Current computer-aided drug design最新文献

Background: The compounds containing heterocyclic cores with O, N and/or S atoms are bioactive and valuable molecules in the field of drug discovery and development. There are several applications in different areas for the molecules having oxadiazole moiety in their structures viz. herbicides and corrosion inhibitors, electron-transport materials, polymers and luminescent materials. Hence, demand for new anticonvulsant, antibacterial and analgesic agents has turned into an imperative assignment in the area of medicinal chemistry to improve therapeutic efficacy as well as safety.

Methods: In the journey of new anticonvulsive, antibacterial and analgesic molecules with better potency, some newer Oxadiazole analogues were attained by a sequence of synthetic steps with the substituted acrylic acids. IR and ¹H-NMR spectral data were used for the structure elucidation of obtained chemical compounds. In this perspective, the anticonvulsant, antibacterial and analgesic activities were evaluated for synthetically obtained newer chemical moieties. Furthermore, a molecular docking study was performed to elucidate the binding modes of synthesized ligands in the active pockets of Cox-1/2 enzymes, DNA Gyrase and GABA inhibitors.

Results: It has been observed that all the synthetic molecules showed good analgesic activity while A1 molecule demonstrated better analgesic activity. In the case of anticonvulsant and antibacterial activity among other ligands, C1 molecule possessed profound anticonvulsant activity whereas B1 molecule showed maximum antibacterial activity and molecular docking study also endorsed the same consequences.

Conclusion: It might be recognized from the present study that prepared compounds are distinctive in lieu of their structure and noticeable biological activity. In the quest for a newer group of anticonvulsant, antibacterial and analgesic molecules, these compounds might be useful for the society.

Objective: Parkinson's disease (PD) and Alzheimer's disease (AD) are the most common forms of neurodegenerative disorders. The aim of the current work is to study the potential of some new indanone derivatives for the treatment of these neurological disorders.

Methods: A new series of 4-(2-oxo-2-aminoethoxy)-2-benzylidene substituted indanone derivatives have been synthesized and studied for anti-Parkinsonian and anti-Alzheimer's effects. Substitution of different aminoalkyl functionalities at the para position of 2-benzylidene moiety of indanone ring resulted in the formation of potent anti-parkinsonian and anti-Alzheimer's agents (5-10). The neuroprotective effects of newly synthesized compounds were evaluated using perphenazine (PPZ)-induced catatonia in rats and LPS-induced cognitive deficits in mice models. Further, in silico molecular modelling studies of the new indanone derivatives were performed by docking against the 3D structures of various neuroinflammatory mediators, such as interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α) and monoamine oxidase-B (MAO-B), to gain the mechanistic insights of their anti-Alzheimer's and antiparkinsonian effects.

Results: The newly synthesized indanone analogues 5-10 were found effective against PPZinduced motor dysfunction and LPS-induced memory impairment in animal models. Among all the synthesized analogues, morpholine-substituted indanone 9 displayed maximum anti-parkinsonian activity, even better than the standard drug L-DOPA, while pyrrolidine and piperidine substituted analogues 5 and 6 were found to be the most potent anti-Alzheimer's agents.

Conclusion: The new 2-arylidene-1-indanone analogues show good potential as promising leads for designing compounds against Parkinson's and Alzheimer's diseases.

Background: Obesity has now become a global issue due to the increase in the population of obese people. It also substantially impacts the individual's social, financial, and psychological well-being, which may contribute to depression. Being overweight induces many metabolic and chronic disorders, urging many researchers to focus on developing the drug for obesity treatment. Pancreatic lipase inhibitors and natural product/compound-derived pancreatic lipase inhibitors have recently received much attention because of their structural variety and low toxicity.

Objective: This study aimed to build pharmacophores and QSAR for analyzing the necessary structure of pancreatic lipase inhibitors and designing new molecules with the best activity.

Methods: Ligand-based pharmacophore modeling and Atom-Based 3D-QSAR were carried out using the PHASE module of Schrodinger to determine the critical structural properties necessary for pancreatic lipase (PL) inhibitory activity. A total of 157 phytoconstituents and a standard drug, orlistat, were selected for the present study. Considering the important features for pancreatic lipase inhibition, 15 new molecules were designed and subjected to molecular docking studies and molecular dynamics simulations. The activity of designed molecules was predicted using the Atom- Based QSAR tool of the PHASE module.

Results: The top docked score molecule is structure-7 with a docking score of -6.094 Kcal/mol, whereas the docking score of orlistat and tristin is -3.80Kcal/mol and -5.63Kcal/mol, respectively.

Conclusion: The designed molecules have a high docking score and good stability, are in the desirable ADME range and are derived from natural products, so they might be used as lead molecules for anti-obesity drug development.

Background: Breast cancer is one of the most commonly diagnosed cancer types among women worldwide. Cytochrome P450 aromatase (CYP19A1) is an enzyme in vertebrates that selectively catalyzes the biosynthesis of estrogens from androgenic precursors. Researchers have increasingly focused on developing non-steroidal aromatase inhibitors (NSAIs) for their potential clinical use, avoiding steroidal side effects.

Objectives: The objective of the present work is to search for potential lead compounds from the ZINC database through various in silico approaches.

Methods: In the present study, compounds from the ZINC database were initially screened through receptor independent-based pharmacophore virtual screening. These screened molecules were subjected to several assessments, such as Lipinski rule of 5, SMART filtration, ADME prediction using SwissADME and lead optimization. Molecular docking was further applied to study the interaction of the filtered compounds with the active site of aromatase. Finally, the obtained hit compounds, consequently represented to be ideal lead candidates, were escalated to the MD simulations.

Results: The results indicated that the lead compounds might be potential anti-aromatase drug candidate.

Conclusion: The findings provided a valuable approach in developing novel anti-aromatase inhibitors for the treatment of ER+ breast cancer.

Background: Hepatocellular carcinoma (HCC) is the most common liver malignancy where tumorigenesis and metastasis are believed to be tied to the hallmarks of hypoxia and tumor microenvironment (TME).

Methods: In this study, to investigate the relationships among hypoxia, TME, and HCC prognosis, we collected two independent datasets from a public database (TCGA-LIHC for identification, GSE14520 for validation) and identified the hypoxia-related differentially expressed genes (DEGs) from the TCGA data, and the univariable Cox regression and lasso regression analyses were performed to construct the prognosis model. An HCC prognosis model with 4 hypoxiarelated DEGs ("NDRG1", "ENO1", "SERPINE1", "ANXA2") was constructed, and high- and low-risk groups of HCC were established by the median of the model risk score.

Results: The survival analysis revealed significant differences between the two groups in both datasets, with the results of the AUC of the ROC curve of 1, 3, and 5 years in two datasets indicating the robustness of the prognosis model. Meanwhile, for the TCGA-LIHC data, the immune characteristics between the two groups revealed that the low-risk group presented higher levels of activated NK cells, monocytes, and M2 macrophages, and 7 immune checkpoint genes were found upregulated in the high-risk group. Additionally, the two groups have no difference in molecular characteristics (tumor mutational burden, TMB). The proportion of recurrence was higher in the high-risk group, and the correlation between the recurrence month and risk score was negative, indicating high-risk correlates with a short recurrence month.

Conclusion: In summary, this study shows the association among hypoxic signals, TME, and HCC prognosis and may help reveal potential regulatory mechanisms between hypoxia, tumorigenesis, and metastasis in HCC. The hypoxia-related model demonstrated the potential to be a predictor and drug target of prognosis.

Aim: This article aims to quantitatively analyze the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm.

Background: In the last two decades, the global pharmaceutical industry has faced the dilemma of low research & development (R&D) success rate. The US is the world's largest pharmaceutical market, while China is the largest emerging market.

Objective: To collect data from the database and apply machine learning to build the model.

Methods: LightGBM algorithm was used to build the model and identify the factor important to the performance of pharmaceutical companies.

Results: The prediction accuracy for US companies was 80.3%, while it was 64.9% for Chinese companies. The feature importance shows that the net profit growth rate and debt liability ratio are significant in financial indicators. The results indicated that the US may continue to dominate the global pharmaceutical industry, while several Chinese pharmaceutical companies rose sharply after 2015 with the narrowing gap between the Chinese and US pharmaceutical industries.

Conclusion: In summary, our research quantitatively analyzed the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm, which provide a novel perspective for the global pharmaceutical industry. According to the R&D capability and profitability, 141 US-listed and 129 China-listed pharmaceutical companies were divided into four levels to evaluate the growth trend of pharmaceutical firms.

Background: A network pharmacology study on the biological action of Tripterygium wilfordii on myocardial fibrosis (MF).

Methods: The effective components and potential targets of tripterygium wilfordii were screened from the TCMSP database to develop a combination target network. A protein-protein interaction network was constructed by analyzing the interaction between tripterygium wilfordii and MF; then, the Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed. Furthermore, molecular docking was utilized to verify the network analysis results.

Results: It was predicted that MF has 29 components contributing to its effectiveness and 87 potential targets. It is predicted that Tripterygium wilfordii has 29 active components and 87 potential targets for the treatment of MF. The principal active components of tripterygium wilfordii include kaempferol, β-sitosterol, triptolide, and Nobiletin. Signaling pathways: AGE-RAGE, PI3K-Akt, and MAPK may be involved in the mechanism of its action.7 Seven key targets (TNF, STAT3, AKT1, TP53, VEGFA, CASP3, STAT1) are possibly involved in treating MF by tripterygium wilfordii.

Conclusion: This study shows the complex network relationship between multiple components, targets, and pathways of Tripterygium wilfordii in treating MF.

Background: Predicting drug-target interactions (DTIs) is an important topic of study in the field of drug discovery and development. Since DTI prediction in vitro studies is very expensive and time-consuming, computational techniques for predicting drug-target interactions have been introduced successfully to solve these problems and have received extensive attention.

Objective: In this paper, we provided a summary of databases that are useful in DTI prediction and intend to concentrate on machine learning methods as a chemogenomic approach in drug discovery. Unlike previous surveys, we propose a comparative analytical framework based on the evaluation criteria.

Methods: In our suggested framework, there are three stages to follow: First, we present a comprehensive categorization of machine learning-based techniques as a chemogenomic approach for drug-target interaction prediction problems; Second, to evaluate the proposed classification, several general criteria are provided; Third, unlike other surveys, according to the evaluation criteria introduced in the previous stage, a comparative analytical evaluation is performed for each approach.

Results: This systematic research covers the earliest, most recent, and outstanding techniques in the DTI prediction problem and identifies the advantages and weaknesses of each approach separately. Additionally, it can be helpful in the effective selection and improvement of DTI prediction techniques, which is the main superiority of the proposed framework.

Conclusion: This paper gives a thorough overview to serve as a guide and reference for other researchers by providing an analytical framework which can help to select, compare, and improve DTI prediction methods.

Background: Hydrazone and its azomethine (-NHN=CH-) derivatives are widely reported for their immense pharmacological potential. They have also been reported to possess potent anti-tuberculosis, anti-malarial, anti-inflammatory, and anti-oxidant activities. Considering their pharmacological significance, we herein synthesized a set of 10 hydrazones (1S-10S) using green, biodegradable chitosan and HCl as catalyst.

Methods: All synthesized compounds were characterized using modern spectroscopic techniques, including Nuclear magnetic resonance, 1H-/13C-NMR; Fourier transform infrared spectroscopy (FT-IR); Ultraviolet-visible spectroscopy; Mass spectrometry (m/z), etc. Synthesized compounds were in silico screened using molecular docking, dynamics, pharmacokinetics, theoretical properties, and common pharmacophore analysis. Moreover, we also subjected all compounds to DPPH radical scavenging assay, protein denaturation assay, Trypan Blue assay for cell viability assessments, β-hematin assay for hemozoin inhibition analysis and standard antimicrobial analysis.

Results: Our results suggested that the synthesized compound 2S had high potency against studied microbial strains (minimum MIC = 3.12 μg/mL). Our antioxidant analysis for 1S-10S revealed that our compounds had radical scavenging effects ranging from 25.1-80.3 %. Compounds 2S exhibited % cell viability of 68.92% (at 100 μg concentration of sample), while the same compound retained anti-inflammatory % inhibition at 62.16 %. Compound 2S was obtained as the best docked molecule, with a docking score of -5.32 Kcal/mol with target pdb id: 1d7u protein. Molecular dynamics simulation and normal mode analysis for 100 ns for 1d7u:2S retained good stability. Finally, in silico pharmacokinetics, theoretical properties and pharmacophoric features were assessed.

Conclusion: In summary, synthesized hydrazone exhibited a good biological profile according to in silico and in vitro studies. However, further in vivo studies are required that may shed more insights on its potencies.

Aims: Detecting and classifying a brain tumor amid a sole image can be problematic for doctors, although improvements can be made with medical image fusions.

Background: A brain tumor develops in the tissues surrounding the brain or the skull and has a major impact on human life. Primary tumors begin within the brain, whereas secondary tumors, identified as brain metastasis tumors, are generated outside the brain.

Objective: This paper proposes hybrid fusion techniques to fuse multi-modal images. The evaluations are based on performance metrics, and the results are compared with conventional ones.

Methods: In this paper, pre-processing is done considering enhancement methods like Binarization, Contrast Stretching, Median Filter, & Contrast Limited Adaptive Histogram Equalization (CLAHE). Authors have proposed three techniques, PCA-DWT, DCT-PCA, and Discrete ComponentWaveletCosine Transform (DCWCT), which were used to fuse CT-MR images of brain tumors.

Results: The different features were evaluated from the fused images, which were classified using various machine learning approaches. Maximum accuracy of 97.9% and 93.5% is obtained using DCWCT for Support Vector Machine (SVM) and k Nearest Neighbor (kNN), respectively, considering the combination of both feature's shape & Grey Level Difference Statistics. The model is validated using another online dataset.

Conclusion: It has been observed that the classification accuracy for detecting cerebrovascular disease is better after employing the proposed image fusion technique.