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CDD: Vault, CDD: Vision and CDD: Models software for biologists and chemists doing drug discovery


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A perspective on 12 yrs of CDD and developing products and collaborations.
A presentation given at the ACS meeting in San Diego - small business section

Published in: Science
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CDD: Vault, CDD: Vision and CDD: Models software for biologists and chemists doing drug discovery

  2. 2. •2016: CDD Vault = Activity & Registration + Inventory (New) + Visualization •2015: CDD Vault adds advanced calculations, modeling, and visualization •2014: CDD celebrates 10 Year Users Meeting together with Leading Scientists •2014: CDD Vault hosts >23 Million Compounds •2013: CDD Vault securely hosted >9 years – with 99.98% up-time •2013: CDD surpassed >100,000 customer logins •2012: CDD FISMA compliant and accredited •2012: NIH picked CDD Vault for Neuroscience Blueprint Network •2012: CDD securely hosted >160,000,000 datapoints •2011: CDD won Bio-IT World Editors’ Choice Best Practices Award •2011: MM4TB 5 year EU funded project with AstraZeneca, Sanofi-Aventis •2010: GSK, Novartis, Pfizer, and NIH Collaborations announced •2009: CDD Vault surpasses >1 Million Compounds •2008: Gates Foundation 2 year grant (extended to 8 years) •2005: Eli Lilly co-invested in a syndicate with Omidyar Network and Founders Fund •2004: CDD spun out of Lilly, UCSF signs up as first customer Overview: Balancing grants, contracts and sales Barry Bunin CEO and Board Director
  3. 3. Visualization Inventory ELN ELN: Electronic Laboratory Notebook (Coming Soon) Inventory: Reagents & Samples CDD Visualization: Calculations & Models CDD Vault: Public + Collaborative Value Efforts to Advance our Field for the Public Good Beyond What the Market Naturally Supports Activity & Registration Full Suite: Commercial Capabilities CDD Vault – Modern Drug Discovery Informatics
  4. 4. Ideas focused around neglected diseases • Work on neglected diseases is less sensitive • Provides more exposure for diseases • Less competitive than major diseases like cancer • Opportunities for grants and funding • Can build a following by publishing and networking • Leverages what we do best – we use the product in collaborations • Find new angles to combine collaboration with software • Create new knowledge, mine data, model data • Help create IP for collaborators • Combine in silico and in vitro • = ‘Scientific marketing’ adds credibility
  5. 5. NIH GRANTS • Non - dilutive • STTR • Find an academic collaborator lab with data and willing to make / test compounds • E.g. Tuberculosis, Chagas, Biocomputation grants • SBIR • Develop software with collaborators • E.g. Ontology development
  6. 6.
  7. 7. CDD- Over a decade of drug discovery collaborations SaaS Easy to use Used by Academia Industry, Biotech Private Selective collaboration 100’s of published datasets
  8. 8. Enterprise Capabilities Web Interface, Management Tools, Integration, Customizable Drug Discovery Data Mining Search, Visualization, Presentation Chemical Intelligence Chemical Drawing, Registration, Property Calculators, Structure Search, SAR Tools Collaborative Environment Controlled Access, Data Privacy, Security, Community Free Public Data Access Screening Data, Compound Data CDD Vault Features
  9. 9. • Online Zendesk • CDD Models • CDD Vision • Integration of CDD Public, ChemSpider, Zinc, and PubChem. Benefits of CDD Vault
  10. 10. CDD Vault® - Differentiators • Registration, SAR, Tracking, Visualization for budget sensitive labs • Easy for Your Whole Project Team to Embrace (Key Differentiation) • Secure & Proven (>250,000 logins) • 99.98% uptime for global access over 12 years • No hardware, no software, login online, fast adoptions
  11. 11. Budget Sensitive Startup or Academic Scientists Won’t lose data Get better results Easy to trial, set up, configure, be trained and GO! ex-Big Pharma Scientist Familiar with Registration/SAR Software Nimble Save $$$ with modern cloud solution Relax – data migration is a snap! Big Collaborations funded by Pharma, NIH, Foundations (PPP) Control exactly which data you share with others Relax – security is built in Foster interactions between biologists and chemists Passed Big Pharma & NIH FISMA audits (CDD does not own IP) CDD Vault® - “Value Proposition”
  12. 12. Login/month
  13. 13. TB Project overview Phase I STTR – Proof of concept of mimic strategy Phase II STTR – Expand mimic strategy and validation of phase I hits
  14. 14. Ekins et al., PLoS One. 2015, 10:e0141076 We have published 27 papers on tuberculosis with collaborators since 2010 ~50 papers and book chapters etc. published on CDD since 2009 Getting the word out
  15. 15. Copyright © 2013 All Rights Reserved Collaborative Drug Discovery MM4TB: 25 organizations New Old Neuroscience Kinetoplastid Drug Development Consortium
  16. 16. CDD Vault: Visualization, Calculations, & Models • Data Visualization Analytics • Scatterplots, Histograms, Interactive • Excel Type Calculations • On Experimental and/or Calculated Values • Automatically During Data Capture • Machine Learning Algorithms • Simplifies Model Building with Public or Private Data
  17. 17. CDD VISION Data taken from CDD Vault and utilized in CDD Vision Backend formed using immutable and Crossfilter.js, binding layer uses d3.js and jQuery, Rendering uses d3.js and Pixi.js
  18. 18. Launching CDD Vision CDD VISION
  19. 19. Filters CDD VISION
  20. 20. Compound details CDD VISION
  21. 21. Multiple plots, different sizes CDD VISION
  22. 22. MoDELS RESIDE IN PAPERS NOT ACCESSIBLE…THIS IS UNDESIRABLE How do we share them? How do we use Them?
  23. 23. Open Extended Connectivity Fingerprints ECFP_6 FCFP_6 • Collected, deduplicated, hashed • Sparse integers • Invented for Pipeline Pilot: public method, proprietary details • Often used with Bayesian models: many published papers • Built a new implementation: open source, Java, CDK – stable: fingerprints don't change with each new toolkit release – well defined: easy to document precise steps – easy to port: already migrated to iOS (Objective-C) for TB Mobile app • Provides core basis feature for CDD open source model service Clark et al., J Cheminform 6:38 2014
  24. 24. Predictions for the InhA target: (a) the ROC curve with ECFP_6 and FCFP_6 fingerprints; (b) modified Bayesian estimators for active and inactive compounds; (c) structures of selected binders. For each listed target with at least two binders, it is first assumed that all of the molecules in the collection that do not indicate this as one of their targets are inactive. In the app we used ECFP_6 fingerprints Building Bayesian models for each target in TB Mobile Clark et al., J Cheminform 6:38 2014
  25. 25. TB Mobile Ekins et al., J Cheminform 5:13, 2013 Clark et al., J Cheminform 6:38 2014 Predict targets Cluster molecules
  26. 26. Single point data > 300K molecules Uses Bayesian algorithm and FCFP_6 fingerprints Clark et al., J Cheminform 6:38 2014
  27. 27. Using AZ-ChEMBL data for CDD Models
  28. 28. • Human microsomal intrinsic clearance • Rat hepatocyte intrinsic clearance
  29. 29. Clark et al., JCIM 55: 1231-1245 (2015) Exporting models from CDD
  30. 30. Clark et al., JCIM 55: 1231-1245 (2015)9R44TR000942-02 Open Models in MMDS
  31. 31. 9R44TR000942-02 Composite Models – Binned Bayesians Clark et al., J Chem Inf Model. 2016, 56(2):275-85
  32. 32. Summary • Accessible software • Used widely in academia and industry • Leader in collaboration and security • Grown steadily through sales and grants • Dedicated sales in Europe, Asia • Coming soon: ELN • CDD provides integrated software for drug discovery
  33. 33. Anna Coulon-Spektor, Kellan Gregory, Charlie Weatherall, Krishna Dole, Andrew McNutt, Peter Nyberg, Tom Gilligan, Xiao Ba, Barbara Holtz, Sylvia Ernst, Frank Cole, Marc Navre, Alex M. Clark Joel Freundlich Peter Madrid Carolyn Talcott Malabika Sarker Jair Lage de Siqueira-Neto EU FP7 funding MM4TB NIH NIAID NIH NLM NIH NCATS Bill and Melinda Gates Foundation (Grant#49852) Acknowledgments