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Enalos Nanoinformatics tools for the prediction of nanomaterials properties NANOGENTOOLS EU Autumn School M. Eng. Dimitra Danai Varsou Hotel Rice Palacio de los Blasones 1 Nanogentools confidential Who we are Nanoinformatics

  1. Enalos Nanoinformatics tools for the prediction of nanomaterials properties NANOGENTOOLS EU Autumn School M. Eng. Dimitra Danai Varsou Hotel Rice Palacio de los Blasones 1 Nanogentools confidential

  2. • Who we are • Nanoinformatics • Enalos+ software • Enalos Cloud Platform for Nanoinformatics 2 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  3. Work orientation Nanoinformatics Molecular modeling Data mining Ligand- & structure- In silico based virtual screening services Advanced modeling & simulation techniques Chemoinformatics workflows Systems biology Image analysis 3 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  4. Investing in people • According to Scopus NovaMechanics is the top Research SME in Cyprus • All personnel is highly skilled with strong scientific background in the field of chemoinformatics, bioinformatics and medicinal chemistry • Senior scientists have a strong academic record • Managerial experience in large scale scientific projects, managed successfully EU & National funded projects 4 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  5. NovaMechanics in NANOGENTOOLS [1] • In silico exploration of tested NMs • Development of QNAR models • Building risk assessment platform • Prioritize NMs for biological evaluation • Design of novel NMs with desired properties 5 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  6. NovaMechanics in NANOGENTOOLS [2] • Meta-models • Meta-models development for the time demanding calculations of NMs quantum- mechanical (QM) and molecular dynamics (MD) simulations • Building a predictive modeling procedure to correlate all described input and output variables • The input/design variables will be selected among the QM and MD data and will be varied in a stepwise fashion to produce a large number of models • The outcome will be validated → robust and fast predictive models with well - defined domain of applicability for the prediction of QM and MD properties 6 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  7. NovaMechanics in NANOGENTOOLS [3] • E-infrastructure/NovaMechanics server (key- feature: NVIDIA Tesla™ P100 12GB Passive GPU, 512GB RAM) • Speeding up the MD calculations procedures • Hosting GPU-accelerated databases • Streaming, processing, querying and analyzing datasets in seconds to milliseconds, instead of hours to minutes • GPU-parallelized processing architecture allows linear scalability and reduces analytical processing times for multi-billion row data sets • Application of time demanding state of the art modelling methodologies such as deep learning, in real time 7 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  8. Nanoinformatics What is all about? Development of a QNAR model Risk assessment platform 8 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  9. What is all about? [1] Main goal: Toxicity assessment of ENMs Classical approach: in vivo and in vitro testing Toxicity endpoints: cell viability, cell membrane damage, mitochondrial damage, DNA damage, genotoxicity etc. Raman spectroscopy, TEM, FTIR, DLS, Engineered NPs mass spectrometry, HTS, etc. 9 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential All photos used in this presentation subject to the CC license

  10. What is all about? [2] Main drawbacks Time-consuming experiments Expensive experiments Use of laboratory animals ENMs currently emerging in commercial applications 10 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  11. What is all about? [3] • In silico testing: • Computational approach of the toxicity assessment of ENMs • High accuracy predictions of the potential toxic effects of ENMs • Development of user-friendly tools (web-services) for nanotoxicity assessment • Prioritization of ENMs for biological evaluation • Reduction of the time and the cost of experimental procedures 11 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  12. Development of a QNAR model [1] Quantitative Nanostructure-Activity Relationship (QNAR) modelling Model Prediction Properties of ENMs Response variable (Y) Predictor variables (X) “Function” that relates Toxicity profile Physicochemical the ENMs properties to Endpoint value properties their activity profile Structure Molecular descriptors 12 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  13. Development of a QNAR model [2] Main steps: 1. Data collection and integration 2. Calculation of descriptors 3. Preprocessing and variable selection 4. Development of the in silico model for the prediction of the ENMs’ biological effects 5. Model validation (internal, external) for testing predictive power of the model 6. Domain of applicability definition 13 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  14. Image Analysis • Microscopy images • Image processing • Useful descriptors • Centroid X • Centroid Y • Circularity • Size • Eccentricity • Perimeter • Convexity etc. 14 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  15. Risk assessment platform [1] Models open to the community: Development of a risk assessment web tools Risk assessment platform QNAR models User-friendly Physicochemical descriptors Ready-to-use Image descriptors No need of previous programming knowledge Ideal for experimentalists 15 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  16. Risk assessment platform [2] Web app: Outputs: Inputs: select an prediction- structure- available tolerance properties model limits 16 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  17. Enalos Nano/Cheminformatics Tools Enalos+ nodes (through KNIME Analytics Platform) Enalos Suite Enalos Cloud Platform 17 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  18. KNIME Analytics Platform [1] • A user-friendly and open-source platform that combines various software tools for data integration, processing, analysis, and exploitation • Creation of a network of nodes • interact easily with the workflow • experiment with different methodologies in short- time • compare the results • have the complete supervision of the analysis process 18 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  19. KNIME Analytics Platform [2] 19 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  20. Enalos+ KNIME nodes [1] • NovaMechanics Ltd made some very useful operations available as extensions for KNIME platform • Enalos + nodes are fully compatible with other KNIME nodes • Enalos+ nodes can be combined with custom made workflows and real time molecular descriptor calculations combined with state of the art modeling techniques (WEKA, R etc.) http://enalosplus.novamechanics.com/ 20 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  21. Enalos+ KNIME nodes [2] • Data handling and preprocessing • Calculation of molecular descriptors • Modelling • Testing the accuracy of the predictions • Direct access to CIR (Chemical Identifier Resolver) through KNIME • Direct access to the PubChem and UniChem databases and information acquisition for thousands of compounds 21 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  22. Molecular descriptors • With molecular descriptors the chemical information contained in the molecule can be treated mathematically and can be used for modelling • The structural characteristics can be directly linked with the biological or physicochemical properties of chemical compounds • Mold2 (National Center for Toxicological Research of FDA), ideal for the calculation of molecular descriptors (777), encoding two-dimensional chemical structure information 22 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

  23. Modelling nodes [1] • Pre-processing nodes • Perform some simple but crucial procedures for handling the data and prepare them for modelling • Time-consuming procedures can be automated, eliminating significantly the effort and the time dedicated to them • Create New Molecules • Int 2 Double • Remove Column • Remove Duplicates 23 NANOGENTOOLS EU Autumn School, 02/10/2017, Hotel Rice Palacio de los Blasones Nanogentools confidential

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