Publications
230 results found
Mak L, Marcus D, Howlett A, et al., 2015, Metrabase: a cheminformatics and bioinformatics database for small molecule transporter data analysis and (Q)SAR modeling, Journal of Cheminformatics, Vol: 7, ISSN: 1758-2946
Both metabolism and transport are key elements defining the bioavailability and biological activity of molecules, i.e. their adverse and therapeutic effects. Structured and high quality experimental data stored in a suitable container, such as a relational database, facilitates easy computational processing and thus allows for high quality information/knowledge to be efficiently inferred by computational analyses. Our aim was to create a freely accessible database that would provide easy access to data describing interactions between proteins involved in transport and xenobiotic metabolism and their small molecule substrates and modulators. We present Metrabase, an integrated cheminformatics and bioinformatics resource containing curated data related to human transport and metabolism of chemical compounds. Its primary content includes over 11,500 interaction records involving nearly 3,500 small molecule substrates and modulators of transport proteins and, currently to a much smaller extent, cytochrome P450 enzymes. Data was manually extracted from the published literature and supplemented with data integrated from other available resources.
Mussa HY, Marcus D, Mitchell JBO, et al., 2015, Verifying the fully “Laplacianised” posterior Naïve Bayesian approach and more, Journal of Cheminformatics, Vol: 7, ISSN: 1758-2946
BackgroundIn a recent paper, Mussa, Mitchell and Glen (MMG) have mathematically demonstrated that the “Laplacian Corrected Modified Naïve Bayes” (LCMNB) algorithm can be viewed as a variant of the so-called Standard Naïve Bayes (SNB) scheme, whereby the role played by absence of compound features in classifying/assigning the compound to its appropriate class is ignored. MMG have also proffered guidelines regarding the conditions under which this omission may hold. Utilising three data sets, the present paper examines the validity of these guidelines in practice. The paper also extends MMG’s work and introduces a new version of the SNB classifier: “Tapered Naïve Bayes” (TNB). TNB does not discard the role of absence of a feature out of hand, nor does it fully consider its role. Hence, TNB encapsulates both SNB and LCMNB.ResultsLCMNB, SNB and TNB performed differently on classifying 4,658, 5,031 and 1,149 ligands (all chosen from the ChEMBL Database) distributed over 31 enzymes, 23 membrane receptors, and one ion-channel, four transporters and one transcription factor as their target proteins. When the number of features utilised was equal to or smaller than the “optimal” number of features for a given data set, SNB classifiers systematically gave better classification results than those yielded by LCMNB classifiers. The opposite was true when the number of features employed was markedly larger than the “optimal” number of features for this data set. Nonetheless, these LCMNB performances were worse than the classification performance achieved by SNB when the “optimal” number of features for the data set was utilised. TNB classifiers systematically outperformed both SNB and LCMNB classifiers.ConclusionsThe classification results obtained in this study concur with the mathematical based guidelines given in MMG’s paper—that is, ignoring the role of absence of a feature out of han
Afzal AM, Mussa HY, Turner RE, et al., 2015, A multi-label approach to target prediction taking ligand promiscuity into account, Journal of Cheminformatics, Vol: 7, ISSN: 1758-2946
BackgroundAccording to Cobanoglu et al., it is now widely acknowledged that the single target paradigm (one protein/target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous – it can interact with more than one target protein.In recent years, in in silico target prediction methods the promiscuity issue has generally been approached computationally in three main ways: ligand-based methods; target-protein-based methods; and integrative schemes. In this study we confine attention to ligand-based target prediction machine learning approaches, commonly referred to as target-fishing.The target-fishing approaches that are currently ubiquitous in cheminformatics literature can be essentially viewed as single-label multi-classification schemes; these approaches inherently bank on the single target paradigm assumption that a ligand can zero in on one single target. In order to address the ligand promiscuity issue, one might be able to cast target-fishing as a multi-label multi-class classification problem. For illustrative and comparison purposes, single-label and multi-label Naïve Bayes classification models (denoted here by SMM and MMM, respectively) for target-fishing were implemented. The models were constructed and tested on 65,587 compounds/ligands and 308 targets retrieved from the ChEMBL17 database.ResultsOn classifying 3,332 test multi-label (promiscuous) compounds, SMM and MMM performed differently. At the 0.05 significance level, a Wilcoxon signed rank test performed on the paired target predictions yielded by SMM and MMM for the test ligands gave a p-value < 5.1 × 10−94 and test statistics value of 6.8 × 105, in favour of MMM. The two models performed differently when tested on four datasets comprising single-label (non-promiscuous) compounds; McNemar’s tes
Glen RC, kirchmair J, Goller AH, et al., 2015, Predicting drug metabolism: experiment and/or computation?, Nature Reviews Drug Discovery, Vol: 14, Pages: 387-404, ISSN: 1474-1784
Abstract | Drug metabolism can produce metabolites with physicochemical andpharmacological properties that differ substantially from those of the parent drug,and consequently has important implications for both drug safety and efficacy.To reduce the risk of costly clinical-stage attrition due to the metaboliccharacteristics of drug candidates, there is a need for efficient and reliable ways topredict drug metabolism in vitro, in silico and in vivo. In this Perspective, we providean overview of the state of the art of experimental and computational approachesfor investigating drug metabolism. We highlight the scope and limitations of thesemethods, and indicate strategies to harvest the synergies that result fromcombining measurement and prediction of drug metabolism.
Brame AL, Maguire JJ, Yang P, et al., 2015, Design, characterization, and first-in-human study of the vascular actions of a novel biased apelin receptor agonist, Hypertension, Vol: 65, Pages: 834-840, ISSN: 1524-4563
Torella R, Li J, Kinrade E, et al., 2014, A combination of computational and experimental approaches identifies DNA sequence constraints associated with target site binding specificity of the transcription factor CSL, NUCLEIC ACIDS RESEARCH, Vol: 42, Pages: 10550-10563, ISSN: 0305-1048
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- Citations: 13
Koutsoukas A, Lowe R, Kalantarmotamedi Y, et al., 2014, Erratum: "in silico target predictions: Defining a benchmarking data set and comparison of performance of the multiclass naïve bayes and parzen-rosenblatt window", Journal of Chemical Information and Modeling, Vol: 54, Pages: 2180-2182, ISSN: 1549-9596
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- Citations: 2
Tyzack JD, Glen RC, 2014, Investigating and Predicting how Biology Changes Molecules and Their Properties, MOLECULAR INFORMATICS, Vol: 33, Pages: 443-445, ISSN: 1868-1743
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- Citations: 4
Tyzack JD, Mussa HY, Williamson MJ, et al., 2014, Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers, JOURNAL OF CHEMINFORMATICS, Vol: 6, ISSN: 1758-2946
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- Citations: 26
Koutsoukas A, Paricharak S, Galloway WRJD, et al., 2014, How Diverse Are Diversity Assessment Methods? A Comparative Analysis and Benchmarking of Molecular Descriptor Space, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 54, Pages: 230-242, ISSN: 1549-9596
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- Citations: 58
Liggi S, Drakakis G, Koutsoukas A, et al., 2014, Extending <i>in silico</i> mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts, FUTURE MEDICINAL CHEMISTRY, Vol: 6, Pages: 2029-2056, ISSN: 1756-8919
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- Citations: 17
Fernstad SJ, Glen RC, 2014, Visual Analysis of Missing Data - To See What Isn't There, IEEE Conference Visual Analytics Sci Technology, Publisher: IEEE, Pages: 249-250, ISSN: 2325-9442
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- Citations: 11
Kirchmair J, Williamson MJ, Afzal AM, et al., 2013, FAst MEtabolizer (FAME): A Rapid and Accurate Predictor of Sites of Metabolism in Multiple Species by Endogenous Enzymes, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 53, Pages: 2896-2907, ISSN: 1549-9596
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- Citations: 40
Lowe DM, Murray-Rust P, Glen RC, 2013, OPSIN: Taming the jungle of IUPAC chemical nomenclature, 246th National Meeting of the American-Chemical-Society (ACS), Publisher: AMER CHEMICAL SOC, ISSN: 0065-7727
Glen RC, 2013, Adventures in drug discovery: For now we see through a glass, darkly, 246th National Meeting of the American-Chemical-Society (ACS), Publisher: AMER CHEMICAL SOC, ISSN: 0065-7727
Nguyen HP, Koutsoukas A, Fauzi FM, et al., 2013, Diversity Selection of Compounds Based on 'Protein Affinity Fingerprints' Improves Sampling of <i>Bioactive</i> Chemical Space, CHEMICAL BIOLOGY & DRUG DESIGN, Vol: 82, Pages: 252-266, ISSN: 1747-0277
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- Citations: 13
Mussa HY, Mitchell JBO, Glen RC, 2013, Full "Laplacianised" posterior naive Bayesian algorithm, JOURNAL OF CHEMINFORMATICS, Vol: 5, ISSN: 1758-2946
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- Citations: 12
Koutsoukas A, Lowe R, KalantarMotamedi Y, et al., 2013, In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Naive Bayes and Parzen-Rosenblatt Window, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 53, Pages: 1957-1966, ISSN: 1549-9596
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- Citations: 119
Tyzack JD, Williamson MJ, Torella R, et al., 2013, Prediction of Cytochrome P450 Xenobiotic Metabolism: Tethered Docking and Reactivity Derived from Ligand Molecular Orbital Analysis, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 53, Pages: 1294-1305, ISSN: 1549-9596
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- Citations: 39
Fauzi FM, Koutsoukas A, Lowe R, et al., 2013, Linking Ayurveda and Western medicine by integrative analysis., J Ayurveda Integr Med, Vol: 4, Pages: 117-119, ISSN: 0975-9476
In this article, we discuss our recent work in elucidating the mode-of-action of compounds used in traditional medicine including Ayurvedic medicine. Using computational ('in silico') approach, we predict potential targets for Ayurvedic anti-cancer compounds, obtained from the Indian Plant Anticancer Database given its chemical structure. In our analysis, we observed that: (i) the targets predicted can be connected to cancer pathogenesis i.e. steroid-5-alpha reductase 1 and 2 and estrogen receptor-β, and (ii) predominantly hormone-dependent cancer targets were predicted for the anti-cancer compounds. Through the use of our in silico target prediction, we conclude that understanding how traditional medicine such as Ayurveda work through linking with the 'western' understanding of chemistry and protein targets can be a fruitful avenue in addition to bridging the gap between the two different schools of thinking. Given that compounds used in Ayurveda have been tested and used for thousands of years (although not in the same approach as Western medicine), they can potentially be developed into potential new drugs. Hence, to further advance the case of Ayurvedic medicine, we put forward some suggestions namely: (a) employing and integrating novel analytical methods given the advancements of 'omics' and (b) sharing experimental data and clinical results on studies done on Ayurvedic compounds in an easy and accessible way.
Fauzi FM, Koutsoukas A, Lowe R, et al., 2013, Chemogenomics Approaches to Rationalizing the Mode-of-Action of Traditional Chinese and Ayurvedic Medicines, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 53, Pages: 661-673, ISSN: 1549-9596
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- Citations: 41
Kirchmair J, Howlett A, Peironcely JE, et al., 2013, How Do Metabolites Differ from Their Parent Molecules and How Are They Excreted?, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 53, Pages: 354-367, ISSN: 1549-9596
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- Citations: 24
Mak L, Liggi S, Tan L, et al., 2013, Anti-cancer Drug Development: Computational Strategies to Identify and Target Proteins Involved in Cancer Metabolism, CURRENT PHARMACEUTICAL DESIGN, Vol: 19, Pages: 532-577, ISSN: 1381-6128
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- Citations: 27
Cortes-Ciriano I, Koutsoukas A, Abian O, et al., 2013, Experimental validation of <i>in silico</i> target predictions on synergistic protein targets, MEDCHEMCOMM, Vol: 4, Pages: 278-288, ISSN: 2040-2503
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- Citations: 6
Townsend JA, Glen RC, Mussa HY, 2012, Note on Naive Bayes Based on Binary Descriptors in Cheminformatics, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 52, Pages: 2494-2500, ISSN: 1549-9596
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- Citations: 7
Wang Z, Mussa HY, Lowe R, et al., 2012, Probability Based hERG Blocker Classifiers, MOLECULAR INFORMATICS, Vol: 31, Pages: 679-685, ISSN: 1868-1743
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- Citations: 3
Orchard S, Al-Lazikani B, Bryant S, et al., 2012, Shouldn't enantiomeric purity be included in the 'minimum information about a bioactive entity? Response from the MIABE group, NATURE REVIEWS DRUG DISCOVERY, Vol: 11, ISSN: 1474-1776
Glen RC, 2012, Language, semantics, and chemistry: Why computers need to say what we mean, 244th National Fall Meeting of the American-Chemical-Society (ACS), Publisher: AMER CHEMICAL SOC, ISSN: 0065-7727
Marcus D, Mussa HY, Bender A, et al., 2012, Exploring activity landscapes through molecular reference structures, 244th National Fall Meeting of the American-Chemical-Society (ACS), Publisher: AMER CHEMICAL SOC, ISSN: 0065-7727
Lowe DM, Murray-Rust P, Glen RC, 2012, Automated extraction of reactions from the patent literature, 11th International Biorelated Polymer Symposium / 243rd National Spring Meeting of the American-Chemical-Society (ACS), Publisher: AMER CHEMICAL SOC, ISSN: 0065-7727
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