3D-QSAR modeling and molecular docking study on Mer kinase inhibitors of pyridine-substituted pyrimidines

Exploration of 2D and 3D-QSAR analysis and docking studies for novel dihydropteridone derivatives as promising therapeutic agents targeting glioblastoma

Background: Dihydropteridone derivatives represent a novel class of PLK1 inhibitors, exhibiting promising anticancer activity and potential as chemotherapeutic drugs for glioblastoma. Objective: The aim of this study is to develop 2D and 3D-QSAR models to validate the anticancer activity of dihydropteridone derivatives and identify optimal structural characteristics for the design of new therapeutic agents. Methods: The Heuristic method (HM) was employed to construct a 2D-linear QSAR model, while the gene expression programming (GEP) algorithm was utilized to develop a 2D-nonlinear QSAR model. Additionally, the CoMSIA approach was introduced to investigate the impact of drug structure on activity. A total of 200 novel anti-glioma dihydropteridone compounds were designed, and their activity levels were predicted using chemical descriptors and molecular field maps. The compounds with the highest activity were subjected to molecular docking to confirm their binding affinity. Results: Within the analytical purview, the coefficient of determination (R2) for the HM linear model is elucidated at 0.6682, accompanied by an R2 cv of 0.5669 and a residual sum of squares (S2) of 0.0199. The GEP nonlinear model delineates coefficients of determination for the training and validation sets at 0.79 and 0.76, respectively. Empirical modeling outcomes underscore the preeminence of the 3D-QSAR model, succeeded by the GEP nonlinear model, whilst the HM linear model manifested suboptimal efficacy. The 3D paradigm evinced an exemplary fit, characterized by formidable Q2 (0.628) and R2 (0.928) values, complemented by an impressive F-value (12.194) and a minimized standard error of estimate (SEE) at 0.160. The most significant molecular descriptor in the 2D model, which included six descriptors, was identified as "Min exchange energy for a C-N bond" (MECN). By combining the MECN descriptor with the hydrophobic field, suggestions for the creation of novel medications were generated. This led to the identification of compound 21E.153, a novel dihydropteridone derivative, which exhibited outstanding antitumor properties and docking capabilities. Conclusion: The development of 2D and 3D-QSAR models, along with the innovative integration of contour maps and molecular descriptors, offer novel concepts and techniques for the design of glioblastoma chemotherapeutic agents.

3D- and 2D-QSAR models’ study and molecular docking of novel nitrogen-mustard compounds for osteosarcoma

Background: The dipeptide-alkylated nitrogen-mustard compound is a new kind of nitrogen-mustard derivative with a strong anti-tumor activity, which can be used as a potential anti-osteosarcoma chemotherapy drug.

Objective: 2D- and 3D-QSAR (structure–activity relationship quantification) models were established to predict the anti-tumor activity of dipeptide-alkylated nitrogen-mustard compounds.

Method: In this study, a linear model was established using a heuristic method (HM) and a non-linear model was established using the gene expression programming (GEP) algorithm, but there were more limitations in the 2D model, so a 3D-QSAR model was introduced and established through the CoMSIA method. Finally, a series of new dipeptide-alkylated nitrogen-mustard compounds were redesigned using the 3D-QSAR model; docking experiments were carried out on several compounds with the highest activity against tumors.

Result: The 2D- and 3D-QSAR models obtained in this experiment were satisfactory. A linear model with six descriptors was obtained in this experiment using the HM through CODESSA software, where the descriptor “Min electroph react index for a C atom” has the greatest effect on the compound activity; a reliable non-linear model was obtained using the GEP algorithm model (the best model was generated in the 89th generation cycle, with a correlation coefficient of 0.95 and 0.87 for the training and test set, respectively, and a mean error of 0.02 and 0.06, respectively). Finally, 200 new compounds were designed by combining the contour plots of the CoMSIA model with each other, together with the descriptors in the 2D-QSAR, among which compound I1.10 had a high anti-tumor and docking ability.

Conclusion: Through the model established in this study, the factors influencing the anti-tumor activity of dipeptide-alkylated nitrogen-thaliana compounds were revealed, providing direction and guidance for the further design of efficient chemotherapy drugs against osteosarcoma.

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma

Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children's eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents. Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models. Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed. Result: The best linear model created using HM had an R², S², and R²cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q² (0.503), R² (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model's reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability. Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.

3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma

Background: Quinazolines are an important class of benzopyrimidine heterocyclic compounds with a promising antitumor activity that can be used for the design and development of osteosarcoma target compounds. Objective: To predict the compound activity of quinazoline compounds by constructing 2D- and 3D-QSAR models, and to design new compounds according to the main influencing factors of compound activity in the two models. Methods: First, heuristic method and GEP (gene expression programming) algorithm were used to construct linear and non-linear 2D-QSAR models. Then a 3D-QSAR model was constructed using CoMSIA method in SYBYL software package. Finally, new compounds were designed according to molecular descriptors of 2D-QSAR model and contour maps of 3D-QSAR model. Several compounds with optimal activity were used for docking experiments with osteosarcoma related targets (FGFR4). Results: The non-linear model constructed by GEP algorithm was more stable and predictive than the linear model constructed by heuristic method. A 3D-QSAR model with high Q² (0.63) and R ² (0.987) values and low error values (0.05) was obtained in this study. The success of the model fully passed the external validation formula, proving that the model is very stable and has strong predictive power. 200 quinazoline derivatives were designed according to molecular descriptors and contour maps, and docking experiments were carried out for the most active compounds. Compound 19g.10 has the best compound activity with good target binding capability. Conclusion: To sum up, the two novel QSAR models constructed were very reliable. The combination of descriptors in 2D-QSAR with COMSIA contour maps provides new design ideas for future compound design in osteosarcoma.

An overview of kinase downregulators and recent advances in discovery approaches

Since the clinical approval of imatinib, the discovery of protein kinase downregulators entered a prosperous age. However, challenges still exist in the discovery of kinase downregulator drugs, such as the high failure rate during development, side effects, and drug-resistance problems. With the progress made through multidisciplinary efforts, an increasing number of new approaches have been applied to solve the above problems during the discovery process of kinase downregulators. In terms of in vitro and in vivo drug evaluation, progress was also made in cellular and animal model platforms for better and more clinically relevant drug assessment. Here, we review the advances in drug design strategies, drug property evaluation technologies, and efficacy evaluation models and technologies. Finally, we discuss the challenges and perspectives in the development of kinase downregulator drugs.

3D-QSAR and Molecular Docking Studies on Design Anti-Prostate Cancer Curcumin Analogues

BACKGROUND

Prostate cancer is one of the most common tumors in the world and the fifth leading cause of male cancer death. Although the treatment of localized androgen-dependent prostate cancer has been successful, the efficacy of androgen-independent metastatic disease is limited. Curcumin, a natural product, has been found to inhibit the proliferation of prostate cancer cells.

OBJECTIVE

To design curcumin analogs with higher biological activity and lower toxicity and side effects for the treatment of prostate cancer.

METHODS

In this study, the three dimensional-quantitative structure activity relationship (3DQSAR) and molecular docking studies were performed on 34 curcumin analogs as anti-prostate cancer compounds. We introduced OSIRIS Property Explorer to predict drug-related properties of newly designed compounds.

RESULTS

The optimum CoMSIA model exhibited statistically significant results: the cross-validated correlation coefficient q2 is 0.540 and non-cross-validated R2 value is 0.984. The external predictive correlation coefficient Rext 2 is 0.792. The information of structure-activity relationship can be obtained from the CoMSIA contour maps. In addition, the molecular docking study of the compounds for 3ZK6 as the protein target revealed important interactions between active compounds and amino acids.

CONCLUSION

Compound 28i may be a new type of anti-prostate cancer drug with higher biological activity and more promising development.

Nickel(II) Chromite Nanoparticles: An Eco-Friendly and Reusable Catalyst for Synthesis of 2,4-Diamino-6-aryl-pyrimidine-5-yl Cyanides under Ultrasonic Radiation

BACKGROUND

One of the principal factors in the field of research in green chemistry is to drive chemical reactions using ultrasonication as a versatile synthetic tool. Moreover, nanostructured metal salts occupy an important position as low cost, efficient, heterogeneous, and green catalysts in chemical reactions. Pyrimidine has also acquired significance because it is a core structure in a variety of natural and non-natural agents, many of which display versatile biological activities and medical applications.

OBJECTIVE

The aim of this study was to explore the role of nickel(II) chromite nanoparticles (NiCr₂O₄ NPs) as a green and recyclable catalyst for the synthesis of 2,4-diamino-6-arylpyrimidine- 5-yl cyanides under ultrasonic radiation.

METHODS

A direct cyclocondensation reaction of guanidine nitrate, aromatic aldehydes, and malononitrile was performed using NiCr₂O₄ NPs as an effective heterogeneous catalyst under ultrasonic radiation at room temperature conditions to prepare 2,4-diamino-6-aryl-pyrimidine-5-yl cyanides in high yields. The described catalyst was prepared successfully according to a simple hydrothermal route and fully characterized by the X-Ray diffraction (XRD) technique, dispersive energy X-Ray (EDS) analysis, scanning electron microscopy (SEM), and dynamic light scattering (DLS).

RESULTS

A number of 2,4-diamino-6-aryl-pyrimidine-5-yl cyanides were effectively synthesized in high yields (94-98%) within short reaction times (15 min). All synthesized compounds were well characterized by IR, ¹H and ¹³C NMR spectroscopy, and also by elemental analyses.

CONCLUSION

In conclusion, a simple, efficient, and green synthesis of 2,4-diamino-6-arylpyrimidine- 5-yl cyanides was developed using NiCr₂O₄ NPs as a green nanocatalyst, and under ultrasound radiation as a green tool. The mild reaction conditions, avoiding the use of toxic solvents or reagents, high atom economy, high yields, and simple workup are the attractive features of this new protocol.

Insight into structural requirements of ACE inhibitory dipeptides: QSAR and molecular docking studies

The angiotensin I-converting enzyme (ACE) has been found to exhibit inhibitory capability against blood pressure. Recently, several ACE inhibitors with different structures have been reported. In the present work, molecular modeling studies using quantitative structure-activity relationship (QSAR) and molecular docking simulations were carried out. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were firstly used to generate 3D-QSAR models. The results indicate that the best CoMFA model has [Formula: see text] = 0.504, [Formula: see text] = 0.5896, and the best CoMSIA model has [Formula: see text] = 0.525, [Formula: see text] = 0.5666. Furthermore, 2D-QSAR models developed by multiple linear regression/MLR, partial least squares regression/PLSR, and support vector machine regression/SVR methods provide highly significant squared correlation coefficient R_tr² values of 0.8380, 0.8650, and 0.8230, external validated correlation coefficient Q_te² of 0.8279, 0.8223, and 0.7255, respectively. The statistical results show satisfactory goodness-of-fit, robustness, and perfect external predictive performance. Moreover, molecular docking studies were employed to predict the binding mode between dipeptides and ACE receptor. The combination of QSAR studies and molecular docking indicates the requirement of certain physicochemical parameters for better ACE inhibitors.

Efficient synthesis of pyrido[2,3-d]pyrimidine-7-carboxylic acids catalyzed by a TiO2/SiO2 nanocomposite in aqueous media at room temperature

A novel and efficient synthesis of eight 5-aryl-1,3-dimethyl-2,4-dioxo-1,2,3,4,5,8-hexahydropyrido[2,3-d]pyrimidine-7-carboxylic acids using a TiO2/SiO2 nanocomposite with a molar ratio of 1:1 as a recyclable heterogeneous catalyst is described. The desired products, five of which are new, are formed in short reaction times (2–3 h) with high to excellent yields (94%–98%) under very moderate reaction conditions (room temperature, aqueous media).

QSAR modeling and in silico design of small-molecule inhibitors targeting the interaction between E3 ligase VHL and HIF-1α

Protein-protein interactions (PPIs) have attracted much attention recently because of their preponderant role in most biological processes. The prevention of the interaction between E3 ligase VHL and HIF-1[Formula: see text] may improve tolerance to hypoxia and ameliorate the prognosis of many diseases. To obtain novel potent inhibitors of VHL/HIF-1[Formula: see text] interaction, a series of hydroxyproline-based inhibitors were investigated for structural optimization using a combination of QSAR modeling and molecular docking. Here, 2D- and 3D-QSAR models were developed by genetic function approximation (GFA) and comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods, respectively. The top-ranked models with strict validation revealed satisfactory statistical parameters (CoMFA with [Formula: see text], 0.637; [Formula: see text], 0.955; [Formula: see text], 0.944; CoMSIA with [Formula: see text], 0.649; [Formula: see text], 0.954; [Formula: see text], 0.911; GFA with [Formula: see text], 0.721; [Formula: see text], 0.801; [Formula: see text], 0.861). The selected five 2D-QSAR descriptors were in good accordance with the 3D-QSAR results, and contour maps gave the visualization of feature requirements for inhibitory activity. A new diverse molecular database was created by molecular fragment replacement and BREED techniques for subsequent virtual screening. Eventually, 31 novel hydroxyproline derivatives stood out as potential VHL/HIF-1[Formula: see text] inhibitors with favorable predictions by the CoMFA, CoMSIA and GFA models. The reliability of this protocol suggests that it could also be applied to the exploration of lead optimization of other PPI targets.

In Silico Exploration of 1,7-Diazacarbazole Analogs as Checkpoint Kinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study, and Molecular Dynamics Simulations

Checkpoint kinase 1 (Chk1) is an important serine/threonine kinase with a self-protection function. The combination of Chk1 inhibitors and anti-cancer drugs can enhance the selectivity of tumor therapy. In this work, a set of 1,7-diazacarbazole analogs were identified as potent Chk1 inhibitors through a series of computer-aided drug design processes, including three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling, molecular docking, and molecular dynamics simulations. The optimal QSAR models showed significant cross-validated correlation q² values (0.531, 0.726), fitted correlation r² coefficients (higher than 0.90), and standard error of prediction (less than 0.250). These results suggested that the developed models possess good predictive ability. Moreover, molecular docking and molecular dynamics simulations were applied to highlight the important interactions between the ligand and the Chk1 receptor protein. This study shows that hydrogen bonding and electrostatic forces are key interactions that confer bioactivity.