Displaying publications 1 - 20 of 141 in total

  1. Masand VH, Al-Hussain S, Alzahrani AY, El-Sayed NNE, Yeo CI, Tan YS, et al.
    Expert Opin Drug Discov, 2024;19(1):111-124.
    PMID: 37811790 DOI: 10.1080/17460441.2023.2266990
    BACKGROUND: The process of drug development and discovery is costly and slow. Although an understanding of molecular design principles and biochemical processes has progressed, it is essential to minimize synthesis-testing cycles. An effective approach is to analyze key heteroatoms, including oxygen and nitrogen. Herein, we present an analysis focusing on the utilization of nitrogen atoms in approved drugs.

    RESEARCH DESIGN AND METHODS: The present work examines the frequency, distribution, prevalence, and diversity of nitrogen atoms in a dataset comprising 2,049 small molecules approved by different regulatory agencies (FDA and others). Various types of nitrogen atoms, such as sp3-, sp2-, sp-hybridized, planar, ring, and non-ring are included in this investigation.

    RESULTS: The results unveil both previously reported and newly discovered patterns of nitrogen atom distribution around the center of mass in the majority of drug molecules.

    CONCLUSIONS: This study has highlighted intriguing trends in the role of nitrogen atoms in drug design and development. The majority of drugs contain 1-3 nitrogen atoms within 5Å from the center of mass (COM) of a molecule, with a higher preference for the ring and planar nitrogen atoms. The results offer invaluable guidance for the multiparameter optimization process, thus significantly contributing toward the conversion of lead compounds into potential drug candidates.

    Matched MeSH terms: Drug Design*
  2. Afolabi LT, Saeed F, Hashim H, Petinrin OO
    PLoS One, 2018;13(1):e0189538.
    PMID: 29329334 DOI: 10.1371/journal.pone.0189538
    Pharmacologically active molecules can provide remedies for a range of different illnesses and infections. Therefore, the search for such bioactive molecules has been an enduring mission. As such, there is a need to employ a more suitable, reliable, and robust classification method for enhancing the prediction of the existence of new bioactive molecules. In this paper, we adopt a recently developed combination of different boosting methods (Adaboost) for the prediction of new bioactive molecules. We conducted the research experiments utilizing the widely used MDL Drug Data Report (MDDR) database. The proposed boosting method generated better results than other machine learning methods. This finding suggests that the method is suitable for inclusion among the in silico tools for use in cheminformatics, computational chemistry and molecular biology.
    Matched MeSH terms: Drug Design*
  3. Nabihah Nasir N, Sekar M, Ravi S, Wong LS, Sisinthy SP, Gan SH, et al.
    Drug Des Devel Ther, 2023;17:1065-1078.
    PMID: 37064433 DOI: 10.2147/DDDT.S388490
    Streptonigrin is an aminoquinone alkaloid isolated from Streptomyces flocculus and is gaining attention as a drug molecule owing to its potential antitumor and antibiotic effects. It was previously used as an anticancer drug but has been discontinued because of its toxic effects. However, according to the most recent studies, the toxicity of streptonigrin and its structurally modified derivatives has been reduced while maintaining their potential pharmacological action at lower concentrations. To date, many investigations have been conducted on this molecule and its derivatives to determine the most effective molecule with low toxicity to enable new drug discovery. Therefore, the main objective of this study is to provide a comprehensive review and to discuss the prospects for streptonigrin and its derived compounds, which may boost the molecule as a highly interesting target molecule for new drug design, development and therapy. To complete this review, relevant literature was collected from several scientific databases, including Google Scholar, PubMed, Scopus and ScienceDirect. Following a complete screening, the obtained information is summarized in the present review to provide a good reference and accelerate the development and utilization of streptonigrin and its derivatives as pharmaceuticals.
    Matched MeSH terms: Drug Design
  4. Arif SM, Holliday JD, Willett P
    J Chem Inf Model, 2010 Aug 23;50(8):1340-9.
    PMID: 20672867 DOI: 10.1021/ci1001235
    This paper discusses the weighting of two-dimensional fingerprints for similarity-based virtual screening, specifically the use of weights that assign greatest importance to the substructural fragments that occur least frequently in the database that is being screened. Virtual screening experiments using the MDL Drug Data Report and World of Molecular Bioactivity databases show that the use of such inverse frequency weighting schemes can result, in some circumstances, in marked increases in screening effectiveness when compared with the use of conventional, unweighted fingerprints. Analysis of the characteristics of the various schemes demonstrates that such weights are best used to weight the fingerprint of the reference structure in a similarity search, with the database structures' fingerprints unweighted. However, the increases in performance resulting from such weights are only observed with structurally homogeneous sets of active molecules; when the actives are diverse, the best results are obtained using conventional, unweighted fingerprints for both the reference structure and the database structures.
    Matched MeSH terms: Drug Design*
  5. Suvarna BS
    Kathmandu Univ Med J (KUMJ), 2010 1 15;7(26):172-6.
    PMID: 20071855
    Individuals respond differently to drugs and sometimes the effects are unpredictable. Differences in DNA that alter the expression or function of proteins targeted by drugs can contribute significantly to the variation in the individuals responses. The use of pharmacogenomics is to identify genetic polymorphisms that predispose patients to adverse drug effects that, although they may occur in only a small subset of the people treated with a new medication, are sufficiently toxic to jeopardise further development of the drug for all patients. Given the potential value of knowing all the possible factors that influence the effects of new agents, it is likely that pharmacogenomics will have an increasingly important role in drug discovery and development. This article briefly reviews concepts that underlie the emerging fields of pharmacogenetics and pharmacogenomics, with an emphasis on the pharmacogenetics of drug metabolism. Although only a few examples will be provided to illustrate concepts and to demonstrate the potential contribution of pharmacogenetics to medical practice, it is now clear that virtually every pathway of drug metabolism will eventually be found to have genetic variation.
    Matched MeSH terms: Drug Design*
  6. Usuda S, Masukawa N, Leong KH, Okada K, Onuki Y
    Chem Pharm Bull (Tokyo), 2021;69(9):896-904.
    PMID: 34470954 DOI: 10.1248/cpb.c21-00427
    This study investigated the effect of manufacturing process variables of mini-tablets, in particular, the effect of process variables concerning fluidized bed granulation on tablet weight variation. Test granules were produced with different granulation conditions according to a definitive screening design (DSD). The five evaluated factors assigned to DSD were: the grinding speed of the sample mill at the grinding process of the active pharmaceutical ingredient (X1), microcrystalline cellulose content in granules (X2), inlet air temperature (X3), binder concentration (X4) and the spray speed of the binder solution (X5) at the granulation process. First, the relationships between the evaluated factors and the granule properties were investigated. As a result of the DSD analysis, the mode of action of granulation parameters on the granule properties was fully characterized. Subsequently, the variation in tablet weight was examined. In addition to mini-tablets (3 mm diameter), this experiment assessed regular tablets (8 mm diameter). From the results for regular tablets, the variation in tablet weight was affected by the flowability of granules. By contrast, regarding the mini-tablets, no significant effect on the variation of tablet weight was found from the evaluated factors. From this result, this study further focused on other important factors besides the granulation process, and then the effect of the die-hole position of the multiple-tip tooling on tablet weight variation was proven to be significant. Our findings provide a better understanding of manufacturing mini-tablets.
    Matched MeSH terms: Drug Design*
  7. Loo JSE, Emtage AL, Ng KW, Yong ASJ, Doughty SW
    J Mol Graph Model, 2017 Dec 29;80:38-47.
    PMID: 29306746 DOI: 10.1016/j.jmgm.2017.12.017
    GPCR crystal structures have become more readily accessible in recent years. However, homology models of GPCRs continue to play an important role as many GPCR structures remain unsolved. The new crystal structures now available provide not only additional templates for homology modelling but also the opportunity to assess the performance of homology models against their respective crystal structures and gain insight into the performance of such models. In this study we have constructed homology models from templates of various transmembrane sequence identities for eight GPCR targets to better understand the relationship between transmembrane sequence identity and model quality. Model quality was assessed relative to the crystal structure in terms of structural accuracy as well as performance in two typical structure-based drug design applications: ligand binding pose prediction and docking enrichment in virtual screening. Crystal structures significantly outperformed homology models in both assessments. Accurate ligand binding pose prediction was possible but difficult to achieve using homology models, even with the use of induced fit docking. In virtual screening using homology models still conferred significant enrichment compared to random selection, with a clear benefit also observed in using models optimized through induced fit docking. Our results indicate that while homology models that are reasonably accurate structurally can be constructed, without significant refinement homology models will be outperformed by crystal structures in ligand binding pose prediction and docking enrichment regardless of the template used, primarily due to the extremely high level of structural accuracy needed for such applications.
    Matched MeSH terms: Drug Design
  8. Amir Yusri MA, Sekar M, Wong LS, Gan SH, Ravi S, Subramaniyan V, et al.
    Drug Des Devel Ther, 2023;17:1079-1096.
    PMID: 37064431 DOI: 10.2147/DDDT.S389977
    Celastrol is a naturally occurring chemical isolated from Tripterygium wilfordii Hook. f., root extracts widely known for their neuroprotective properties. In this review, we focus on the efficacy of celastrol in mitigating memory impairment (MI) in both in vivo and in vitro models. Scopus, PubMed and Web of Science databases were utilised to locate pertinent literatures that explore the effects of celastrol in the brain, including its pharmacokinetics, bioavailability, behavioral effects and some of the putative mechanisms of action on memory in many MI models. To date, preclinical studies strongly suggest that celastrol is highly effective in enhancing the cognitive performance of MI animal models, particularly in the memory domain, including spatial, recognition, retention and reference memories, via reduction in oxidative stress and attenuation of neuro-inflammation, among others. This review also emphasised the challenges and potential associated enhancement of medication delivery for MI treatment. Additionally, the potential structural alterations and derivatives of celastrol in enhancing its physicochemical and drug-likeness qualities are examined. The current review demonstrated that celastrol can improve cognitive performance and mitigate MI in several preclinical investigations, highlighting its potential as a natural lead molecule for the design and development of a novel neuroprotective medication.
    Matched MeSH terms: Drug Design
  9. Sanachai K, Mahalapbutr P, Sanghiran Lee V, Rungrotmongkol T, Hannongbua S
    J Phys Chem B, 2021 Dec 23;125(50):13644-13656.
    PMID: 34904832 DOI: 10.1021/acs.jpcb.1c07060
    Global public health has been a critical problem by the sudden increase of the COVID-19 outbreak. The papain-like protease (PLpro) of SARS-CoV-2 is a key promising target for antiviral drug development since it plays a pivotal role in viral replication and innate immunity. Here, we employed the all-atom molecular dynamics (MD) simulations and binding free energy calculations based on MM-PB(GB)SA and SIE methods to elucidate and compare the binding behaviors of five inhibitors derived from peptidomimetic inhibitors (VIR250 and VIR251) and naphthalene-based inhibitors (GRL-0617, compound 3, and compound Y96) against SARS-CoV-2 PLpro. The obtained results revealed that all inhibitors interacting within the PLpro active site are mostly driven by vdW interactions, and the hydrogen bond formation in residues G163 and G271 with peptidomimetics and the Q269 residue with naphthalene-based inhibitors was essential for stabilizing the protein-ligand complexes. Among the five studied inhibitors, VIR250 exhibited the most binding efficiency with SARS-CoV-2 PLpro, and thus, it was chosen for the rational drug design. Based on the computationally designed ligand-protein complexes, the replacement of aromatic rings including heteroatoms (e.g., thiazolopyridine) at the P2 and P4 sites could help to improve the inhibitor-binding efficiency. Furthermore, the hydrophobic interactions with residues at P1-P3 sites can be increased by enlarging the nonpolar moieties (e.g., ethene) at the N-terminal of VIR250. We expect that the structural data obtained will contribute to the development of new PLpro inhibitors with more inhibitory potency for COVID-19.
    Matched MeSH terms: Drug Design
  10. Chua HM, Moshawih S, Goh HP, Ming LC, Kifli N
    PLoS One, 2023;18(9):e0290948.
    PMID: 37656730 DOI: 10.1371/journal.pone.0290948
    There is still unmet medical need in cancer treatment mainly due to drug resistance and adverse drug events. Therefore, the search for better drugs is essential. Computer-aided drug design (CADD) and discovery tools are useful to streamline the lengthy and costly drug development process. Anthraquinones are a group of naturally occurring compounds with unique scaffold that exert various biological properties including anticancer activities. This protocol describes a systematic review that provide insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment. It was prepared in accordance with the "Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 guidelines, and published in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904). Search strategies will be developed based on the combination of relevant keywords and executed in PubMed, Scopus, Web of Science and MedRxiv. Only original studies that employed CADD as primary tool in virtual screening for the purpose of designing or discovering anti-cancer drugs involving anthraquinone scaffold published in English language will be included. Two independent reviewers will be involved to screen and select the papers, extract the data and assess the risk of bias. Apart from exploring the trends and types of CADD methods used, the target proteins of these compounds in cancer treatment will also be revealed in this review. It is believed that the outcome of this study could be utilized to support the ongoing research in similar area with better quality and greater probability of success, consequently optimizing the resources in subsequent in vitro, in vivo, non-clinical and clinical development. It will also serve as an evidence based scientific guide for new research to design novel anthraquinone-derived drug with improved efficacy and safety profile for cancer treatment.
    Matched MeSH terms: Drug Design
  11. Azmi ID, Wibroe PP, Wu LP, Kazem AI, Amenitsch H, Moghimi SM, et al.
    J Control Release, 2016 Oct 10;239:1-9.
    PMID: 27524284 DOI: 10.1016/j.jconrel.2016.08.011
    Non-lamellar liquid crystalline aqueous nanodispersions, known also as ISAsomes (internally self-assembled 'somes' or nanoparticles), are gaining increasing interest in drug solubilisation and bio-imaging, but they often exhibit poor hemocompatibility and induce cytotoxicity. This limits their applications in intravenous drug delivery and targeting. Using a binary mixture of citrem and soy phosphatidylcholine (SPC) at different weight ratios, we describe a library of colloidally stable aqueous and hemocompatible nanodispersions of diverse nanoarchitectures (internal self-assembled nanostructures). This engineered library is structurally stable in human plasma as well as being hemocompatible (non-hemolytic, and poor activator of the complement system). By varying citrem to lipid weight ratio, the nanodispersion susceptibility to macrophage uptake could also be modulated. Finally, the formation of nanodispersions comprising internally V2 (inverse bicontinuous cubic) and H2 (inverse hexagonal) nanoarchitectures was achieved without the use of an organic solvent, a secondary emulsifier, or high-energy input. The tunable binary citrem/SPC nanoplatform holds promise for future development of hemocompatible and immune-safe nanopharmaceuticals.
    Matched MeSH terms: Drug Design*
  12. Ramesh M, Muthuraman A
    Curr Top Med Chem, 2021;21(32):2856-2868.
    PMID: 34809547 DOI: 10.2174/1568026621666211122161932
    Neuropathic pain occurs due to physical damage, injury, or dysfunction of neuronal fibers. The pathophysiology of neuropathic pain is too complex. Therefore, an accurate and reliable prediction of the appropriate hits/ligands for the treatment of neuropathic pain is a challenging process. However, computer-aided drug discovery approaches contributed significantly to discovering newer hits/ligands for the treatment of neuropathic pain. The computational approaches like homology modeling, induced-fit molecular docking, structure-activity relationships, metadynamics, and virtual screening were cited in the literature for the identification of potential hit molecules against neuropathic pain. These hit molecules act as inducible nitric oxide synthase inhibitors, FLAT antagonists, TRPA1 modulators, voltage-gated sodium channel binder, cannabinoid receptor-2 agonists, sigma-1 receptor antagonists, etc. Sigma-1 receptor is a distinct type of opioid receptor and several patents were obtained for sigma-1 receptor antagonists for the treatment of neuropathic pain. These molecules were found to have a profound role in the management of neuropathic pain. The present review describes the validated therapeutic targets, potential chemical scaffolds, and crucial protein-ligand interactions for the management of neuropathic pain based on the recently reported computational methodologies of the present and past decades. The study can help the researcher to discover newer drugs/drug-like molecules against neuropathic pain.
    Matched MeSH terms: Drug Design*
  13. Tai HK, Jusoh SA, Siu SWI
    J Cheminform, 2018 Dec 14;10(1):62.
    PMID: 30552524 DOI: 10.1186/s13321-018-0320-9
    BACKGROUND: Protein-ligand docking programs are routinely used in structure-based drug design to find the optimal binding pose of a ligand in the protein's active site. These programs are also used to identify potential drug candidates by ranking large sets of compounds. As more accurate and efficient docking programs are always desirable, constant efforts focus on developing better docking algorithms or improving the scoring function. Recently, chaotic maps have emerged as a promising approach to improve the search behavior of optimization algorithms in terms of search diversity and convergence speed. However, their effectiveness on docking applications has not been explored. Herein, we integrated five popular chaotic maps-logistic, Singer, sinusoidal, tent, and Zaslavskii maps-into PSOVina[Formula: see text], a recent variant of the popular AutoDock Vina program with enhanced global and local search capabilities, and evaluated their performances in ligand pose prediction and virtual screening using four docking benchmark datasets and two virtual screening datasets.

    RESULTS: Pose prediction experiments indicate that chaos-embedded algorithms outperform AutoDock Vina and PSOVina in ligand pose RMSD, success rate, and run time. In virtual screening experiments, Singer map-embedded PSOVina[Formula: see text] achieved a very significant five- to sixfold speedup with comparable screening performances to AutoDock Vina in terms of area under the receiver operating characteristic curve and enrichment factor. Therefore, our results suggest that chaos-embedded PSOVina methods might be a better option than AutoDock Vina for docking and virtual screening tasks. The success of chaotic maps in protein-ligand docking reveals their potential for improving optimization algorithms in other search problems, such as protein structure prediction and folding. The Singer map-embedded PSOVina[Formula: see text] which is named PSOVina-2.0 and all testing datasets are publicly available on https://cbbio.cis.umac.mo/software/psovina .

    Matched MeSH terms: Drug Design
  14. Vassiliev P, Iezhitsa I, Agarwal R, Marcus AJ, Spasov A, Zhukovskaya O, et al.
    Data Brief, 2018 Jun;18:340-347.
    PMID: 29896521 DOI: 10.1016/j.dib.2018.02.067
    This article contains data that relate to the study carried out in the work of Marcus et al. (2018) [1]. Data represent an information about pharmacophore analysis of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole derivatives and results of construction of the relationship between intraocular pressure (IOP) lowering activity and hypotensive activity of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole derivatives using a multilayer perceptron artificial neural network. In particular, they include the ones listed in this article: 1) table of all pharmacophores of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole derivatives that showed IOP lowering activity; 2) table of all pharmacophores of the compounds that showed absence of IOP lowering activity; 3) table of initial data for artificial neural network analysis of relationship between IOP activity and hypotensive activity of this chemical series; 4) graphical representation of the best neural network model of this dependence; 5) original txt-file of results of pharmacophore analysis; 6) xls-file of initial data for neural network modeling; 7) original stw-file of results of neural network modeling; 8) original xml-file of the best neural network model of dependence between IOP lowering activity and hypotensive activity of these azole derivatives. The data may be useful for researchers interested in designing new drug substances and will contribute to understanding of the mechanisms of IOP lowering activity.
    Matched MeSH terms: Drug Design
  15. Hentabli H, Saeed F, Abdo A, Salim N
    ScientificWorldJournal, 2014;2014:286974.
    PMID: 25140330 DOI: 10.1155/2014/286974
    Molecular similarity is a pervasive concept in drug design. The basic idea underlying molecular similarity is the similar property principle, which states that structurally similar molecules will exhibit similar physicochemical and biological properties. In this paper, a new graph-based molecular descriptor (GBMD) is introduced. The GBMD is a new method of obtaining a rough description of 2D molecular structure in textual form based on the canonical representations of the molecule outline shape and it allows rigorous structure specification using small and natural grammars. Simulated virtual screening experiments with the MDDR database show clearly the superiority of the graph-based descriptor compared to many standard descriptors (ALOGP, MACCS, EPFP4, CDKFP, PCFP, and SMILE) using the Tanimoto coefficient (TAN) and the basic local alignment search tool (BLAST) when searches were carried.
    Matched MeSH terms: Drug Design
  16. Guo L, Wang Y, Xu X, Cheng KK, Long Y, Xu J, et al.
    J Proteome Res, 2021 01 01;20(1):346-356.
    PMID: 33241931 DOI: 10.1021/acs.jproteome.0c00431
    Identification of phosphorylation sites is an important step in the function study and drug design of proteins. In recent years, there have been increasing applications of the computational method in the identification of phosphorylation sites because of its low cost and high speed. Most of the currently available methods focus on using local information around potential phosphorylation sites for prediction and do not take the global information of the protein sequence into consideration. Here, we demonstrated that the global information of protein sequences may be also critical for phosphorylation site prediction. In this paper, a new deep neural network model, called DeepPSP, was proposed for the prediction of protein phosphorylation sites. In the DeepPSP model, two parallel modules were introduced to extract both local and global features from protein sequences. Two squeeze-and-excitation blocks and one bidirectional long short-term memory block were introduced into each module to capture effective representations of the sequences. Comparative studies were carried out to evaluate the performance of DeepPSP, and four other prediction methods using public data sets The F1-score, area under receiver operating characteristic curves (AUROC), and area under precision-recall curves (AUPRC) of DeepPSP were found to be 0.4819, 0.82, and 0.50, respectively, for S/T general site prediction and 0.4206, 0.73, and 0.39, respectively, for Y general site prediction. Compared with the MusiteDeep method, the F1-score, AUROC, and AUPRC of DeepPSP were found to increase by 8.6, 2.5, and 8.7%, respectively, for S/T general site prediction and by 20.6, 5.8, and 18.2%, respectively, for Y general site prediction. Among the tested methods, the developed DeepPSP method was also found to produce best results for different kinase-specific site predictions including CDK, mitogen-activated protein kinase, CAMK, AGC, and CMGC. Taken together, the developed DeepPSP method may offer a more accurate phosphorylation site prediction by including global information. It may serve as an alternative model with better performance and interpretability for protein phosphorylation site prediction.
    Matched MeSH terms: Drug Design
  17. Tan YY, Yap PK, Xin Lim GL, Mehta M, Chan Y, Ng SW, et al.
    Chem Biol Interact, 2020 Sep 25;329:109221.
    PMID: 32768398 DOI: 10.1016/j.cbi.2020.109221
    Cancer continues to be one of the most challenging diseases to be treated and is one of the leading causes of deaths around the globe. Cancers account for 13% of all deaths each year, with cancer-related mortality expected to rise to 13.1 million by the year 2030. Although, we now have a large library of chemotherapeutic agents, the problem of non-selectivity remains the biggest drawback, as these substances are toxic not only to cancerous cells, but also to other healthy cells in the body. The limitations with chemotherapy and radiation have led to the discovery and development of novel strategies for safe and effective treatment strategies to manage the menace of cancer. Researchers have long justified and have shed light on the emergence of nanotechnology as a potential area for cancer therapy and diagnostics, whereby, nanomaterials are used primarily as nanocarriers or as delivery agents for anticancer drugs due to their tumor targeting properties. Furthermore, nanocarriers loaded with chemotherapeutic agents also overcome biological barriers such as renal and hepatic clearances, thus improving therapeutic efficacy with lowered morbidity. Theranostics, which is the combination of rationally designed nanomaterials with cancer-targeting moieties, along with protective polymers and imaging agents has become one of the core keywords in cancer research. In this review, we have highlighted the potential of various nanomaterials for their application in cancer therapy and imaging, including their current state and clinical prospects. Theranostics has successfully paved a path to a new era of drug design and development, in which nanomaterials and imaging contribute to a large variety of cancer therapies and provide a promising future in the effective management of various cancers. However, in order to meet the therapeutic needs, theranostic nanomaterials must be designed in such a way, that take into account the pharmacokinetic and pharmacodynamics properties of the drug for the development of effective carcinogenic therapy.
    Matched MeSH terms: Drug Design
  18. Mumtaz A, Ashfaq UA, Ul Qamar MT, Anwar F, Gulzar F, Ali MA, et al.
    Nat Prod Res, 2017 Jun;31(11):1228-1236.
    PMID: 27681445 DOI: 10.1080/14786419.2016.1233409
    Medicinal plants are the main natural pools for the discovery and development of new drugs. In the modern era of computer-aided drug designing (CADD), there is need of prompt efforts to design and construct useful database management system that allows proper data storage, retrieval and management with user-friendly interface. An inclusive database having information about classification, activity and ready-to-dock library of medicinal plant's phytochemicals is therefore required to assist the researchers in the field of CADD. The present work was designed to merge activities of phytochemicals from medicinal plants, their targets and literature references into a single comprehensive database named as Medicinal Plants Database for Drug Designing (MPD3). The newly designed online and downloadable MPD3 contains information about more than 5000 phytochemicals from around 1000 medicinal plants with 80 different activities, more than 900 literature references and 200 plus targets. The designed database is deemed to be very useful for the researchers who are engaged in medicinal plants research, CADD and drug discovery/development with ease of operation and increased efficiency. The designed MPD3 is a comprehensive database which provides most of the information related to the medicinal plants at a single platform. MPD3 is freely available at: http://bioinform.info .
    Matched MeSH terms: Drug Design
  19. Athar Abbasi M, Raza H, Aziz-Ur-Rehman, Zahra Siddiqui S, Adnan Ali Shah S, Hassan M, et al.
    Bioorg Chem, 2019 03;83:63-75.
    PMID: 30342387 DOI: 10.1016/j.bioorg.2018.10.018
    Present work aimed to synthesize some unique bi-heterocyclic benzamides as lead compounds for the in vitro inhibition of urease enzyme, followed by in silico studies. These targeted benzamides were synthesized in good yields through a multi-step protocol and their structures were confirmed by IR, 1H NMR, 13C NMR, EI-MS and elemental analysis. The in vitro screening results showed that most of the ligands exhibited good inhibitory potentials against the urease. Chemo-informatics analysis envisaged that all these compounds obeyed the Lipinski's rule. Molecular docking results showed that 7h exhibited good binding energy value (-8.40 kcal/mol) and was bound within the active region of urease enzyme. From the present investigation, it was inferred that some of these potent urease inhibitors might serve as novel templates in drug designing.
    Matched MeSH terms: Drug Design
  20. Moshawih S, Hadikhani P, Fatima A, Goh HP, Kifli N, Kotra V, et al.
    J Mol Graph Model, 2022 Dec;117:108307.
    PMID: 36096064 DOI: 10.1016/j.jmgm.2022.108307
    A Laplacian scoring algorithm for gene selection and the Gini coefficient to identify the genes whose expression varied least across a large set of samples were the state-of-the-art methods used here. These methods have not been trialed for their feasibility in cheminformatics. This was a maiden attempt to investigate a complete comparative analysis of an anthraquinone and chalcone derivatives-based virtual combinatorial library. This computational "proof-of-concept" study illustrated the combinatorial approach used to explain how the structure of the selected natural products (NPs) undergoes molecular diversity analysis. A virtual combinatorial library (1.6 M) based on 20 anthraquinones and 24 chalcones was enumerated. The resulting compounds were optimized to the near drug-likeness properties, and the physicochemical descriptors were calculated for all datasets including FDA, Non-FDA, and NPs from ZINC 15. UMAP and PCA were applied to compare and represent the chemical space coverage of each dataset. Subsequently, the Laplacian score and Gini coefficient were applied to delineate feature selection and selectivity among properties, respectively. Finally, we demonstrated the diversity between the datasets by employing Murcko's and the central scaffolds systems, calculating three fingerprint descriptors and analyzing their diversity by PCA and SOM. The optimized enumeration resulted in 1,610,268 compounds with NP-Likeness, and synthetic feasibility mean scores close to FDA, Non-FDA, and NPs datasets. The overlap between the chemical space of the 1.6 M database was more prominent than with the NPs dataset. A Laplacian score prioritized NP-likeness and hydrogen bond acceptor properties (1.0 and 0.923), respectively, while the Gini coefficient showed that all properties have selective effects on datasets (0.81-0.93). Scaffold and fingerprint diversity indicated that the descending order for the tested datasets was FDA, Non-FDA, NPs and 1.6 M. Virtual combinatorial libraries based on NPs can be considered as a source of the combinatorial compound with NP-likeness properties. Furthermore, measuring molecular diversity is supposed to be performed by different methods to allow for comparison and better judgment.
    Matched MeSH terms: Drug Design
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