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Krishnan ST, Winkler D, Creek D, Anderson D, Kirana C, Maddern GJ, Fenix K, Hauben E, Rudd D, Voelcker NH. Staging of colorectal cancer using lipid biomarkers and machine learning. Metabolomics 2023; 19:84. [PMID: 37731020 PMCID: PMC10511619 DOI: 10.1007/s11306-023-02049-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 09/07/2023] [Indexed: 09/22/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Alteration in lipid metabolism and chemokine expression are considered hallmark characteristics of malignant progression and metastasis of CRC. Validated diagnostic and prognostic biomarkers are urgently needed to define molecular heterogeneous CRC clinical stages and subtypes, as liver dominant metastasis has poor survival outcomes. OBJECTIVES The aim of this study was to integrate lipid changes, concentrations of chemokines, such as platelet factor 4 and interleukin 8, and gene marker status measured in plasma samples, with clinical features from patients at different CRC stages or who had progressed to stage-IV colorectal liver metastasis (CLM). METHODS High-resolution liquid chromatography-mass spectrometry (HR-LC-MS) was used to determine the levels of candidate lipid biomarkers in each CRC patient's preoperative plasma samples and combined with chemokine, gene and clinical data. Machine learning models were then trained using known clinical outcomes to select biomarker combinations that best classify CRC stage and group. RESULTS Bayesian neural net and multilinear regression-machine learning identified candidate biomarkers that classify CRC (stages I-III), CLM patients and control subjects (cancer-free or patients with polyps/diverticulitis), showing that integrating specific lipid signatures and chemokines (platelet factor-4 and interluken-8; IL-8) can improve prognostic accuracy. Gene marker status could contribute to disease prediction, but requires ubiquitous testing in clinical cohorts. CONCLUSION Our findings demonstrate that correlating multiple disease related features with lipid changes could improve CRC prognosis. The identified signatures could be used as reference biomarkers to predict CRC prognosis and classify stages, and monitor therapeutic intervention.
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Affiliation(s)
- Sanduru Thamarai Krishnan
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
- Department of Chemistry, University of Reading, Whiteknights, Reading, RG6 6DX, UK
- Melbourne Centre for Nanofabrication, Victorian Node of the Australian National Fabrication Facility, 151 Wellington Road, Clayton, VIC, 3168, Australia
| | - David Winkler
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Bundoora, 3086, Australia
- School of Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham, NG7 2QL, UK
| | - Darren Creek
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
- Monash Proteomics and Metabolomics Facility, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
| | - Dovile Anderson
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
- Monash Proteomics and Metabolomics Facility, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
| | - Chandra Kirana
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Woodville, SA, 5011, Australia
| | - Guy J Maddern
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Woodville, SA, 5011, Australia
| | - Kevin Fenix
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Woodville, SA, 5011, Australia
| | - Ehud Hauben
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Woodville, SA, 5011, Australia
| | - David Rudd
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.
- Melbourne Centre for Nanofabrication, Victorian Node of the Australian National Fabrication Facility, 151 Wellington Road, Clayton, VIC, 3168, Australia.
| | - Nicolas Hans Voelcker
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.
- Melbourne Centre for Nanofabrication, Victorian Node of the Australian National Fabrication Facility, 151 Wellington Road, Clayton, VIC, 3168, Australia.
- Commonwealth Scientific and Industrial Research Organization (CSIRO), Clayton, VIC, 3168, Australia.
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2
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Contreas L, Hook AL, Winkler DA, Figueredo G, Williams P, Laughton CA, Alexander MR, Williams PM. Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates. ACS APPLIED MATERIALS & INTERFACES 2023; 15. [PMID: 36881023 PMCID: PMC10037238 DOI: 10.1021/acsami.2c23182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Bacterial infections are increasingly problematic due to the rise of antimicrobial resistance. Consequently, the rational design of materials naturally resistant to biofilm formation is an important strategy for preventing medical device-associated infections. Machine learning (ML) is a powerful method to find useful patterns in complex data from a wide range of fields. Recent reports showed how ML can reveal strong relationships between bacterial adhesion and the physicochemical properties of polyacrylate libraries. These studies used robust and predictive nonlinear regression methods that had better quantitative prediction power than linear models. However, as nonlinear models' feature importance is a local rather than global property, these models were hard to interpret and provided limited insight into the molecular details of material-bacteria interactions. Here, we show that the use of interpretable mass spectral molecular ions and chemoinformatic descriptors and a linear binary classification model of attachment of three common nosocomial pathogens to a library of polyacrylates can provide improved guidance for the design of more effective pathogen-resistant coatings. Relevant features from each model were analyzed and correlated with easily interpretable chemoinformatic descriptors to derive a small set of rules that give model features tangible meaning that elucidate relationships between the structure and function. The results show that the attachment of Pseudomonas aeruginosa and Staphylococcus aureus can be robustly predicted by chemoinformatic descriptors, suggesting that the obtained models can predict the attachment response to polyacrylates to identify anti-attachment materials to synthesize and test in the future.
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Affiliation(s)
- Leonardo Contreas
- School
of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Andrew L. Hook
- School
of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - David A. Winkler
- School
of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
- Monash
Institute of Pharmaceutical Sciences, Monash
University, Parkville, Victoria 3052, Australia
- Department
of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Bundoora, Victoria 3086, Australia
| | - Grazziela Figueredo
- School
of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom
| | - Paul Williams
- National
Biofilms Innovation Centre and Biodiscovery Institute, School of Life
Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Charles A. Laughton
- School
of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Morgan R. Alexander
- School
of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Philip M. Williams
- School
of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
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3
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Ye S, Meftahi N, Lyskov I, Tian T, Whitfield R, Kumar S, Christofferson AJ, Winkler DA, Shih CJ, Russo S, Leroux JC, Bao Y. Machine learning-assisted exploration of a versatile polymer platform with charge transfer-dependent full-color emission. Chem 2023. [DOI: 10.1016/j.chempr.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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4
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Kadaoluwa Pathirannahalage SP, Meftahi N, Elbourne A, Weiss ACG, McConville CF, Padua A, Winkler DA, Costa Gomes M, Greaves TL, Le TC, Besford QA, Christofferson AJ. Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations. J Chem Inf Model 2021; 61:4521-4536. [PMID: 34406000 DOI: 10.1021/acs.jcim.1c00794] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter-property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.
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Affiliation(s)
- Sachini P Kadaoluwa Pathirannahalage
- School of Science, RMIT University, Melbourne, Victoria 3000, Australia.,Laboratoire de Chimie, Ecole Normale Supérieure de Lyon, CNRS, Lyon 69342, France
| | - Nastaran Meftahi
- ARC Centre of Excellence in Exciton Science, School of Science, RMIT University, Melbourne, Victoria 3000, Australia
| | - Aaron Elbourne
- School of Science, RMIT University, Melbourne, Victoria 3000, Australia
| | - Alessia C G Weiss
- Leibniz-Institut für Polymerforschung e.V., Hohe Straße 6, 01069 Dresden, Germany
| | - Chris F McConville
- School of Science, RMIT University, Melbourne, Victoria 3000, Australia.,Institute for Frontier Materials, Deakin University, Geelong, Victoria 3220, Australia
| | - Agilio Padua
- Laboratoire de Chimie, Ecole Normale Supérieure de Lyon, CNRS, Lyon 69342, France
| | - David A Winkler
- School of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Bundoora, Victoria 3086, Australia.,Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia.,School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
| | | | - Tamar L Greaves
- School of Science, RMIT University, Melbourne, Victoria 3000, Australia
| | - Tu C Le
- School of Engineering, RMIT University, Melbourne, Victoria 3001, Australia
| | - Quinn A Besford
- Leibniz-Institut für Polymerforschung e.V., Hohe Straße 6, 01069 Dresden, Germany
| | - Andrew J Christofferson
- School of Science, RMIT University, Melbourne, Victoria 3000, Australia.,ARC Centre of Excellence in Exciton Science, School of Science, RMIT University, Melbourne, Victoria 3000, Australia
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5
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Greaves TL, Schaffarczyk McHale KS, Burkart-Radke RF, Harper JB, Le TC. Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids. Phys Chem Chem Phys 2021; 23:2742-2752. [PMID: 33496292 DOI: 10.1039/d0cp04227g] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The ability to tailor the constituent ions in ionic liquids (ILs) is highly advantageous as it provides access to solvents with a range of physicochemical properties. However, this benefit also leads to large compositional spaces that need to be explored to optimise systems, often involving time consuming experimental work. The use of machine learning methods is an effective way to gain insight based on existing data, to develop structure-property relationships and to allow the prediction of ionic liquid properties. Here we have applied machine learning models to experimentally determined rate constants of a representative organic process (the reaction of pyridine with benzyl bromide) in IL-acetonitrile mixtures. Multiple linear regression (MLREM) and artificial neural networks (BRANNLP) were both able to model the data well. The MLREM model was able to identify the structural features on the cations and anions that had the greatest effect on the rate constant. Secondly, predictive MLREM and BRANNLP models were developed from the full initial set of rate constant data. From these models, a large number of predictions (>9000) of rate constant were made for mixtures of different ionic liquids, at different proportions of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested experimentally, including through the preparation of novel ionic liquids, with overall good agreement between the predicted and experimental data. This study highlights the benefits of using machine learning methods on kinetic data in ionic liquid mixtures to enable the development of rigorous structure-property relationships across multiple variables simultaneously, and to predict properties of new ILs and experimental conditions.
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Affiliation(s)
- Tamar L Greaves
- College of Science Engineering and Health, RMIT University, Melbourne, VIC 3001, Australia.
| | | | | | - Jason B Harper
- School of Chemistry, University of New South Wales, Sydney, NSW 2052, Australia.
| | - Tu C Le
- College of Science Engineering and Health, RMIT University, Melbourne, VIC 3001, Australia.
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6
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Afantitis A, Melagraki G, Isigonis P, Tsoumanis A, Varsou DD, Valsami-Jones E, Papadiamantis A, Ellis LJA, Sarimveis H, Doganis P, Karatzas P, Tsiros P, Liampa I, Lobaskin V, Greco D, Serra A, Kinaret PAS, Saarimäki LA, Grafström R, Kohonen P, Nymark P, Willighagen E, Puzyn T, Rybinska-Fryca A, Lyubartsev A, Alstrup Jensen K, Brandenburg JG, Lofts S, Svendsen C, Harrison S, Maier D, Tamm K, Jänes J, Sikk L, Dusinska M, Longhin E, Rundén-Pran E, Mariussen E, El Yamani N, Unger W, Radnik J, Tropsha A, Cohen Y, Leszczynski J, Ogilvie Hendren C, Wiesner M, Winkler D, Suzuki N, Yoon TH, Choi JS, Sanabria N, Gulumian M, Lynch I. NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment. Comput Struct Biotechnol J 2020; 18:583-602. [PMID: 32226594 PMCID: PMC7090366 DOI: 10.1016/j.csbj.2020.02.023] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 02/18/2020] [Accepted: 02/29/2020] [Indexed: 01/26/2023] Open
Abstract
Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.
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Key Words
- (quantitative) Structure–activity relationships
- AI, Artificial Intelligence
- AOPs, Adverse Outcome Pathways
- API, Application Programming interface
- CG, coarse-grained (model)
- CNTs, carbon nanotubes
- Computational toxicology
- Engineered nanomaterials
- FAIR, Findable Accessible Inter-operable and Re-usable
- GUI, Graphical Processing Unit
- HOMO-LUMO, Highest Occupied Molecular Orbital Lowest Unoccupied Molecular Orbital
- Hazard assessment
- IATA, Integrated Approaches to Testing and Assessment
- Integrated approach for testing and assessment
- KE, key events
- MIE, molecular initiating events
- ML, machine learning
- MOA, mechanism (mode) of action
- MWCNT, multi-walled carbon nanotubes
- Machine learning
- NMs, nanomaterials
- Nanoinformatics
- OECD, Organisation for Economic Co-operation and Development
- PBPK, Physiologically Based PharmacoKinetics
- PC, Protein Corona
- PChem, Physicochemical
- PTGS, Predictive Toxicogenomics Space
- Predictive modelling
- QC, quantum-chemical
- QM, quantum-mechanical
- QSAR, quantitative structure-activity relationship
- QSPR, quantitative structure-property relationship
- RA, risk assessment
- REST, Representational State Transfer
- ROS, reactive oxygen species
- Read across
- SAR, structure-activity relationship
- SMILES, Simplified Molecular Input Line Entry System
- SOPs, standard operating procedures
- Safe-by-design
- Toxicogenomics
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Affiliation(s)
| | | | | | | | | | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Anastasios Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Laura-Jayne A. Ellis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Pantelis Karatzas
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Periklis Tsiros
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Vladimir Lobaskin
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Dario Greco
- Faculty of Medicine and Health Technology, University of Tampere, FI-33014, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, University of Tampere, FI-33014, Finland
| | | | | | - Roland Grafström
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Pekka Kohonen
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Penny Nymark
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Egon Willighagen
- Department of Bioinformatics – BiGCaT, School of Nutrition and Translational Research in Metabolism, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | | | - Alexander Lyubartsev
- Institutionen för material- och miljökemi, Stockholms Universitet, 106 91 Stockholm, Sweden
| | - Keld Alstrup Jensen
- The National Research Center for the Work Environment, Lersø Parkallé 105, 2100 Copenhagen, Denmark
| | - Jan Gerit Brandenburg
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Germany
- Chief Digital Organization, Merck KGaA, Frankfurter Str. 250, 64293 Darmstadt, Germany
| | - Stephen Lofts
- UK Centre for Ecology and Hydrology, Library Ave, Bailrigg, Lancaster LA1 4AP, UK
| | - Claus Svendsen
- UK Centre for Ecology and Hydrology, MacLean Bldg, Benson Ln, Crowmarsh Gifford, Wallingford OX10 8BB, UK
| | - Samuel Harrison
- UK Centre for Ecology and Hydrology, Library Ave, Bailrigg, Lancaster LA1 4AP, UK
| | - Dieter Maier
- Biomax Informatics AG, Robert-Koch-Str. 2, 82152 Planegg, Germany
| | - Kaido Tamm
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Jaak Jänes
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Lauri Sikk
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Maria Dusinska
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Eleonora Longhin
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Elise Rundén-Pran
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Espen Mariussen
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Naouale El Yamani
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Wolfgang Unger
- Federal Institute for Material Testing and Research (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Jörg Radnik
- Federal Institute for Material Testing and Research (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Alexander Tropsha
- Eschelman School of Pharmacy, University of North Carolina at Chapel Hill, 100K Beard Hall, CB# 7568, Chapel Hill, NC 27955-7568, USA
| | - Yoram Cohen
- Samueli School Of Engineering, University of California, Los Angeles, 5531 Boelter Hall, Los Angeles, CA 90095, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J. R. Lynch Street, Jackson, MS 39217, USA
| | - Christine Ogilvie Hendren
- Center for Environmental Implications of Nanotechnologies, Duke University, 121 Hudson Hall, Durham, NC 27708-0287, USA
| | - Mark Wiesner
- Center for Environmental Implications of Nanotechnologies, Duke University, 121 Hudson Hall, Durham, NC 27708-0287, USA
| | - David Winkler
- La Trobe Institute of Molecular Sciences, La Trobe University, Plenty Rd & Kingsbury Dr, Bundoora, VIC 3086, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia
- CSIRO Data61, Clayton 3168, Australia
- School of Pharmacy, University of Nottingham, Nottingham, UK
| | - Noriyuki Suzuki
- National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-0053, Japan
| | - Tae Hyun Yoon
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Republic of Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of Korea
| | - Jang-Sik Choi
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of Korea
| | - Natasha Sanabria
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa
| | - Mary Gulumian
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa
- Haematology and Molecular Medicine, University of the Witwatersrand, Johannesburg, South Africa
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
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7
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Yalcin D, Le TC, Drummond CJ, Greaves TL. Machine Learning Approaches for Further Developing the Understanding of the Property Trends Observed in Protic Ionic Liquid Containing Solvents. J Phys Chem B 2019; 123:4085-4097. [DOI: 10.1021/acs.jpcb.9b02072] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Dilek Yalcin
- School of Science, College of Science, Engineering and Health, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Tu C. Le
- School of Engineering, College of Science, Engineering and Health, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Calum J. Drummond
- School of Science, College of Science, Engineering and Health, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Tamar L. Greaves
- School of Science, College of Science, Engineering and Health, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
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8
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Le TC, Penna M, Winkler DA, Yarovsky I. Quantitative design rules for protein-resistant surface coatings using machine learning. Sci Rep 2019; 9:265. [PMID: 30670792 PMCID: PMC6342937 DOI: 10.1038/s41598-018-36597-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 11/23/2018] [Indexed: 12/31/2022] Open
Abstract
Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein adsorption is a crucial mediator of the interactions at the bio - nano -materials interface but is not well understood. Although general, empirical rules have been developed to guide the design of protein-resistant surface coatings, they are still largely qualitative. Herein we demonstrate that this knowledge gap can be addressed by using machine learning approaches to extract quantitative relationships between the material surface chemistry and the protein adsorption characteristics. We illustrate how robust linear and non-linear models can be constructed to accurately predict the percentage of protein adsorbed onto these surfaces using lysozyme or fibrinogen as prototype common contaminants. Our computational models could recapitulate the adsorption of proteins on functionalised surfaces in a test set with an r2 of 0.82 and standard error of prediction of 13%. Using the same data set that enabled the development of the Whitesides rules, we discovered an extension to the original rules. We describe a workflow that can be applied to large, consistently obtained data sets covering a broad range of surface functional groups and protein types.
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Affiliation(s)
- Tu C Le
- School of Engineering, RMIT University, GPO Box 2476, Melbourne, Victoria, 3001, Australia.
| | - Matthew Penna
- School of Engineering, RMIT University, GPO Box 2476, Melbourne, Victoria, 3001, Australia
- ARC Industrial Transformation Research Hub for Australian Steel Manufacturing, Wollongong, NSW, 2522, Australia
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
- La Trobe Institute for Molecular Science, La Trobe University, Bundoora, Victoria, 3084, Australia
- CSIRO Manufacturing, Clayton, Victoria, 3168, Australia
- School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Irene Yarovsky
- School of Engineering, RMIT University, GPO Box 2476, Melbourne, Victoria, 3001, Australia.
- ARC Industrial Transformation Research Hub for Australian Steel Manufacturing, Wollongong, NSW, 2522, Australia.
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9
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Rasi Ghaemi S, Delalat B, Gronthos S, Alexander MR, Winkler DA, Hook AL, Voelcker NH. High-Throughput Assessment and Modeling of a Polymer Library Regulating Human Dental Pulp-Derived Stem Cell Behavior. ACS APPLIED MATERIALS & INTERFACES 2018; 10:38739-38748. [PMID: 30351898 DOI: 10.1021/acsami.8b12473] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The identification of biomaterials that modulate cell responses is a crucial task for tissue engineering and cell therapy. The identification of novel materials is complicated by the immense number of synthesizable polymers and the time required for testing each material experimentally. In the current study, polymeric biomaterial-cell interactions were assessed rapidly using a microarray format. The attachment, proliferation, and differentiation of human dental pulp stem cells (hDPSCs) were investigated on 141 homopolymers and 400 diverse copolymers. The copolymer of isooctyl acrylate and 2-(methacryloyloxy)ethyl acetoacetate achieved the highest attachment and proliferation of hDPSC, whereas high cell attachment and differentiation of hDPSC were observed on the copolymer of isooctyl acrylate and trimethylolpropane ethoxylate triacrylate. Computational models were generated, relating polymer properties to cellular responses. These models could accurately predict cell behavior for up to 95% of materials within a test set. The models identified several functional groups as being important for supporting specific cell responses. In particular, oxygen-containing chemical moieties, including fragments from the acrylate/acrylamide backbone of the polymers, promoted cell attachment. Small hydrocarbon fragments originating from polymer pendant groups promoted cell proliferation and differentiation. These computational models constitute a key tool to direct the discovery of novel materials within the enormous chemical space available to researchers.
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Affiliation(s)
- Soraya Rasi Ghaemi
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Future Industries Institute , University of South Australia , Mawson Lakes , South Australia 5095 , Australia
| | - Bahman Delalat
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Future Industries Institute , University of South Australia , Mawson Lakes , South Australia 5095 , Australia
- Manufacturing , Commonwealth Scientific and Industrial Research Organization (CSIRO) , Clayton , Victoria 3168 , Australia
| | - Stan Gronthos
- Adelaide Medical School, Faculty of Health and Medical Sciences , University of Adelaide , Adelaide , South Australia 5005 , Australia
| | - Morgan R Alexander
- Advanced Materials and Healthcare Technologies , University of Nottingham , Nottingham NG7 2RD , U.K
| | - David A Winkler
- Manufacturing , Commonwealth Scientific and Industrial Research Organization (CSIRO) , Clayton , Victoria 3168 , Australia
- Adelaide Medical School, Faculty of Health and Medical Sciences , University of Adelaide , Adelaide , South Australia 5005 , Australia
- Biochemistry and Genetics, La Trobe Institute for Molecular Science , La Trobe University , Bundoora , Victoria 3086 , Australia
| | - Andrew L Hook
- Advanced Materials and Healthcare Technologies , University of Nottingham , Nottingham NG7 2RD , U.K
| | - Nicolas H Voelcker
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Future Industries Institute , University of South Australia , Mawson Lakes , South Australia 5095 , Australia
- Manufacturing , Commonwealth Scientific and Industrial Research Organization (CSIRO) , Clayton , Victoria 3168 , Australia
- Victorian Node of the Australian National Fabrication Facility , Melbourne Centre for Nanofabrication , Clayton , Victoria 3168 , Australia
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10
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Madiona RMT, Bamford SE, Winkler DA, Muir BW, Pigram PJ. Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using Self-Organizing Maps and a Universal Data Matrix. Anal Chem 2018; 90:12475-12484. [DOI: 10.1021/acs.analchem.8b01951] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Robert M. T. Madiona
- Centre for Materials and Surface Science and Department of Chemistry and Physics, School of Molecular Sciences, La Trobe University, Melbourne, VIC 3086, Australia
- CSIRO Manufacturing, Clayton, VIC 3168, Australia
| | - Sarah E. Bamford
- Centre for Materials and Surface Science and Department of Chemistry and Physics, School of Molecular Sciences, La Trobe University, Melbourne, VIC 3086, Australia
| | - David A. Winkler
- La Trobe Institute for Molecular Sciences, School of Molecular Sciences, La Trobe University, Melbourne, VIC 3086, Australia
- CSIRO Manufacturing, Clayton, VIC 3168, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, U.K
| | | | - Paul J. Pigram
- Centre for Materials and Surface Science and Department of Chemistry and Physics, School of Molecular Sciences, La Trobe University, Melbourne, VIC 3086, Australia
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11
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Sparse QSAR modelling methods for therapeutic and regenerative medicine. J Comput Aided Mol Des 2018; 32:497-509. [DOI: 10.1007/s10822-018-0106-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Accepted: 02/08/2018] [Indexed: 10/18/2022]
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12
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Mikulskis P, Hook A, Dundas AA, Irvine D, Sanni O, Anderson D, Langer R, Alexander MR, Williams P, Winkler DA. Prediction of Broad-Spectrum Pathogen Attachment to Coating Materials for Biomedical Devices. ACS APPLIED MATERIALS & INTERFACES 2018; 10:139-149. [PMID: 29191009 PMCID: PMC7613461 DOI: 10.1021/acsami.7b14197] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Bacterial infections in healthcare settings are a frequent accompaniment to both routine procedures such as catheterization and surgical site interventions. Their impact is becoming even more marked as the numbers of medical devices that are used to manage chronic health conditions and improve quality of life increases. The resistance of pathogens to multiple antibiotics is also increasing, adding an additional layer of complexity to the problems of employing safe and effective medical procedures. One approach to reducing the rate of infections associated with implanted and indwelling medical devices is the use of polymers that resist the formation of bacterial biofilms. To significantly accelerate the discovery of such materials, we show how state of the art machine learning methods can generate quantitative predictions for the attachment of multiple pathogens to a large library of polymers in a single model for the first time. Such models facilitate design of polymers with very low pathogen attachment across different bacterial species that will be candidate materials for implantable or indwelling medical devices such as urinary catheters, cochlear implants, and pacemakers.
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Affiliation(s)
- Paulius Mikulskis
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Andrew Hook
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Adam A. Dundas
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
- Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Derek Irvine
- Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Olutoba Sanni
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Daniel Anderson
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts 02139-4307, United States
| | - Robert Langer
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts 02139-4307, United States
| | - Morgan R. Alexander
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
- Corresponding Authors; ;
| | - Paul Williams
- Centre for Biomolecular Sciences, School of Life Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - David A. Winkler
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
- Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Kingsbury Drive, Melbourne, Victoria 3086, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Chemical and Physical Sciences, Flinders University, Bedford Park, South Australia 5046, Australia
- Corresponding Authors; ;
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13
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Xu Y, Ma J, Liaw A, Sheridan RP, Svetnik V. Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships. J Chem Inf Model 2017; 57:2490-2504. [PMID: 28872869 DOI: 10.1021/acs.jcim.7b00087] [Citation(s) in RCA: 132] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision1,2 and natural language processing.3,4 In the past four years, DNNs have also generated promising results for quantitative structure-activity relationship (QSAR) tasks.5,6 Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR data sets. It was also found that multitask DNN models-those trained on and predicting multiple QSAR properties simultaneously-outperform DNNs trained separately on the individual data sets in many, but not all, tasks. To date there has been no satisfactory explanation of why the QSAR of one task embedded in a multitask DNN can borrow information from other unrelated QSAR tasks. Thus, using multitask DNNs in a way that consistently provides a predictive advantage becomes a challenge. In this work, we explored why multitask DNNs make a difference in predictive performance. Our results show that during prediction a multitask DNN does borrow "signal" from molecules with similar structures in the training sets of the other tasks. However, whether this borrowing leads to better or worse predictive performance depends on whether the activities are correlated. On the basis of this, we have developed a strategy to use multitask DNNs that incorporate prior domain knowledge to select training sets with correlated activities, and we demonstrate its effectiveness on several examples.
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Affiliation(s)
- Yuting Xu
- Biometrics Research Department, Merck & Co., Inc. , Rahway, New Jersey 07065, United States
| | - Junshui Ma
- Biometrics Research Department, Merck & Co., Inc. , Rahway, New Jersey 07065, United States
| | - Andy Liaw
- Biometrics Research Department, Merck & Co., Inc. , Rahway, New Jersey 07065, United States
| | - Robert P Sheridan
- Modeling and Informatics Department, Merck & Co., Inc. , Kenilworth, New Jersey 07033, United States
| | - Vladimir Svetnik
- Biometrics Research Department, Merck & Co., Inc. , Rahway, New Jersey 07065, United States
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14
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Winkler DA. Biomimetic molecular design tools that learn, evolve, and adapt. Beilstein J Org Chem 2017; 13:1288-1302. [PMID: 28694872 PMCID: PMC5496546 DOI: 10.3762/bjoc.13.125] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 06/09/2017] [Indexed: 12/17/2022] Open
Abstract
A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known "S curve", with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.
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Affiliation(s)
- David A Winkler
- CSIRO Manufacturing, Bayview Avenue, Clayton 3168, Australia
- Monash Institute of Pharmaceutical Sciences, 392 Royal Parade, Parkville 3052, Australia
- Department of Chemistry and Physics, La Trobe Institute for Molecular Science, La Trobe University, Kingsbury Drive, Melbourne, Victoria 3086, Australia
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15
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Winkler DA, Le TC. Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR. Mol Inform 2016; 36. [DOI: 10.1002/minf.201600118] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 10/04/2016] [Indexed: 12/17/2022]
Affiliation(s)
- David A. Winkler
- CSIRO Manufacturing; Clayton 3168 Australia
- Monash Institute of Pharmaceutical Sciences; Monash University; Parkville 3052 Australia
- Latrobe Institute for Molecular Science; Latrobe University; Bundoora 3082 Australia
- School of Chemical and Physical Science; Flinders University; Bedford Park 5042 Australia
| | - Tu C. Le
- CSIRO Manufacturing; Clayton 3168 Australia
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16
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Jones DE, Ghandehari H, Facelli JC. A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 132:93-103. [PMID: 27282231 PMCID: PMC4902872 DOI: 10.1016/j.cmpb.2016.04.025] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Revised: 04/11/2016] [Accepted: 04/22/2016] [Indexed: 05/08/2023]
Abstract
This article presents a comprehensive review of applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles of medical interest. The papers reviewed here present the results of research using these techniques to predict the biological fate and properties of a variety of nanoparticles relevant to their biomedical applications. These include the influence of particle physicochemical properties on cellular uptake, cytotoxicity, molecular loading, and molecular release in addition to manufacturing properties like nanoparticle size, and polydispersity. Overall, the results are encouraging and suggest that as more systematic data from nanoparticles becomes available, machine learning and data mining would become a powerful aid in the design of nanoparticles for biomedical applications. There is however the challenge of great heterogeneity in nanoparticles, which will make these discoveries more challenging than for traditional small molecule drug design.
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Affiliation(s)
- David E Jones
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA
| | - Hamidreza Ghandehari
- Departments of Bioengineering and Pharmaceutics and Pharmaceutical Chemistry, University of Utah, Salt Lake City, UT 84112, USA; Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA
| | - Julio C Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA; Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA.
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17
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Chen YS. A comprehensive identification-evidence based alternative for HIV/AIDS treatment with HAART in the healthcare industries. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 131:111-126. [PMID: 27265053 DOI: 10.1016/j.cmpb.2016.04.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Revised: 03/03/2016] [Accepted: 04/01/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVE The HIV/AIDS-related issue has given rise to a priority concern in which potential new therapies are increasingly highlighted to lessen the negative impact of highly active anti-retroviral therapy (HAART) in the healthcare industry. With the motivation of "medical applications," this study focuses on the main advanced feature selection techniques and classification approaches that reflect a new architecture, and a trial to build a hybrid model for interested parties. METHODS This study first uses an integrated linear-nonlinear feature selection technique to identify the determinants influencing HAART medication and utilizes organizations of different condition-attributes to generate a hybrid model based on a rough set classifier to study evolving HIV/AIDS research in order to improve classification performance. RESULTS The proposed model makes use of a real data set from Taiwan's specialist medical center. The experimental results show that the proposed model yields a satisfactory result that is superior to the listed methods, and the core condition-attributes PVL, CD4, Code, Age, Year, PLT, and Sex were identified in the HIV/AIDS data set. In addition, the decision rule set created can be referenced as a knowledge-based healthcare service system as the best of evidence-based practices in the workflow of current clinical diagnosis. CONCLUSIONS This study highlights the importance of these key factors and provides the rationale that the proposed model is an effective alternative to analyzing sustained HAART medication in follow-up studies of HIV/AIDS treatment in practice.
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Affiliation(s)
- You-Shyang Chen
- Department of Information Management, Hwa Hsia University of Technology, 111, Gongzhuan Rd., Zhonghe Dist., New Taipei City 235, Taiwan.
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18
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Le TC, Yin H, Chen R, Chen Y, Zhao L, Casey PS, Chen C, Winkler DA. An Experimental and Computational Approach to the Development of ZnO Nanoparticles that are Safe by Design. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2016; 12:3568-77. [PMID: 27167706 DOI: 10.1002/smll.201600597] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 04/05/2016] [Indexed: 05/10/2023]
Abstract
Zinc oxide nanoparticles have found wide application due to their unique optoelectronic and photocatalytic characteristics. However, their safety aspects remain of critical concern, prompting the use of physicochemical modifications of pristine ZnO to reduce any potential toxicity. However, the relationships between these modifications and their effects on biology are complex and still relatively unexplored. To address this knowledge gap, a library of 45 types of ZnO nanoparticles with varying particle size, aspect ratio, doping type, doping concentration, and surface coating is synthesized, and their biological effects measured. Three biological assays measuring cell damage or stress are used to study the responses of human umbilical vein endothelial cells (HUVECs) or human hepatocellular liver carcinoma cells (HepG2) to the nanoparticles. These experimental data are used to develop quantitative and predictive computational models linking nanoparticle properties to cell viability, membrane integrity, and oxidative stress. It is found that the concentration of nanoparticles the cells are exposed to, the type of surface coating, the nature and extent of doping, and the aspect ratio of the particles make significant contributions to the cell toxicity of the nanoparticles tested. Our study shows that it is feasible to generate models that could be used to design or optimize nanoparticles with commercially useful properties that are also safe to humans and the environment.
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Affiliation(s)
- Tu C Le
- CSIRO Manufacturing, Bayview Avenue, Clayton, 3168, Australia
| | - Hong Yin
- CSIRO Manufacturing, Bayview Avenue, Clayton, 3168, Australia
| | - Rui Chen
- CAS Key Lab for Biomedical Effects of Nanomaterials and Nanosafety and CAS Centre for Excellence in Nanoscience, National Centre for Nanoscience and Technology of China, Beijing, 100190, China
| | - Yandong Chen
- CAS Key Lab for Biomedical Effects of Nanomaterials and Nanosafety and CAS Centre for Excellence in Nanoscience, National Centre for Nanoscience and Technology of China, Beijing, 100190, China
| | - Lin Zhao
- CAS Key Lab for Biomedical Effects of Nanomaterials and Nanosafety and CAS Centre for Excellence in Nanoscience, National Centre for Nanoscience and Technology of China, Beijing, 100190, China
| | - Philip S Casey
- CSIRO Manufacturing, Bayview Avenue, Clayton, 3168, Australia
| | - Chunying Chen
- CAS Key Lab for Biomedical Effects of Nanomaterials and Nanosafety and CAS Centre for Excellence in Nanoscience, National Centre for Nanoscience and Technology of China, Beijing, 100190, China
| | - David A Winkler
- CSIRO Manufacturing, Bayview Avenue, Clayton, 3168, Australia
- Monash Institute of Pharmaceutical Sciences, 381 Royal Parade, Parkville, VIC, 3052, Australia
- Latrobe Institute for Molecular Science, La Trobe University, Bundoora, VIC, 3046, Australia
- School of Chemical and Physical Sciences, Flinders University, Bedford Park, 5042, Australia
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19
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Dobchev D, Karelson M. Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework? Expert Opin Drug Discov 2016; 11:627-39. [PMID: 27149299 DOI: 10.1080/17460441.2016.1186876] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. AREAS COVERED In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. EXPERT OPINION The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.
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Affiliation(s)
- Dimitar Dobchev
- a Department of Chemistry , Tallinn University of Technology , Tallinn , Estonia
| | - Mati Karelson
- b Institute of Chemistry , University of Tartu , Tartu , Estonia
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20
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Affiliation(s)
- Toshio Fujita
- Professor Emeritus at Kyoto University, 38-1 Iwakura-Miyakecho, Kyoto, Japan 606-0022
| | - David A. Winkler
- CSIRO Manufacturing, Bag 10, Clayton South MDC 3169, Australia
- Monash Institute of Pharmaceutical Sciences, 392 Royal Parade, Parkville 3052, Australia
- Latrobe
Institute for Molecular Science, Latrobe University, Bundoora 3086, Australia
- School
of Chemical and Physical Sciences, Flinders University, Bedford Park 5042, Australia
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21
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Burden FR, Winkler DA. Relevance Vector Machines: Sparse Classification Methods for QSAR. J Chem Inf Model 2015; 55:1529-34. [PMID: 26158341 DOI: 10.1021/acs.jcim.5b00261] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Frank R. Burden
- CSIRO Manufacturing
Flagship, Clayton South, Victoria 3169, Australia
- Monash Institute of Pharmaceutical Sciences, Parkville, Victoria 3052, Australia
| | - David A. Winkler
- CSIRO Manufacturing
Flagship, Clayton South, Victoria 3169, Australia
- Monash Institute of Pharmaceutical Sciences, Parkville, Victoria 3052, Australia
- Latrobe Institute for Molecular Science, Bundoora, Victoria 3083, Australia
- School
of Chemical and Physical Sciences, Flinders University, Bedford Park, South Australia 5042, Australia
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22
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Alexander DLJ, Tropsha A, Winkler DA. Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models. J Chem Inf Model 2015; 55:1316-22. [PMID: 26099013 DOI: 10.1021/acs.jcim.5b00206] [Citation(s) in RCA: 330] [Impact Index Per Article: 36.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The statistical metrics used to characterize the external predictivity of a model, i.e., how well it predicts the properties of an independent test set, have proliferated over the past decade. This paper clarifies some apparent confusion over the use of the coefficient of determination, R(2), as a measure of model fit and predictive power in QSAR and QSPR modeling. R(2) (or r(2)) has been used in various contexts in the literature in conjunction with training and test data for both ordinary linear regression and regression through the origin as well as with linear and nonlinear regression models. We analyze the widely adopted model fit criteria suggested by Golbraikh and Tropsha ( J. Mol. Graphics Modell. 2002 , 20 , 269 - 276 ) in a strict statistical manner. Shortcomings in these criteria are identified, and a clearer and simpler alternative method to characterize model predictivity is provided. The intent is not to repeat the well-documented arguments for model validation using test data but rather to guide the application of R(2) as a model fit statistic. Examples are used to illustrate both correct and incorrect uses of R(2). Reporting the root-mean-square error or equivalent measures of dispersion, which are typically of more practical importance than R(2), is also encouraged, and important challenges in addressing the needs of different categories of users such as computational chemists, experimental scientists, and regulatory decision support specialists are outlined.
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Affiliation(s)
- D L J Alexander
- †CSIRO Digital Productivity Flagship, Private Bag 10, Clayton South, VIC 3169, Australia
| | - A Tropsha
- ‡UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - David A Winkler
- §CSIRO Manufacturing Flagship, Clayton, VIC 3168, Australia.,∥Monash Institute of Pharmaceutical Sciences, Parkville, VIC 3052, Australia.,⊥Latrobe Institute for Molecular Science, Bundoora, VIC 3046, Australia.,#School of Chemical and Physical Sciences, Flinders University, Bedford Park, SA 5042, Australia
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23
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Sparse feature selection methods identify unexpected global cellular response to strontium-containing materials. Proc Natl Acad Sci U S A 2015; 112:4280-5. [PMID: 25831522 DOI: 10.1073/pnas.1419799112] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Despite the increasing sophistication of biomaterials design and functional characterization studies, little is known regarding cells' global response to biomaterials. Here, we combined nontargeted holistic biological and physical science techniques to evaluate how simple strontium ion incorporation within the well-described biomaterial 45S5 bioactive glass (BG) influences the global response of human mesenchymal stem cells. Our objective analyses of whole gene-expression profiles, confirmed by standard molecular biology techniques, revealed that strontium-substituted BG up-regulated the isoprenoid pathway, suggesting an influence on both sterol metabolite synthesis and protein prenylation processes. This up-regulation was accompanied by increases in cellular and membrane cholesterol and lipid raft contents as determined by Raman spectroscopy mapping and total internal reflection fluorescence microscopy analyses and by an increase in cellular content of phosphorylated myosin II light chain. Our unexpected findings of this strong metabolic pathway regulation as a response to biomaterial composition highlight the benefits of discovery-driven nonreductionist approaches to gain a deeper understanding of global cell-material interactions and suggest alternative research routes for evaluating biomaterials to improve their design.
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24
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Huh YH, Noh M, Burden FR, Chen JC, Winkler DA, Sherley JL. Sparse feature selection identifies H2A.Z as a novel, pattern-specific biomarker for asymmetrically self-renewing distributed stem cells. Stem Cell Res 2015; 14:144-54. [PMID: 25636161 DOI: 10.1016/j.scr.2014.12.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2014] [Revised: 12/19/2014] [Accepted: 12/26/2014] [Indexed: 01/17/2023] Open
Abstract
There is a long-standing unmet clinical need for biomarkers with high specificity for distributed stem cells (DSCs) in tissues, or for use in diagnostic and therapeutic cell preparations (e.g., bone marrow). Although DSCs are essential for tissue maintenance and repair, accurate determination of their numbers for medical applications has been problematic. Previous searches for biomarkers expressed specifically in DSCs were hampered by difficulty obtaining pure DSCs and by the challenges in mining complex molecular expression data. To identify such useful and specific DSC biomarkers, we combined a novel sparse feature selection method with combinatorial molecular expression data focused on asymmetric self-renewal, a conspicuous property of DSCs. The analysis identified reduced expression of the histone H2A variant H2A.Z as a superior molecular discriminator for DSC asymmetric self-renewal. Subsequent molecular expression studies showed H2A.Z to be a novel "pattern-specific biomarker" for asymmetrically self-renewing cells, with sufficient specificity to count asymmetrically self-renewing DSCs in vitro and potentially in situ.
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Affiliation(s)
- Yang Hoon Huh
- Division of Electron Microscopic Research, Korea Basic Science Institute, 169-148 Gwahak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea
| | - Minsoo Noh
- College of Pharmacy, Seoul National University, Seoul, Republic of Korea
| | | | - Jennifer C Chen
- The Senator Paul D. Wellstone Muscular Dystrophy Cooperative Research Center, University of Massachusetts Medical School, Worcester, MA, USA
| | - David A Winkler
- CSIRO Manufacturing Flagship, Clayton, Australia; Monash Institute of Pharmaceutical Sciences, Parkville, Australia; La Trobe Institute for Molecular Science, Bundoora, Australia.
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25
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Abstract
The emphasis of this review is particularly on multivariate statistical methods currently used in quantitative structure–activity relationship (QSAR) studies.
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Affiliation(s)
- Somayeh Pirhadi
- Drug Design in Silico Lab
- Chemistry Faculty
- K. N. Toosi University of Technology
- Tehran
- Iran
| | | | - Jahan B. Ghasemi
- Drug Design in Silico Lab
- Chemistry Faculty
- K. N. Toosi University of Technology
- Tehran
- Iran
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26
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Thornton AW, Winkler DA, Liu MS, Haranczyk M, Kennedy DF. Towards computational design of zeolite catalysts for CO2 reduction. RSC Adv 2015. [DOI: 10.1039/c5ra06214d] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Computational search of structure database for CO2 reduction catalysts using molecular simulation and machine learning.
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Affiliation(s)
| | - D. A. Winkler
- CSIRO Manufacturing Flagship
- Australia
- Monash Institute of Pharmaceutical Sciences
- Parkville 3052
- Australia
| | - M. S. Liu
- CSIRO Digital Productivity and Services Flagship
- Australia
| | - M. Haranczyk
- Lawrence Berkeley National Laboratory
- Computational Research Division
- Berkeley
- USA
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27
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Le TC, Ballard M, Casey P, Liu MS, Winkler DA. Illuminating Flash Point: Comprehensive Prediction Models. Mol Inform 2014; 34:18-27. [DOI: 10.1002/minf.201400098] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Accepted: 09/27/2014] [Indexed: 11/08/2022]
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28
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Winkler DA, Burden FR, Yan B, Weissleder R, Tassa C, Shaw S, Epa VC. Modelling and predicting the biological effects of nanomaterials. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2014; 25:161-172. [PMID: 24625316 DOI: 10.1080/1062936x.2013.874367] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2013] [Accepted: 12/09/2013] [Indexed: 06/03/2023]
Abstract
The commercial applications of nanoparticles are growing rapidly, but we know relatively little about how nanoparticles interact with biological systems. Their value--but also their risk--is related to their nanophase properties being markedly different to those of the same material in bulk. Experiments to determine how nanoparticles are taken up, distributed, modified, and elicit any adverse effects are essential. However, cost and time considerations mean that predictive models would also be extremely valuable, particularly assisting regulators to minimize health and environmental risks. We used novel sparse machine learning methods that employ Bayesian neural networks to model three nanoparticle data sets using both linear and nonlinear machine learning methods. The first data comprised iron oxide nanoparticles decorated with 108 different molecules tested against five cell lines, HUVEC, pancreatic cancer, and three macrophage or macrophage-like lines. The second data set comprised 52 nanoparticles with various core compositions, coatings, and surface attachments. The nanoparticles were characterized using four descriptors (size, relaxivities, and zeta potential), and their biological effects on four cells lines assessed using four biological assays per cell line and four concentrations per assay. The third data set involved the biological responses to gold nanoparticles functionalized by 80 different small molecules. Nonspecific binding and binding to AChE were the biological endpoints modelled. The biological effects of nanoparticles were modelled using molecular descriptors for the molecules that decorated the nanoparticle surface. Models with good statistical quality were constructed for most biological endpoints. These proof-of-concept models show that modelling biological effects of nanomaterials is possible using modern modelling methods.
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Affiliation(s)
- D A Winkler
- a CSIRO Materials Science & Engineering Parkville Australia
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29
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Salahinejad M, Le TC, Winkler DA. Aqueous Solubility Prediction: Do Crystal Lattice Interactions Help? Mol Pharm 2013; 10:2757-66. [DOI: 10.1021/mp4001958] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Maryam Salahinejad
- Faculty of Chemistry, Tarbiat Moallem University, Tehran 15719-14911, Iran
- CSIRO Materials Science & Engineering, Clayton 3168, Australia
- Monash Institute of Pharmaceutical Sciences, Parkville 3052, Australia
| | - Tu C. Le
- CSIRO Materials Science & Engineering, Clayton 3168, Australia
| | - David A. Winkler
- CSIRO Materials Science & Engineering, Clayton 3168, Australia
- Monash Institute of Pharmaceutical Sciences, Parkville 3052, Australia
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30
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Salahinejad M, Le TC, Winkler DA. Capturing the crystal: prediction of enthalpy of sublimation, crystal lattice energy, and melting points of organic compounds. J Chem Inf Model 2013; 53:223-9. [PMID: 23215043 DOI: 10.1021/ci3005012] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Accurate computational prediction of melting points and aqueous solubilities of organic compounds would be very useful but is notoriously difficult. Predicting the lattice energies of compounds is key to understanding and predicting their melting behavior and ultimately their solubility behavior. We report robust, predictive, quantitative structure-property relationship (QSPR) models for enthalpies of sublimation, crystal lattice energies, and melting points for a very large and structurally diverse set of small organic compounds. Sparse Bayesian feature selection and machine learning methods were employed to select the most relevant molecular descriptors for the model and to generate parsimonious quantitative models. The final enthalpy of sublimation model is a four-parameter multilinear equation that has an r(2) value of 0.96 and an average absolute error of 7.9 ± 0.3 kJ.mol(-1). The melting point model can predict this property with a standard error of 45° ± 1 K and r(2) value of 0.79. Given the size and diversity of the training data, these conceptually transparent and accurate models can be used to predict sublimation enthalpy, lattice energy, and melting points of organic compounds in general.
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Affiliation(s)
- Maryam Salahinejad
- Faculty of Chemistry, Tarbiat Moallem University, Tehran 15719-14911, Iran
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31
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Epa VC, Burden FR, Tassa C, Weissleder R, Shaw S, Winkler DA. Modeling biological activities of nanoparticles. NANO LETTERS 2012; 12:5808-5812. [PMID: 23039907 DOI: 10.1021/nl303144k] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Products are increasingly incorporating nanomaterials, but we have a poor understanding of their adverse effects. To assess risk, regulatory authorities need more experimental testing of nanoparticles. Computational models play a complementary role in allowing rapid prediction of potential toxicities of new and modified nanomaterials. We generated quantitative, predictive models of cellular uptake and apoptosis induced by nanoparticles for several cell types. We illustrate the potential of computational methods to make a contribution to nanosafety.
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Affiliation(s)
- V Chandana Epa
- CSIRO Materials Science and Engineering, 343 Royal Parade, Parkville, Victoria 3052, Australia
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32
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Epa VC, Yang J, Mei Y, Hook AL, Langer R, Anderson DG, Davies MC, Alexander MR, Winkler DA. Modelling human embryoid body cell adhesion to a combinatorial library of polymer surfaces. JOURNAL OF MATERIALS CHEMISTRY 2012; 22:20902-20906. [PMID: 24092955 PMCID: PMC3787298 DOI: 10.1039/c2jm34782b] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Designing materials to control biology is an intense focus of biomaterials and regenerative medicine research. Discovering and designing materials with appropriate biological compatibility or active control of cells and tissues is being increasingly undertaken using high throughput synthesis and assessment methods. We report a relatively simple but powerful machine-learning method of generating models that link microscopic or molecular properties of polymers or other materials to their biological effects. We illustrate the potential of these methods by developing the first robust, predictive, quantitative, and purely computational models of adhesion of human embryonic stem cell embryoid bodies (hEB) to the surfaces of a 496-member polymer micro array library.
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Affiliation(s)
| | - Jing Yang
- Laboratory of Biophysics and Surface Analysis, University of Nottingham, Nottingham, UK
| | - Ying Mei
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Health Science Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrew L. Hook
- Laboratory of Biophysics and Surface Analysis, University of Nottingham, Nottingham, UK
| | - Robert Langer
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Health Science Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Daniel G. Anderson
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Health Science Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Martyn C. Davies
- Laboratory of Biophysics and Surface Analysis, University of Nottingham, Nottingham, UK
| | - Morgan R. Alexander
- Laboratory of Biophysics and Surface Analysis, University of Nottingham, Nottingham, UK
| | - David A. Winkler
- CSIRO Materials Science & Engineering, Clayton, Australia
- Monash Institute of Pharmaceutical Sciences, 381 Royal Parade, Parkville 3052, Australia
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Winkler DA, Burden FR. Robust, quantitative tools for modelling ex-vivo expansion of haematopoietic stem cells and progenitors. MOLECULAR BIOSYSTEMS 2012; 8:913-20. [PMID: 22282302 DOI: 10.1039/c2mb05439f] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Despite substantial research activity on bioreactor design and experiments, there are very few reports of modelling tools that can be used to generate predictive models describing how bioreactor parameters affect performance. New developments in mathematics, such as sparse Bayesian feature selection methods and nonlinear model-free modelling regression methods, offer considerable promise for modelling diverse types of data. The utility of these mathematical tools in stem cell biology are demonstrated by analysis of a large set of bioreactor data derived from the literature. In spite of the diversity of the data sources, and the inherent difficulty in representing bioreactor variables, these modelling methods were able to develop robust, quantitative, predictive models. These models relate bioreactor operational parameters to the degree of expansion of haematopoietic stem cells or their progenitors, and also identify the bioreactor variables that are most likely to affect performance across many experiments. These methods show substantial promise in assisting the design and optimisation of stem cell bioreactors.
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34
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Le T, Epa VC, Burden FR, Winkler DA. Quantitative structure-property relationship modeling of diverse materials properties. Chem Rev 2012; 112:2889-919. [PMID: 22251444 DOI: 10.1021/cr200066h] [Citation(s) in RCA: 237] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Tu Le
- CSIRO Materials Science and Engineering, Bag 10, Clayton South MDC 3169, Australia
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35
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Fong C, Le T, Drummond CJ. Lyotropic liquid crystal engineering–ordered nanostructured small molecule amphiphileself-assembly materials by design. Chem Soc Rev 2012; 41:1297-322. [DOI: 10.1039/c1cs15148g] [Citation(s) in RCA: 235] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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36
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Manallack D, Burden F, Winkler D. Modelling Inhalational Anaesthetics Using Bayesian Feature Selection and QSAR Modelling Methods. ChemMedChem 2010; 5:1318-23. [DOI: 10.1002/cmdc.201000056] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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