3D-QSAR (CoMFA, CoMSIA, HQSAR and topomer CoMFA), MD simulations and molecular docking studies on purinylpyridine derivatives as B-Raf inhibitors for the treatment of melanoma cancer

Combining QSAR techniques, molecular docking, and molecular dynamics simulations to explore anti-tumor inhibitors targeting Focal Adhesion Kinase

Focal Adhesion Kinase (FAK) is an important target for tumor therapy and is closely related to tumor cell genesis and progression. In this paper, we selected 46 FAK inhibitors with anticancer activity in the pyrrolo pyrimidine backbone to establish 3D/2D-QSAR models to explore the relationship between inhibitory activity and molecular structure. We have established two ideal models, namely, the Topomer CoMFA model (q 2 = 0.715, r 2 = 0.984) and the Holographic Quantitative Structure-Activity Relationship (HQSAR) model (q 2 = 0.707, r 2 = 0.899). Both models demonstrate excellent external prediction capabilities.Based on the QSAR results, we designed 20 structurally modified novel compounds, which were subjected to molecular docking and molecular dynamics studies, and the results showed that the new compounds formed many robust interactions with residues within the active pocket and could maintain stable binding to the receptor proteins. This study not only provides a powerful screening tool for designing novel FAK inhibitors, but also presents a series of novel FAK inhibitors with high micromolar activity that can be used for further characterization. It provides a reference for addressing the shortcomings of drug metabolism and drug resistance of traditional FAK inhibitors, as well as the development of novel clinically applicable FAK inhibitors.

Identification of potential SPHK1 inhibitors based on structural optimization by molecular simulation

Sphingosine Kinase 1 (SPHK1), observed to be overexpressed in an array of human malignancies, plays a pivotal role in modulating essential cellular activities throughout the process of tumor formation. Consequently, SPHK1 represents a promising therapeutic target, offering novel approaches to tumor treatment. Here, the structure-activity relationship was researched by using CoMFA and CoMSIA models. Both CoMFA (q2=0.621; n = 10; r2=0.992) and CoMSIA (q2=0.585; n = 7; r2=0.967) demonstrated satisfactory predictive capabilities. The structure-activity relationship of the compounds was analyzed by the counter maps of various fields. Further on, the compounds were interfaced with SPHK1 using the Surfex-Dock method to elucidate their interactive characteristics. Findings reveal that the binding is predominantly reliant on van der Waals, carbon-hydrogen bonds and hydrophobic interactions. Furthermore, the potential activities and ADME/T properties of six novel compounds were predicted utilizing 3D-QSAR models and online tools. The newly designed compounds were validated to have better activities and suitable ADME/T properties. In addition, molecular dynamics (MD) simulation further revealed that key residues, such as Ala339, Ala170, Ala115, Asp81, Gly342, Phe288 Ser164, Phe188, Ile170, etc. This study offers a roadmap to the discovery and design of innovative SPHK1 inhibitors.

Three-Dimensional Quantitative Structure-Activity Relationship-Based Molecular Design through a Side Arm Strategy to Synthesize Phenylpyrazole Oxime Derivatives and Improve Their Insecticidal Activity and Photoself-Degradation

Three-dimensional quantitative structure-activity relationship (3D-QSAR) serves as one of the most important and effective tools to guide molecular design for the development of new pesticides. According to the principle of structural splicing, only changing a small group may lead to a great increase in activity while maintaining the active center unchanged. Under the guidance of 3D-QSAR, three series of phenylpyrazole oxime fluorescent insecticides acting on the GABA receptor, namely, esters (POEs), ethers (POETs), and triazoles (POTs), were designed through a side arm strategy and synthesized by an ultrasonic bath reaction, which were fully characterized and crystal-analyzed. The preliminary bioassay results indicated that the insecticidal activities of POE12 and POT2 against Plutella xylostella were 4.2 and 2.7 times higher than that of fipronil and better than that of the isolated Mythimna separata. Through the trend of insecticidal activity, the introduction of an aryl ring and an electron-withdrawing group in the substituted functional group of the side arm can enhance the insecticidal activity. Reversed-phase HPLC also confirmed that POEs and POTs had good lipid solubility, which was beneficial to improve their fluidity in the cell membrane. Through molecular packing, molecular docking, and Hirshfeld surface, the intermolecular interaction brought by side arms of POEs, POETs, and POTs and the strong interaction with GABA receptors were preliminarily verified. Photophysical tests revealed that the introduction of the side arm expanded the conjugated system and improved its light absorption and fluorescence. Under the irradiation of simulated sunlight, it was found that they had photoself-degradation and could be retransformed into the parent fragment of phenylpyrazole, thereby improving its biological activity and reducing residues.

Computational Simulation Study of Potential Inhibition of c-Met Kinase Receptor by Phenoxy pyridine Derivatives: Based on QSAR, Molecular Docking, Molecular Dynamics

The mesenchymal-epithelial transition factor (c-Met) is a tyrosine kinase receptor protein, and excessive cell transformation can lead to cancer. Therefore, there is an urgent need to develop novel receptor tyrosine kinase inhibitors by inhibiting the activity of c-Met protein. In this study, 41 compounds are selected from the reported literature, and the interactions between phenoxy pyridine derivatives and tumor-associated proteins are systematically investigated using a series of computer-assisted drug design (CADD) methods, aiming to predict potential c-Met inhibitors with high activity. The Topomer CoMFA (q2=0.620, R2=0.837) and HQSAR (q2=0.684, R2=0.877) models demonstrate a high level of robustness. Further internal and external validation assessments show high applicability and accuracy. Based on the results of the Topomer CoMFA model, structural fragments with higher contribution values are identified and randomly combined using a fragment splice technique, result in a total of 20 compounds with predicted activities higher than the template molecules. Molecular docking results show that these compounds have good interactions and van der Waals forces with the target proteins. The results of molecular dynamics and ADMET predictions indicate that compounds Y4, Y5, and Y14 have potential as c-Met inhibitors. Among them, compound Y14 exhibits superior stability with a binding free energy of -165.18 KJ/mol. These studies provide a reference for the future design and development of novel compounds with c-Met inhibitory activity.

QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking

VEGFR-2 kinase inhibitors are clinically approved drugs that can effectively target cancer angiogenesis. However, such inhibitors have adverse effects such as skin toxicity, gastrointestinal reactions and hepatic impairment. In this study, machine learning and Topomer CoMFA, which is an alignment-dependent, descriptor-based method, were employed to build structural activity relationship models of potentially new VEGFR-2 inhibitors. The prediction ac-curacy of the training and test sets of the 2D-SAR model were 82.4 and 80.1%, respectively, with KNN. Topomer CoMFA approach was then used for 3D-QSAR modeling of VEGFR-2 inhibitors. The coefficient of q2 for cross-validation of the model 1 was greater than 0.5, suggesting that a stable drug activity-prediction model was obtained. Molecular docking was further performed to simulate the interactions between the five most promising compounds and VEGFR-2 target protein and the Total Scores were all greater than 6, indicating that they had a strong hydrogen bond interactions were present. This study successfully used machine learning to obtain five potentially novel VEGFR-2 inhibitors to increase our arsenal of drugs to combat cancer.

Design of potential anti-cancer agents as COX-2 inhibitors, using 3D-QSAR modeling, molecular docking, oral bioavailability proprieties, and molecular dynamics simulation

Modeling the structural properties of novel morpholine-bearing 1, 5-diaryl-diazole derivatives as potent COX-2 inhibitor, two proposed models based on CoMFA and CoMSIA were evaluated by external and internal validation methods. Partial least squares analysis produced statistically significant models with Q 2 values of 0.668 and 0.652 for CoMFA and CoMSIA, respectively, and also a significant non-validated correlation coefficient R² with values of 0.882 and 0.878 for CoMFA and CoMSIA, respectively. Both models met the requirements of Golbraikh and Tropsha, which means that both models are consistent with all validation techniques. Analysis of the CoMFA and CoMSIA contribution maps and molecular docking revealed that the R1 substituent has a very significant effect on their biological activity. The most active molecules were evaluated for their thermodynamic stability by performing MD simulations for 100 ns; it was revealed that the designed macromolecular ligand complex with 3LN1 protein exhibits a high degree of structural and conformational stability. Based on these results, we predicted newly designed compounds, which have acceptable oral bioavailability properties and would have high synthetic accessibility.

Molecular dynamics, MMGBSA, and docking studies of natural products conjugated to tumor-targeted peptide for targeting BRAF V600E and MERTK receptors

Recent studies have revealed that MERTK and BRAF V600E receptors have been found to be over-expressed in several types of cancers including melanoma, making these receptors targets for drug design. In this study, we have designed novel peptide conjugates with the natural products vanillic acid, thiazole-2-carboxylic acid, cinnamic acid, theanine, and protocatechuic acid. Each of these compounds was conjugated with the tumor targeting peptide sequence TAASGVRSMH, known to bind to NG2 and target tumor neovasculature. We examined their binding affinities and stability with MERTK and BRAF V600E receptors using molecular docking and molecular dynamics studies. Compared to the neat compounds, the peptide conjugates displayed higher binding affinity toward both receptors. In the case of MERTK, the most stable complexes were formed with di-theaninate-peptide, vanillate-peptide, and thiazole-2-amido peptide conjugates and binding occurred in the hinge region. Additionally, it was discovered that the peptide alone also had high binding ability and stability with the MERTK receptor. In the case of BRAF V600E, the peptide conjugates of protocatechuate, vanillate and thiazole-2-amido peptide conjugates showed the formation of the most stable complexes and binding occurred in the ATP binding cleft. Further analysis revealed that the number of hydrogen bonds and hydrophobic interactions played a critical role in enhanced stability of the complexes. Docking studies also revealed that binding affinities for NG2 were similar to MERTK and higher for BRAF V600E. MMGBSA studies of the trajectories revealed that the protocatechuate-peptide conjugate showed the highest binding energy with BRAF V600E while the peptide-TAASGVRSMH showed the highest binding energy with MERTK. ADME studies revealed that each of the compounds showed medium to high permeability toward MDCK cells and were not hERG blockers. Furthermore, the conjugates were not CYP inhibitors or substrates, but they were found to be Pgp substrates. Our results indicated that the protocatechuate-TAASGVRSMH, thiazole-2-amido-TAASGVRSMH, and vanillate-TAASGVRSMH conjugates may be furthered developed for in vitro and in vivo studies as novel tumor targeting compounds for tumor cells over-expressing BRAF V600E, while di-theaninate-amido-TAASGVRSMH and thiazole-2-amido-TAASGVRSMH conjugates may be developed for targeting MERTK receptors. These studies provide insight into the molecular interactions of natural product-peptide conjugates and their potential for binding to and targeting MERTK and BRAF V600E receptors in developing new therapeutics for targeting cancer.

Insights into the structural requirements of triazole derivatives as promising DPP IV inhibitors: computational investigations

Diabetes is one of the leading causes of death globally as per World Health Organization 2019. To cope up with side effects of current diabetes therapy, researchers have found several novel targets for the treatment of diabetes. Currently, dipeptidyl peptidase IV (DPP IV) has emerged as a target in modulating the diabetes physiology. In the present work, various 3D-Quantitative structure activity relationship (QSAR) techniques namely comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis, topomer CoMFA and molecular hologram QSAR are used to explore the structural requirements of triazole derivatives as DPP IV inhibitors. Different models generated by 3D QSAR studies had acceptable statistical values for further prediction of molecules. From the contour maps of QSAR results, important structural features are deduced. Substitutions on N₁ and N₂ of triazole ring with H-bond donor group enhances the biological activity. Aliphatic side chain, less bulky group, H-bond donor group and -COOH group on N₃ of triazole ring are vital for the DPP IV inhibition. Moreover, electron withdrawing side chain on the triazole ring improves the biological activity. Further, novel triazole derivatives were designed and docking results of these compounds proved the efficiency of the developed 3D QSAR model. In future, results of this study may provide promising DPP IV inhibitors for the treatment of diabetes. Communicated by Ramaswamy H. Sarma.

Research on the anti-tumor activity of a novel aminopeptidase inhibitor based on 3D QSAR model

Background:

Aminopeptidase N (APN) is a type II transmembrane zinc ion-dependent metalloprotease. It is closely related to many processes of tumor occurrence and development, such as the formation of new blood vessels and tumor metastasis. Recent studies have shown that APN is a member of the family of surface markers of liver cancer stem cells. Therefore, APN small molecule inhibitors may have multiple compound functions, exerting multiple anti-tumor effects at multiple stages of cancer occurrence and development.

Methods:

Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) approaches.

Results:

Both internal and external cross-validation were conducted to obtain high predictive and satisfactory CoMFA model (q2 = 0.627, r2 =0.995, SEE = 0.043) and CoMSIA model (q2 = 0.575, r2 = 0.998, SEE = 0.031). The statistical results obtained from CoMFA and CoMSIA models were full of credibility and remarkable predictive power.

Conclusion:

The results of 3D-QSAR are reliable and significant with high predictive (q2) ability, and a lower value of the standard error of estimation indicates a good correlation between predicted and observed activity. All these results revealed many useful structural insights to improve the activity of the newly designed APN small molecule inhibitors.

Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning

A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inhibitors were collected from the databases of ChEMBL, Binding Database, DrugBank, and PubMed, as well as from 40 references. The database was divided into the training set and test set by random sampling. By exploring the correlation between molecular descriptors and inhibitory activity, it is found that the classification and specific activity data of inhibitors can be more accurately predicted by the combination of molecular descriptors and molecular fingerprints. The calculation of molecular fingerprint descriptor provides the additional substructure information to improve the prediction ability. Based on the training set, two machine learning methods, the recursive partition (RP) and naive Bayes (NB) models, were used to build the classifiers of HIV-1 IN inhibitors. Through the test set verification, the RP technique accurately predicted 82.5% inhibitors and 86.3% noninhibitors. The NB model predicted 88.3% inhibitors and 87.2% noninhibitors with correlation coefficient of 85.2%. The results show that the prediction performance of NB model is slightly better than that of RP, and the key molecular segments are also obtained. Additionally, CoMFA and CoMSIA models with good activity prediction ability both were constructed by exploring the structure-activity relationship, which is helpful for the design and optimization of HIV-1 IN inhibitors.

Structural modification of 4, 5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives as BRD4 inhibitors using 2D/3D-QSAR and molecular docking analysis

Cancer treatment continues to be one of the most serious public health issues in the world. The overexpression of BRD4 protein has led to a series of malignant tumors, hence the development of small molecule BRD4 protease inhibitors has always been a hot spot in the field of medical research. In this study, a series of 4,5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives were used to establish 3D/2D-QSAR models and to discuss the relationship between inhibitor structure and activity. Four ideal models were established, including the comparative molecular field analysis (CoMFA: [Formula: see text] = 0.574, [Formula: see text] = 0.947) model, comparative molecular similarity index analysis (CoMSIA: [Formula: see text]= 0.622, [Formula: see text] = 0.916) model, topomer CoMFA ([Formula: see text] = 0.691, [Formula: see text]= 0.912) model and hologram quantitative structure-activity relationship (HQSAR: [Formula: see text]= 0.759, [Formula: see text] = 0.963) model. They show quite good external predictive power for the test set, with [Formula: see text] values of 0.602, 0.624, 0.671 and 0.750, respectively. In addition, the contour and color code map given by the 2D/3D-QSAR model with the results of molecular docking analyzed to chalk up modification methods for improving inhibitory activity, which was verified by designing novel compounds. The analysis results are helpful to promote the modification of the inhibitor framework and to provide a reference for the construction of new and promising BRD4 inhibitor compounds.

Distributed and multicore QuBiLS-MIDAS software v2.0: Computing chiral, fuzzy, weighted and truncated geometrical molecular descriptors based on tensor algebra

Advances to the distributed, multi-core and fully cross-platform QuBiLS-MIDAS software v2.0 (http://tomocomd.com/qubils-midas) are reported in this article since the v1.0 release. The QuBiLS-MIDAS software is the only one that computes atom-pair and alignment-free geometrical MDs (3D-MDs) from several distance metrics other than the Euclidean distance, as well as alignment-free 3D-MDs that codify structural information regarding the relations among three and four atoms of a molecule. The most recent features added to the QuBiLS-MIDAS software v2.0 are related (a) to the calculation of atomic weightings from indices based on the vertex-degree invariant (e.g., Alikhanidi index); (b) to consider central chirality during the molecular encoding; (c) to use measures based on clustering methods and statistical functions to codify structural information among more than two atoms; (d) to the use of a novel method based on fuzzy membership functions to spherically truncate inter-atomic relations; and (e) to the use of weighted and fuzzy aggregation operators to compute global 3D-MDs according to the importance and/or interrelation of the atoms of a molecule during the molecular encoding. Moreover, a novel module to compute QuBiLS-MIDAS 3D-MDs from their headings was also developed. This module can be used either by the graphical user interface or by means of the software library. By using the library, both the predictive models built with the QuBiLS-MIDAS 3D-MDs and the QuBiLS-MIDAS 3D-MDs calculation can be embedded in other tools. A set of predefined QuBiLS-MIDAS 3D-MDs with high information content and low redundancy on a set comprised of 20,469 compounds is also provided to be employed in further cheminformatics tasks. This set of predefined 3D-MDs evidenced better performance than all the universe of Dragon (v5.5) and PaDEL 0D-to-3D MDs in variability studies, whereas a linear independence study proved that these QuBiLS-MIDAS 3D-MDs codify chemical information orthogonal to the Dragon 0D-to-3D MDs. This set of predefined 3D-MDs would be periodically updated as long as new results be achieved. In general, this report highlights our continued efforts to provide a better tool for a most suitable characterization of compounds, and in this way, to contribute to obtaining better outcomes in future applications.