSEExt = 0.104-0.166). QSAR analysis indicated that number of secondary amines (nArNHR), polarizability (G2p), electronegativity (D467, D580, SpMin2_Bhe, and MATS8e), van der Waals volume (D491 and D461), and H-bond potential (SHBint4) are important properties governing antioxidant activity. The constructed models were also applied to guide in silico rational design of an additional set of 69 structurally modified coumarins with improved antioxidant activity. Finally, a set of 9 promising newly design compounds were highlighted for further development. Structure-activity analysis also revealed key features required for potent activity which would be useful for guiding the future rational design. In overview, our findings demonstrated that QSAR modeling could possibly be a facilitating tool to enhance successful development of bioactive compounds for health and cosmetic applications.