Imperial College London

DrRobertPeach

Faculty of MedicineDepartment of Brain Sciences

Honorary Research Fellow
 
 
 
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Contact

 

r.peach13

 
 
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Location

 

ChemistrySouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

19 results found

Myall A, Price JR, Peach RL, Abbas M, Mookerjee S, Zhu N, Ahmad I, Ming D, Ramzan F, Teixeira D, Graf C, Weiße AY, Harbarth S, Holmes A, Barahona Met al., 2022, Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study., Lancet Digit Health, Vol: 4, Pages: e573-e583

BACKGROUND: Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level. METHODS: We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk. FINDINGS: The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0&

Journal article

Arnaudon A, Peach R, Petri G, Expert Pet al., 2022, Connecting Hodge and Sakaguchi-Kuramoto: a mathematical framework for coupled oscillators on simplicial complexes, Publisher: ArXiv

We formulate a general Kuramoto model on weighted simplicial complexes wherephases oscillators are supported on simplices of any order $k$. Crucially, weintroduce linear and non-linear frustration terms that are independent of theorientation of the $k+1$ simplices, providing a natural generalization of theSakaguchi-Kuramoto model. In turn, this provides a generalized formulation ofthe Kuramoto higher-order parameter as a potential function to write thedynamics as a gradient flow. With a selection of simplicial complexes ofincreasingly complex structure, we study the properties of the dynamics of thesimplicial Sakaguchi-Kuramoto model with oscillators on edges to highlight thecomplexity of dynamical behaviors emerging from even simple simplicialcomplexes. We place ourselves in the case where the vector of internalfrequencies of the edge oscillators lies in the kernel of the Hodge Laplacian,or vanishing linear frustration, and, using the Hodge decomposition of thesolution, we understand how the nonlinear frustration couples the dynamics inorthogonal subspaces. We discover various dynamical phenomena, such as thepartial loss of synchronization in subspaces aligned with the Hodge subspacesand the emergence of simplicial phase re-locking in regimes of highfrustration.

Working paper

Peach R, Arnaudon A, Barahona M, 2022, Relative, local and global dimension in complex networks, Nature Communications, Vol: 13, ISSN: 2041-1723

Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical spaces, which can be constrained by boundaries and distorted by inhomogeneities, or to intrinsically discrete systems such as networks. To take into account locality, finiteness and discreteness, dynamical processes can be used to probe the space geometry and define its dimension. Here we show that each point in space can be assigned a relative dimension with respect to the source of a diffusive process, a concept that provides a scale-dependent definition for local and global dimension also applicable to networks. To showcase its application to physical systems, we demonstrate that the local dimension of structural protein graphs correlates with structural flexibility, and the relative dimension with respect to the active site uncovers regions involved in allosteric communication. In simple models of epidemics on networks, the relative dimension is predictive of the spreading capability of nodes, and identifies scales at which the graph structure is predictive of infectivity. We further apply our dimension measures to neuronal networks, economic trade, social networks, ocean flows, and to the comparison of random graphs.

Journal article

Myall A, Price J, Peach R, Abbas M, Mookerjee S, Zhu N, Ahmad I, Ming D, Ramzan F, Teixeira D, Graf C, Weisse A, Harbarth S, Holmes A, Barahona Met al., 2022, Predicting hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study, The Lancet Digital Health, ISSN: 2589-7500

Background. Real-time prediction is key to prevention and control ofhealthcare-associated infections. Contacts drive many infections, yet most risk prediction frameworks fail to account for their dynamics. We develop, test and internationally validate a real-time machine learning framework, incorporating dynamic patient contact-networks to predict individual-level hospital-onset COVID-19 infections (HOCIs).Methods. Our framework extracts patient contact-networks from routinehospital-data and combines network-derived variables with clinical andcontextual information to predict individual infection risk. We train and test the framework on HOCIs using 51,157 hospital inpatients admitted to a UK National Health Service hospital group across the first two COVID-19 surges. We validate the framework using data from a Swiss hospital group during a COVID-19 surge (40,057 inpatients), and from the same UK group post COVID-19 surges (43,375 inpatients).Findings. The framework was highly predictive across test data using allvariables types (AUC-ROC 0·89 [0·88-0·90]) and similarly predictive using onlycontact-network variables (AUC-ROC 0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [0·80-0·84]) or patient clinical (AUC-ROC 0·64 [0·62-0·66]) variables. A model with only three variables (network closeness, direct contacts with infectious patients (network-derived), plus hospital COVID-19 prevalence (hospital-contextual)) achieved AUC-ROC 0·85 [0·82-0·88]. Incorporating contact-network variables improved performance across both validation datasets (Geneva: AUC-ROC increased from 0·84 [0·82–0·86] to 0·88 [0·86–0·90]; UK-post-surges: AUC-ROC increased from 0·52 [0·49–0·53] to 0·68 [0·64-0·70]).Interpretation. Dynamic contact-networks ar

Journal article

Myall A, Peach R, Wan Y, Mookerjee S, Jauneikaite E, Bolt F, Price J, Davies F, Weisse A, Holmes AH, Barahona Met al., 2022, Improved contact tracing using network analysis and spatial-temporal proximity, IMED conference, Publisher: ELSEVIER SCI LTD, Pages: S20-S20, ISSN: 1201-9712

Conference paper

Myall A, Price J, Peach R, Abbas M, Mookerjee S, Ahmad I, Ming D, Zhu NJ, Ramzan F, Weisse A, Holmes AH, Barahona Met al., 2022, Prediction of hospital-onset COVID-19 using networks of patient contact: an observational study, IMED conference, Publisher: ELSEVIER SCI LTD, Pages: S109-S110, ISSN: 1201-9712

Conference paper

Liu Z, Peach R, Lawrance E, Noble A, Ungless M, Barahona Met al., 2021, Listening to mental health crisis needs at scale: using Natural Language Processing to understand and evaluate a mental health crisis text messaging service, Frontiers in Digital Health, Vol: 3, Pages: 1-14, ISSN: 2673-253X

The current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with post-hoc analysis and evaluation of quality of service provision in digital mental health services.

Journal article

Ming DKY, Myall A, Hernandez B, Weisse A, Peach R, Barahona M, Rawson T, Holmes Aet al., 2021, Informing antimicrobial management in the context of COVID-19: understanding the longitudinal dynamics of C-reactive protein and procalcitonin, BMC Infectious Diseases, Vol: 21, ISSN: 1471-2334

Background:To characterise the longitudinal dynamics of C-reactive protein (CRP) and Procalcitonin (PCT) in a cohort of hospitalised patients with COVID-19 and support antimicrobial decision-making.Methods:Longitudinal CRP and PCT concentrations and trajectories of 237 hospitalised patients with COVID-19 were modelled. The dataset comprised of 2,021 data points for CRP and 284 points for PCT. Pairwise comparisons were performed between: (i) those with or without significant bacterial growth from cultures, and (ii) those who survived or died in hospital.Results:CRP concentrations were higher over time in COVID-19 patients with positive microbiology (day 9: 236 vs 123 mg/L, p < 0.0001) and in those who died (day 8: 226 vs 152 mg/L, p < 0.0001) but only after day 7 of COVID-related symptom onset. Failure for CRP to reduce in the first week of hospital admission was associated with significantly higher odds of death. PCT concentrations were higher in patients with COVID-19 and positive microbiology or in those who died, although these differences were not statistically significant.Conclusions:Both the absolute CRP concentration and the trajectory during the first week of hospital admission are important factors predicting microbiology culture positivity and outcome in patients hospitalised with COVID-19. Further work is needed to describe the role of PCT for co-infection. Understanding relationships of these biomarkers can support development of risk models and inform optimal antimicrobial strategies.

Journal article

Pardo O, Chrysostomou S, Roy R, Prischi F, Thamlikitkul L, Chapman KL, Mufti U, Peach R, Ding L, Hancock D, Moore C, Molina-Arcas M, Mauri F, Pinato DJ, Abrahams JM, Ottaviani S, Castellano L, Giamas G, Pascoe J, Moonamale D, Pirrie S, Gaunt C, Billingham L, Steven NM, Cullen M, Hrouda D, Winkler M, Post J, Cohen P, Salpeter SJ, Bar V, Zundelevich A, Golan S, Leibovici D, Lara R, Klug DR, Yaliraki SN, Barahona M, Wang Y, Downward J, Skehel JM, Ali MMU, Seckl MJet al., 2021, Repurposed floxacins targeting RSK4 prevent chemoresistance and metastasis in lung and bladder cancer, Science Translational Medicine, Vol: 13, ISSN: 1946-6234

Lung and bladder cancers are mostly incurable because of the early development of drug resistance and metastatic dissemination. Hence, improved therapies that tackle these two processes are urgently needed to improve clinical outcome. We have identified RSK4 as a promoter of drug resistance and metastasis in lung and bladder cancer cells. Silencing this kinase, through either RNA interference or CRISPR, sensitized tumor cells to chemotherapy and hindered metastasis in vitro and in vivo in a tail vein injection model. Drug screening revealed several floxacin antibiotics as potent RSK4 activation inhibitors, and trovafloxacin reproduced all effects of RSK4 silencing in vitro and in/ex vivo using lung cancer xenograft and genetically engineered mouse models and bladder tumor explants. Through x-ray structure determination and Markov transient and Deuterium exchange analyses, we identified the allosteric binding site and revealed how this compound blocks RSK4 kinase activation through binding to an allosteric site and mimicking a kinase autoinhibitory mechanism involving the RSK4’s hydrophobic motif. Last, we show that patients undergoing chemotherapy and adhering to prophylactic levofloxacin in the large placebo-controlled randomized phase 3 SIGNIFICANT trial had significantly increased (P = 0.048) long-term overall survival times. Hence, we suggest that RSK4 inhibition may represent an effective therapeutic strategy for treating lung and bladder cancer.

Journal article

Myall AC, Peach RL, Weiße AY, Davies F, Mookerjee S, Holmes A, Barahona Met al., 2021, Network memory in the movement of hospital patients carrying drug-resistant bacteria, Applied Network Science, Vol: 6, ISSN: 2364-8228

Hospitals constitute highly interconnected systems that bring into contact anabundance of infectious pathogens and susceptible individuals, thus makinginfection outbreaks both common and challenging. In recent years, there hasbeen a sharp incidence of antimicrobial-resistance amongsthealthcare-associated infections, a situation now considered endemic in manycountries. Here we present network-based analyses of a data set capturing themovement of patients harbouring drug-resistant bacteria across three largeLondon hospitals. We show that there are substantial memory effects in themovement of hospital patients colonised with drug-resistant bacteria. Suchmemory effects break first-order Markovian transitive assumptions andsubstantially alter the conclusions from the analysis, specifically on noderankings and the evolution of diffusive processes. We capture variable lengthmemory effects by constructing a lumped-state memory network, which we then useto identify overlapping communities of wards. We find that these communities ofwards display a quasi-hierarchical structure at different levels of granularitywhich is consistent with different aspects of patient flows related to hospitallocations and medical specialties.

Journal article

Peach R, Arnaudon A, Schmidt J, Palasciano HA, Bernier NR, Jelfs K, Yaliraki S, Barahona Met al., 2021, HCGA: Highly comparative graph analysis for network phenotyping, Patterns, Vol: 2, ISSN: 2666-3899

Networks are widely used as mathematical models of complex systems across many scientific disciplines. Decades of work have produced a vast corpus of research characterizing the topological, combinatorial, statistical, and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and sometimes overlapping) characteristics of a network. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph datasets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterization of graph datasets. We show that HCGA outperforms other methodologies on supervised classification tasks on benchmark datasets while retaining the interpretability of network features. We exemplify HCGA by predicting the charge transfer in organic semiconductors and clustering a dataset of neuronal morphology images.

Journal article

Myall A, Peach RL, Wan Y, Mookerjee S, Jauneikaite E, Bolt F, Price J, Davies F, Weiße AY, Holmes A, Barahona Met al., 2021, Characterising contact in disease outbreaks via a network model of spatial-temporal proximity

<jats:title>ABSTRACT</jats:title><jats:p>Contact tracing is a key tool in epidemiology to identify and control outbreaks of infectious diseases. Existing contact tracing methodologies produce contact maps of individuals based on a binary definition of contact which can be hampered by missing data and indirect contacts. Here, we present a Spatial-temporal Epidemiological Proximity (StEP) model to recover contact maps in disease outbreaks based on movement data. The StEP model accounts for imperfect data by considering probabilistic contacts between individuals based on spatial-temporal proximity of their movement trajectories, creating a robust movement network despite possible missing data and unseen transmission routes. Using real-world data we showcase the potential of StEP for contact tracing with outbreaks of multidrug-resistant bacteria and COVID-19 in a large hospital group in London, UK. In addition to the core structure of contacts that can be recovered using traditional methods of contact tracing, the StEP model reveals missing contacts that connect seemingly separate outbreaks. Comparison with genomic data further confirmed that these recovered contacts indeed improve characterisation of disease transmission and so highlights how the StEP framework can inform effective strategies of infection control and prevention.</jats:p>

Journal article

Peach R, Greenbury S, Johnston I, Yaliraki S, Lefevre D, Barahona Met al., 2021, Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation, Scientific Reports, Vol: 11, ISSN: 2045-2322

The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners’ behaviors, and to identify critical tasks and course sessions in a given course design. We also introduce a recently developed probabilistic Bayesian model to learn sequential behaviours of students and predict student performance. The application of our data-driven sequence-based analyses to data from learners undertaking an on-line Business Management course reveals distinct behaviors within the cohort of learners, identifying learners or groups of learners that deviate from the nominal order expected in the course. Using course grades a posteriori, we explore differences in behavior between high and low performing learners. We find that high performing learners follow the progression between weekly sessions more regularly than low performing learners, yet within each weekly session high performing learners are less tied to the nominal task order. We then model the sequences of high and low performance students using the probablistic Bayesian model and show that we can learn engagement behaviors associated with performance. We also show that the data sequence framework can be used for task-centric analysis; we identify critical junctures and differences among types of tasks within the course design. We find that non-rote learning tasks, such as interactive tasks or discussion posts, are correlated with higher performance. We discuss the application of such analytical techniques as an aid to course design, intervention, and student supervision.

Journal article

Peach R, Arnaudon A, Barahona M, 2020, Graph centrality is a question of scale, Physical Review Research, Vol: 2, ISSN: 2643-1564

Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A node is defined to be central if it breaks the metricity of the diffusion as a consequence of the effective boundaries and inhomogeneities in the graph. Our measure is naturally multiscale, as it is computed relative to graph neighbourhoods within the varying time horizon of the diffusion. We find that the centrality of nodes can differ widely at different scales. In particular, our measure correlates with degree (i.e., hubs) at small scales and with closeness (i.e., bridges) at large scales, and also reveals the existence of multi-centric structures in complex networks. By examining centrality across scales, our measure thus provides an evaluation of node importance relative to local and global processes on the network.

Journal article

Schreglmann S, Wang D, Peach R, Li J, Zhang X, Latorre A, Rhodes E, Panella E, Boyden E, Barahona M, Santaniello S, Bhatia K, Rothwell J, Grossman Net al., 2020, Non-invasive amelioration of essential tremor via phase-locked disruption of its temporal coherence, Nature Communications, Vol: 12, ISSN: 2041-1723

Abstract Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be noninvasively suppressed via electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained after the end of the stimulation and can be phenomenologically predicted. Finally, using feature-based statistical-learning and neurophysiological-modelling we show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the oscillation via perturbation of the tremor generating a cascade of synchronous activity in the olivocerebellar loop. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may represent a powerful neuromodulatory strategy to treat brain disorders.

Journal article

Peach RL, Arnaudon A, Barahona M, 2020, Semi-supervised classification on graphs using explicit diffusion dynamics, Foundations of Data Science, Vol: 2, Pages: 19-33, ISSN: 2639-8001

Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. Intuitively, the graph acts as a conduit to channel and bias the inference of class labels. Here, we study classification methods that consider the graph as the originator of an explicit graph diffusion. We show that appending graph diffusion to feature-based learning as a posteriori refinement achieves state-of-the-art classification accuracy. This method, which we call Graph Diffusion Reclassification (GDR), uses overshooting events of a diffusive graph dynamics to reclassify individual nodes. The method uses intrinsic measures of node influence, which are distinct for each node, and allows the evaluation of the relationship and importance of features and graph for classification. We also present diff-GCN, a simple extension of Graph Convolutional Neural Network (GCN) architectures that leverages explicit diffusion dynamics, and allows the natural use of directed graphs. To showcase our methods, we use benchmark datasets of documents with associated citation data.

Journal article

Peach RL, Saman D, Yaliraki SN, Klug DR, Ying L, Willison KR, Barahona Met al., 2020, Unsupervised Graph-Based Learning Predicts Mutations That Alter Protein Dynamics

<jats:title>A<jats:sc>bstract</jats:sc></jats:title><jats:p>Proteins exhibit complex dynamics across a vast range of time and length scales, from the atomistic to the conformational. Adenylate kinase (ADK) showcases the biological relevance of such inherently coupled dynamics across scales: single mutations can affect large-scale protein motions and enzymatic activity. Here we present a combined computational and experimental study of multiscale structure and dynamics in proteins, using ADK as our system of choice. We show how a computationally efficient method for unsupervised graph partitioning can be applied to atomistic graphs derived from protein structures to reveal intrinsic, biochemically relevant substructures at all scales, without re-parameterisation or<jats:italic>a priori</jats:italic>coarse-graining. We subsequently perform full alanine and arginine<jats:italic>in silico</jats:italic>mutagenesis scans of the protein, and score all mutations according to the disruption they induce on the large-scale organisation. We use our calculations to guide Förster Resonance Energy Transfer (FRET) experiments on ADK, and show that mutating residue D152 to alanine or residue V164 to arginine induce a large dynamical shift of the protein structure towards a closed state, in accordance with our predictions. Our computations also predict a graded effect of different mutations at the D152 site as a result of increased coherence between the core and binding domains, an effect confirmed quantitatively through a high correlation (<jats:italic>R</jats:italic><jats:sup>2</jats:sup>= 0.93) with the FRET ratio between closed and open populations measured on six mutants.</jats:p>

Journal article

Peach R, Yaliraki S, Lefevre D, Barahona Met al., 2019, Data-driven unsupervised clustering of online learner behaviour , npj Science of Learning, Vol: 4, ISSN: 2056-7936

The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here we introduce a mathematical framework for the analysis of time series of online learner engagement, which allows the identification of clusters of learners with similar online temporal behaviour directly from the raw data without prescribing a priori subjective reference behaviours. The method uses a dynamic time warping kernel to create a pairwise similarity between time series of learner actions, and combines it with an unsupervised multiscale graph clustering algorithm to identify groups of learners with similar temporal behaviour. To showcase our approach, we analyse task completion data from a cohort of learners taking an online post-graduate degree at Imperial Business School. Our analysis reveals clusters of learners with statistically distinct patterns of engagement, from distributed to massed learning, with different levels of regularity, adherence to pre-planned course structure and task completion. The approach also reveals outlier learners with highly sporadic behaviour. A posteriori comparison against student performance shows that, whereas high performing learners are spread across clusters with diverse temporal engagement, low performers are located significantly in the massed learning cluster, and our unsupervised clustering identifies low performers more accurately than common machine learning classification methods trained on temporal statistics of the data. Finally, we test the applicability of the method by analysing two additional datasets: a different cohort of the same course, and time series of different format from another university.

Journal article

Sowley H, Liu Z, Davies J, Peach R, Guo R, Sim S, Long F, Holdgate G, Willison K, Zhuang W, Klug Det al., 2019, Detection of drug binding to a target protein using EVV 2DIR spectroscopy, Journal of Physical Chemistry B, Vol: 123, Pages: 3598-3606, ISSN: 1520-5207

We demonstrate that Electron-Vibration-Vibration Two Dimensional Infrared Spectroscopy (EVV 2DIR) can be used to detect the binding of a drug to a target protein active site. The EVV 2DIR spectrum of the FGFR1 Kinase target protein is found to have ~200 detectable crosspeaks in the spectral region 1250 - 1750cm-1/2600 - 3400cm-1, with an additional 63 caused by the addition of a drug, SU5402. Of these 63 new peaks, it is shown that only 6 are due to protein-drug interactions, with the other 57 being due to vibrational coupling within the drug itself. Quantum mechanical calculations employing density functional theory are used to support assignment of the 6 binding-dependent peaks, with one being assigned to a known interaction between the drug and a backbone carbonyl group which forms part of the binding site. None of the 57 intramolecular coupling peaks associated with the drug molecule change substantially in either intensity or frequency when the drug binds to the target protein. This strongly suggests that the structure of the drug in the target binding site, is essentially identical to that when it is not bound.

Journal article

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