Cancer is often associated with an aberrant increase in tubulin and microtubule activity required for cell migration, invasion, and metastasis. A new series of fatty acid conjugated chalcones have been designed as tubulin polymerization inhibitors and anticancer candidates. These conjugates were designed to harness the beneficial physicochemical properties, ease of synthesis, and tubulin inhibitory activity of two classes of natural components. New lipidated chalcones were synthesized from 4-aminoacetophenone via N-acylation followed by condensation with different aromatic aldehydes. All new compounds showed strong inhibition of tubulin polymerization and antiproliferative activity against breast and lung cancer cell lines (MCF-7 and A549) at low or sub-micromolar concentrations. A significant apoptotic effect was shown using a flow cytometry assay that corresponded to cytotoxicity against cancer cell lines, as indicated by a 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide assay. Decanoic acid conjugates were more potent than longer lipid analogues, with the most active being more potent than the reference tubulin inhibitor, combretastatin-A4 and the anticancer drug, doxorubicin. None of the newly synthesized compounds caused any detectable cytotoxicity against the normal cell line (Wi-38) or hemolysis of red blood cells below 100 μM. It is unlikely that the new conjugates described would affect normal cells or interrupt with cell membranes due to their lipidic nature. A quantitative structure-activity relationship analysis was performed to determine the influence of 315 descriptors of the physicochemical properties of the new conjugates on their tubulin inhibitory activity. The obtained model revealed a strong correlation between the tubulin inhibitory activity of the investigated compounds and their dipole moment and degree of reactivity.
Benzyl-o-vanillin and benzimidazole nucleus serve as important pharmacophore in drug discovery. The benzyl vanillin (2-(benzyloxy)-3-methoxybenzaldehyde) compound shows anti-proliferative activity in HL60 leukemia cancer cells and can effect cell cycle progression at G2/M phase. Its apoptosis activity was due to disruption of mitochondrial functioning. In this study, we have studied a series of compounds consisting of benzyl vanillin and benzimidazole structures. We hypothesize that by fusing these two structures we can produce compounds that have better anticancer activity with improved specificity particularly towards the leukemia cell line. Here we explored the anticancer activity of three compounds namely 2-(2-benzyloxy-3-methoxyphenyl)-1H-benzimidazole, 2MP, N-1-(2-benzyloxy-3-methoxybenzyl)-2-(2-benzyloxy-3-methoxyphenyl)-1H-benzimidazole, 2XP, and (R) and (S)-1-(2-benzyloxy-3-methoxyphenyl)-2, 2, 2-trichloroethyl benzenesulfonate, 3BS and compared their activity to 2-benzyloxy-3-methoxybenzaldehyde, (Bn1), the parent compound. 2XP and 3BS induces cell death of U937 leukemic cell line through DNA fragmentation that lead to the intrinsic caspase 9 activation. DNA binding study primarily by the equilibrium binding titration assay followed by the Viscosity study reveal the DNA binding through groove region with intrinsic binding constant 7.39 µM/bp and 6.86 µM/bp for 3BS and 2XP respectively. 2XP and 3BS showed strong DNA binding activity by the UV titration method with the computational drug modeling showed that both 2XP and 3BS failed to form any electrostatic linkages except via hydrophobic interaction through the minor groove region of the nucleic acid. The benzylvanillin alone (Bn1) has weak anticancer activity even after it was combined with the benzimidazole (2MP), but after addition of another benzylvanillin structure (2XP), stronger activity was observed. Also, the combination of benzylvanillin with benzenesulfonate (3BS) significantly improved the anticancer activity of Bn1. The present study provides a new insight of benzyl vanillin derivatives as potential anti-leukemic agent.
Topological indices are numerical values that can be analysed to predict the chemical properties of the molecular structure and the topological indices are computed for a graph related to groups. Meanwhile, the conjugacy class graph of is defined as a graph with a vertex set represented by the non-central conjugacy classes of . Two distinct vertices are connected if they have a common prime divisor. The main objective of this article is to find various topological indices including the Wiener index, the first Zagreb index and the second Zagreb index for the conjugacy class graph of dihedral groups of order where the dihedral group is the group of symmetries of regular polygon, which includes rotations and reflections. Many topological indices have been determined for simple and connected graphs in general but not graphs related to groups. In this article, the Wiener index and Zagreb index of conjugacy class graph of dihedral groups are generalized.
Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.
One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized methods have drawbacks such as having biases and inconsistencies that make they lack the oracle properties. This paper proposes an adaptive penalized logistic regression (APLR) to overcome these drawbacks. This is done by employing a ratio (BWR) of the descriptors between-groups sum of squares (BSS) to the within-groups sum of squares (WSS) for each descriptor as a weight inside the L1-norm. The proposed method was applied to one dataset that consists of a diverse series of antimicrobial agents with their respective bioactivities against Candida albicans. By experimental study, it has been shown that the proposed method (APLR) was more efficient in the selection of descriptors and classification accuracy than the other competitive methods that could be used in developing QSAR classification models. Another dataset was also successfully experienced. Therefore, it can be concluded that the APLR method had significant impact on QSAR analysis and studies.
Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter, μ , is added in the original transfer function as a time-varying variable. The TVBGSA-based model was internally and externally validated based on
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An improved binary differential search (improved BDS) algorithm is proposed for QSAR classification of diverse series of antimicrobial compounds against Candida albicans inhibitors. The transfer functions is the most important component of the BDS algorithm, and converts continuous values of the donor into discrete values. In this paper, the eight types of transfer functions are investigated to verify their efficiency in improving BDS algorithm performance in QSAR classification. The performance was evaluated using three metrics: classification accuracy (CA), geometric mean of sensitivity and specificity (G-mean), and area under the curve. The Kruskal-Wallis test was also applied to show the statistical differences between the functions. Two functions, S1 and V4, show the best classification achievement, with a slightly better performance of V4 than S1. The V4 function takes the lowest iterations and selects the fewest descriptors. In addition, the V4 function yields the best CA and G-mean of 98.07% and 0.977%, respectively. The results prove that the V4 transfer function significantly improves the performance of the original BDS.
High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.
A series of novel carbonyl compounds was synthesized by a simple, eco-friendly and efficient method. These compounds were screened for anti-oxidant activity, in vitro cytotoxicity and for inhibitory activity for acetylcholinesterase and butyrylcholinesterase. The effect of these compounds against amyloid β-induced cytotoxicity was also investigated. Among them, compound 14 exhibited strong free radical scavenging activity (18.39 μM) while six compounds (1, 3, 4, 13, 14, and 19) were found to be the most protective against Aβ-induced neuronal cell death in PC12 cells. Compounds 4 and 14, containing N-methyl-4-piperidone linker, showed high acetylcholinesterase inhibitory activity as compared to reference drug donepezil. Molecular docking and QSAR (Quantitative Structure-Activity Relationship) studies were also carried out to determine the structural features that are responsible for the acetylcholinesterase and butyrylcholinesterase inhibitory activity.
The mammalian hyaluronidase degrades hyaluronic acid by the cleavage of the β-1,4-glycosidic bond furnishing a tetrasaccharide molecule as the main product which is a highly angiogenic and potent inducer of inflammatory cytokines. Ursolic acid 1, isolated from Prismatomeris tetrandra, was identified as having the potential to develop inhibitors of hyaluronidase. A series of ursolic acid analogues were either synthesized via structure modification of ursolic acid 1 or commercially obtained. The evaluation of the inhibitory activity of these compounds on the hyaluronidase enzyme was conducted. Several structural, topological and quantum chemical descriptors for these compounds were calculated using semi empirical quantum chemical methods. A quantitative structure activity relationship study (QSAR) was performed to correlate these descriptors with the hyaluronidase inhibitory activity. The statistical characteristics provided by the best multi linear model (BML) (R² = 0.9717, R²cv = 0.9506) indicated satisfactory stability and predictive ability of the developed model. The in silico molecular docking study which was used to determine the binding interactions revealed that the ursolic acid analog 22 had a strong affinity towards human hyaluronidase.
A series of 21 compounds isolated from Curcuma zedoaria was subjected to cytotoxicity test against MCF7; Ca Ski; PC3 and HT-29 cancer cell lines; and a normal HUVEC cell line. To rationalize the structure-activity relationships of the isolated compounds; a set of electronic; steric and hydrophobic descriptors were calculated using density functional theory (DFT) method. Statistical analyses were carried out using simple and multiple linear regressions (SLR; MLR); principal component analysis (PCA); and hierarchical cluster analysis (HCA). SLR analyses showed that the cytotoxicity of the isolated compounds against a given cell line depend on certain descriptors; and the corresponding correlation coefficients (R2) vary from 0%-55%. MLR results revealed that the best models can be achieved with a limited number of specific descriptors applicable for compounds having a similar basic skeleton. Based on PCA; HCA and MLR analyses; active compounds were classified into subgroups; which was in agreement with the cell based cytotoxicity assay.
In high-dimensional quantitative structure-activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.
A robust screening approach and a sparse quantitative structure-retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed modified robust sure independence screening (MR-SIS) method. Second, prediction of RIs was made using the proposed robust sparse QSRR with smoothly clipped absolute deviation (SCAD) penalty (RSQSRR). The RSQSRR model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], Y-randomization test, [Formula: see text], [Formula: see text], and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of the RSQSRR for training dataset outperform the other two used modelling methods. The RSQSRR shows the highest [Formula: see text], [Formula: see text], and [Formula: see text], and the lowest [Formula: see text]. For the test dataset, the RSQSRR shows a high external validation value ([Formula: see text]), and a low value of [Formula: see text] compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed RSQSRR is an efficient approach for modelling high dimensional QSRRs and the method is useful for the estimation of RIs of essential oils that have not been experimentally tested.
A penalized quantitative structure-property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ([Formula: see text]) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ([Formula: see text]). The PBridge based model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], the Y-randomization test, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest [Formula: see text] of 0.959, [Formula: see text] of 0.953, [Formula: see text] of 0.949 and [Formula: see text] of 0.959, and the lowest [Formula: see text] and [Formula: see text]. For the test dataset, PBridge shows a higher [Formula: see text] of 0.945 and [Formula: see text] of 0.948, and a lower [Formula: see text] and [Formula: see text], indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.
Two novel and highly accurate hybrid models were developed for the prediction of the flammability limits (lower flammability limit (LFL) and upper flammability limit (UFL)) of pure compounds using a quantitative structure-property relationship approach. The two models were developed using a dataset obtained from the DIPPR Project 801 database, which comprises 1057 and 515 literature data for the LFL and UFL, respectively. Multiple linear regression (MLR), logarithmic, and polynomial models were used to develop the models according to an algorithm and code written using the MATLAB software. The results indicated that the proposed models were capable of predicting LFL and UFL values with accuracies that were among the best (i.e. most optimised) reported in the literature (LFL: R2 = 99.72%, with an average absolute relative deviation (AARD) of 0.8%; UFL: R2 = 99.64%, with an AARD of 1.41%). These hybrid models are unique in that they were developed using a modified mathematical technique combined three conventional methods. These models afford good practicability and can be used as cost-effective alternatives to experimental measurements of LFL and UFL values for a wide range of pure compounds.
A series of 1-(2,3-dihydro-1H-indan-1-yl)-3-aryl urea/thiourea derivatives (4a-j) have been synthesized from the reaction of 2,3-dihydro-1H-inden-1-amine (2) with various aryl isocyanates/isothiocyanates (3a-j) by using N,N-DIPEA base (Hunig's base) catalyst in THF at reflux conditions. All of them are structurally confirmed by spectral (IR, 1H &13C NMR and MASS) and elemental analysis and screened for their in-vitro antioxidant activity against DPPH and NO free radicals and found that compounds 4b, 4i, 4h &4g are potential antioxidants. The obtained in vitro results were compared with the molecular docking, ADMET, QSAR and bioactivity study results performed for them and identified that the recorded in silico binding affinities were observed in good correlation with the in vitro antioxidant results. The Molecular docking analysis had unveiled the strong hydrogen bonding interactions of synthesized ligands with ARG 160 residue of protein tyrosine kinase (2HCK) enzyme and plays an effective role in its inhibition. Toxicology studies have assessed the potential risks of 4a-j and inferred that all of them were in the limits of potential drugs. The conformational analysis of 4a-j inferred that the urea/thiourea spacer linking 2,3-dihydro-1H-inden-1-amino and substituted aryl units has facilitated all these molecules to effectively bind with ARG 160 amino acid residue present on the α-helix of the protein tyrosine kinase (2HCK) enzyme specifically on chain A of hemopoetic cell kinase. Collectively this study has established a relationship between the antioxidant potentiality and ligands binding with ARG 160 amino acid residue of chain A of 2HCK enzyme to inhibit its growth as well as proliferation of reactive oxygen species in vivo.
Polo-like kinase 1 (PLK1) and p38γ mitogen-activated protein kinase (p38γ) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug-drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure-activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38γ on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38γ. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38γ activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.
A series of novel α-furfuryl-2-alkylaminophosphonates have been efficiently synthesized from the one-pot three-component classical Kabachnik-Fields reaction in a green chemical approach by addition of an in situ generated dialkylphosphite to Schiff's base of aldehydes and amines by using environmental and eco-friendly silica gel supported iodine as a catalyst by microwave irradiation. The advantage of this protocol is simplicity in experimental procedures and products were resulted in high isolated yields. The synthesized α-furfuryl-2-alkylaminophosphonates were screened to in vitro antioxidant and plant growth regulatory activities and some are found to be potent with antioxidant and plant growth regulatory activities. These in vitro studies have been further supported by ADMET (absorption, distribution, metabolism, excretion, and toxicity), quantitative structure-activity relationship, molecular docking, and bioactivity studies and identified that they were potentially bound to the GLN340 amino acid residue in chain C of 1DNU protein and TYR597 amino acid residue in chain A of 4M7E protein, causing potential exhibition of antioxidant and plant growth regulatory activities. Eventually, title compounds are identified as good blood-brain barrier (BBB)-penetrable compounds and are considered as proficient central nervous system active and neuroprotective antioxidant agents as the neuroprotective property is determined with BBB penetration thresholds.
A series of 3-amino-2-hydroxybenzofused 2-phosphalactones (4a-l) has been synthesized from the Kabachnik-Fields reaction via a facile route from a one-pot three-component reaction of diphenylphosphite with various 2-hydroxybenzaldehyes and heterocyclic amines in a new way of expansion. The in vitro anti-cell proliferation studies by MTT assay have revealed them as potential Panc-1, Miapaca-2, and BxPC-3 pancreatic cell growth inhibitors, and the same is supported by molecular docking, QSAR, and ADMET studies. The MTT assay of their SAHA derivatives against the same cell lines evidenced them as potential HDAC inhibitors and identified 4a, 4b, and 4k substituted with 1,3-thiazol, 1,3,4-thiadiazol, and 5-sulfanyl-1,3,4-thiadiazol moieties on phenyl and diethylamino phenyl rings as potential ones. Additionally, the flow cytometric analyses of 4a, 4b, and 4k against BxPC-3 cells revealed compound 4k as a lead compound that arrests the S phase cell cycle growth at low micromolar concentrations. The ADMET properties have ascertained their inherent pharmacokinetic potentiality, and the wholesome results prompted us to report it as the first study on anti-pancreatic cancer activity of cyclic α-aminophosphonates. Ultimately, this study serves as a good contribution to update the existing knowledge on the anticancer organophosphorus heterocyclic compounds and elevates the scope for generation of new anticancer drugs. Further, the studies like QSAR, drug properties, toxicity risks, and bioactivity scores predicted for them have ascertained the synthesized compounds as newer and potential drug candidates. Hence, this study had augmented the array of α-aminophosphonates by adding a new collection of 3-amino-2-hydroxybenzofused 2-phosphalactones, a class of cyclic α-aminophosphonates, to it, which proved them as potential anti-pancreatic cancer agents.
Current in silico modelling techniques, such as molecular dynamics, typically focus on compounds with the highest concentration from chromatographic analyses for bioactivity screening. Consequently, they reduce the need for labour-intensive in vitro studies but limit the utilization of extensive chromatographic data and molecular diversity for compound classification. Compound permeability across the blood-brain barrier (BBB) is a key concern in central nervous system (CNS) drug development, and this limitation can be addressed by applying cheminformatics with codeless machine learning (ML). Among the four models developed in this study, the Random Forest (RF) algorithm with the most robust performance in both internal and external validation was selected for model construction, with an accuracy (ACC) of 87.5% and 86.9% and area under the curve (AUC) of 0.907 and 0.726, respectively. The RF model was deployed to classify 285 compounds detected using liquid chromatography quadrupole time-of-flight mass spectrometry (LCQTOF-MS) in Kelulut honey; of which, 140 compounds were screened with 94 descriptors. Seventeen compounds were predicted to permeate the BBB, revealing their potential as drugs for treating neurodegenerative diseases. Our results highlight the importance of employing ML pattern recognition to identify compounds with neuroprotective potential from the entire pool of chromatographic data.