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2 October, 2017
Where will AI/Deep learning have
an impact in Life Science & Health
Pistoia Alliance Debates
27 September ...
This webinar is being recorded
©PistoiaAlliance
Poll Question 1: What role do you play in
your company
A. IT
B. data scientist/informatician
C. scientist...
©PistoiaAlliance
The Panel
4
Peter Henstock
Senior Manager,
Business Technology
group, Pfizer
Sean Ekins, CEO and
Founder ...
Poll Question 2: What is your familiarity
with AI/Deep learning?
A. I am using AI/Deep learning
B. I am experimenting with...
©PistoiaAlliance
David Pearah, CEO
HDF Group
Learning from other industry sectors
©PistoiaAlliance
What is it?
7
©PistoiaAlliance
Data Science and Artificial Intelligence
Hype?
Yes.
Real
Substance
and Impact?
Yes.
©PistoiaAlliance
Artificial Intelligence
(AI)
Field of computer science that allows
computers to “seem human” in some way
...
©PistoiaAlliance
• Storage and processing power as a cheap, on-demand utility:
• Graphics Processing Units (GPUs)
• Cloud ...
©PistoiaAlliance
©PistoiaAlliance
Artificial
Intelligence
Machine
Learning
Knowledge Representation and
Reasoning
Automated
Planning
Natura...
©PistoiaAlliance
Creating artificial intelligence solutions using supervised learning with a neural
network:
Dogs
2
Collec...
©PistoiaAlliance
What is it?
©PistoiaAlliance
What’s happening?
What is it?
©PistoiaAlliance
16
I/O library
optimized for
scale + speed
Self-
documenting
container
optimized for
scientific data +
me...
©PistoiaAlliance
v
v
v
What does the HDF Group do?
• HDF5 Community Edition + Enterprise Edition
• Connectors: ODBC + Clou...
©PistoiaAlliance
Questions? Comments?
Dave Pearah, CEO
David.Pearah@hdfgroup.org
www.hdfgroup.org
©PistoiaAlliance
Poll Question 3: What is your company’s
primary use for AI/Deep learning
A. Early Discovery/ Pre-clinical...
Sean Ekins, CEO, Collaborations
Pharmaceuticals, Inc.
Deep Learning in Pharmaceutical Research
©PistoiaAlliance
AI in Pharma is not new!
222 October, 2017
• Neural Networks
• Genetic algorithms
• SVM
• ‘Used’ for deca...
©PistoiaAlliance
Big data in 2002 vs 2017
232 October, 2017
Now -TB data ~19,000 cpds
©PistoiaAlliance
HTS
phenotypic
screen
Molecule
Screening database
Machine learning models
Vendor library
Top scoring mole...
©PistoiaAlliance
What is Deep Learning
252 October, 2017
©PistoiaAlliance
Deep Learning uses
262 October, 2017
• facial recognition
algorithms
– Facebook tagging
photos
• self-dri...
©PistoiaAlliance
Deep Learning in Pharmaceutical Research
272 October, 2017
• Bioinformatics
– Protein disorder
– Refine d...
©PistoiaAlliance
Gaps in Deep Learning for Pharmaceutical research
282 October, 2017
• TensorFlow
• Deeplearning4j
• Faceb...
©PistoiaAlliance
Recent Deep Learning papers
292 October, 2017
©PistoiaAlliance
Comparison of TB Machine-Learning Models (1µM)
302 October, 2017
Logistic Regression (LR)
Adaboosted Deci...
©PistoiaAlliance
Small scale Machine Learning comparison
312 October, 2017
• Comparing different
algorithms and using FCFP...
©PistoiaAlliance
Building Machine Learning models Assay Central
322 October, 2017
• Curate data and build
models
• Provide...
©PistoiaAlliance
Acknowledgments
332 October, 2017
• Kim Zorn Assay Central Guru
• Alex Clark Assay Central
• Thomas Lane ...
©PistoiaAlliance
Poll Question 4: What is the greatest
barrier to application of AI at your org
A. Technical & skills expe...
Peter Henstock - Business
Technology, Pfizer Inc.
Why is pharma lagging in the AI arena whereas
other industries are alrea...
©PistoiaAlliance
AI Works
©PistoiaAlliance
What does Waze do?
• Obtain public data: maps & locations
• Acquire & organize data for AI analyses
– Lev...
©PistoiaAlliance
Why Isn’t AI Working Yet for Pharma?
drugwaze
Rescreening
55% chance of new series
6 weeks $1.2MM
Optimiz...
©PistoiaAlliance
Keys to Success
• Obtain public data
• Acquire & organize data for AI analyses
• Utilize AI algorithms
• ...
©PistoiaAlliance
Need for a Chief Data Officer
Value
Proposition
https://www.123rf.com/photo_17347316_businessman-pulling-...
©PistoiaAlliance
Analytics First, Then AI
• Readiness for Analytics & AI
–Curated data sources
–Automated data management ...
©PistoiaAlliance
Keys to Success
• Obtain public data
• Acquire & organize data for AI analyses
• Utilize AI algorithms
• ...
©PistoiaAlliance
Harvard Business Review October 2012
©PistoiaAlliance
Modern Data Scientist
Math
Statistics
AI
Hacking
Database
Computing
Story Telling
Visualization
Domain
Kn...
©PistoiaAlliance
AI & Pharma Skillset Intersection
https://www.quora.com/What-is-the-difference-between-Data-Analytics-Dat...
©PistoiaAlliance
Does Pharma Have the Right Skills?
ManagementBusiness
Computer
Science
Biology
Chemistry
Medicine
Law
Sta...
©PistoiaAlliance
Does Pharma Have the Right Skills?
ManagementBusiness
Computer
Science
Biology
Chemistry
Medicine
Law
Sta...
©PistoiaAlliance
http://skrullemperor.deviantart.com/art/Deer-in-Headlights-120323487
©PistoiaAlliance
Threat of High Salaries for “Expertise”
Paul Minton:
Waiter ($20K)  data scientist ($100K)
“As Tech Boom...
©PistoiaAlliance
https://www.linkedin.com/pulse/body-language-does-work-business-owners-andrew-r-mackey
©PistoiaAlliance
AI is a harder concept to grasp
• Pharma & IT grasp replacement technologies
– Virtual machine replaces p...
©PistoiaAlliance
Volume of Tasks
• Easy to develop AI solutions around a single task
– Waze navigates
– Amazon sells
– Lin...
©PistoiaAlliance
Machine Learning Methods of AI
ML Mastery
©PistoiaAlliance
Big Data Landscape
http://mattturck.com/2016/02/01/big-data-landscape/
©PistoiaAlliance
http://arthurmcarthurs.blogspot.com/2011/06/deer-in-headlights.html
©PistoiaAlliance
AI Is Having a Stifled Impact in Pharma
• Bottom-Up Proof Cycle
– Scientific domain culture
– Continually...
©PistoiaAlliance
How to Succeed
1) Organize the data for AI
“Data, rather than software, is the barrier”
2) Invest in AI t...
©PistoiaAlliance
Audience Q&A
Please use the Question function in GoToWebinar
©PistoiaAlliance
Beyond BMI: Body Composition
Phenotyping in the UK Biobank
The next Pistoia Alliance Discussion Webinar:
...
info@pistoiaalliance.org @pistoiaalliance www.pistoiaalliance.org
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Pistoia Alliance debates AI in life science

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The webinar explores some of the current opportunities for AI within Life Science and look ahead to what we can expect to see over the coming years. These are the accompanying slides.

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Pistoia Alliance debates AI in life science

  1. 1. 2 October, 2017 Where will AI/Deep learning have an impact in Life Science & Health Pistoia Alliance Debates 27 September 2017 Nick Lynch
  2. 2. This webinar is being recorded
  3. 3. ©PistoiaAlliance Poll Question 1: What role do you play in your company A. IT B. data scientist/informatician C. scientist D. information professional E. other
  4. 4. ©PistoiaAlliance The Panel 4 Peter Henstock Senior Manager, Business Technology group, Pfizer Sean Ekins, CEO and Founder Collaborations Pharmaceuticals David Pearah , CEO, HDF Group
  5. 5. Poll Question 2: What is your familiarity with AI/Deep learning? A. I am using AI/Deep learning B. I am experimenting with AI/Deep learning C. I am aware of AI/Deep learning D. I know next to nothing about it
  6. 6. ©PistoiaAlliance David Pearah, CEO HDF Group Learning from other industry sectors
  7. 7. ©PistoiaAlliance What is it? 7
  8. 8. ©PistoiaAlliance Data Science and Artificial Intelligence Hype? Yes. Real Substance and Impact? Yes.
  9. 9. ©PistoiaAlliance Artificial Intelligence (AI) Field of computer science that allows computers to “seem human” in some way by replicating human cognitive functions (e.g., learning and problem solving) Machine Learning (ML) Subset of AI approaches that gives computers the ability to learn from and make predictions on data without being explicitly programmed (i.e. learn on their own from new data) Deep Learning (DL) Simulates many (deep) hierarchical layers of neurons in the human brain: by running large amounts of data through this simulation, it develops its own understanding of the concepts inherent in the data
  10. 10. ©PistoiaAlliance • Storage and processing power as a cheap, on-demand utility: • Graphics Processing Units (GPUs) • Cloud computing allows affordable GPUs at scale • Critical mass in open source software community • Powerful new applications for known AI techniques (e.g., deep learning) • Global, online AI community sharing advances daily • Open source software from the community and tech giants (e.g., Google TensorFlow) • Huge AI investments from tech titans who see AI as a strategic asset • Exponential growth in data to analyze using DL. In life science: • Electronic health records • Genomic data • Patient monitoring and treatment devices (e.g., EKG, Pulse, Oxygen, IV Pumps, etc..) • Consumer biomonitoring devices (e.g., FitBit, Apple Watch, smartphones) • Environmental data • Data registries • Medical literature and supporting primary data Deep Learning (DL): Why Now?
  11. 11. ©PistoiaAlliance
  12. 12. ©PistoiaAlliance Artificial Intelligence Machine Learning Knowledge Representation and Reasoning Automated Planning Natural Language Processing Multi-Agent Systems Robotic s Reinforcement Learning Supervised Learning Semi-supervised Learning Unsupervised Learning Markov Decision Processes (e.g. Policy iteration) Classification/Regression Clustering Summarization Anomaly Detection Distance-based (e.g.: LOF) Model-based (e.g.: MMPP) Graphical and Statistical (e.g.: Exponential Smoothing) Dimensionality Reduction (e.g. PCA, SVD) Association and Sequence models (e.g.: apriori algorithm) Density-based (e.g.: DBSCAN) Hierarchical (e.g.: Single-linkage) Centriod-based (e.g.: K-Means) Distribution-based (e.g.: Mixture of Gaussians) Instance-based (e.g.: KNN, CBR) Decision Tree (e.g.: Random Forest) Artificial Neural Networks (e.g. Perceptron) Bayesian Networks (e.g.: Naïve Bayes) Kernel-based (e.g. SVM)
  13. 13. ©PistoiaAlliance Creating artificial intelligence solutions using supervised learning with a neural network: Dogs 2 Collecting and annotating data sets 3 Training via Computation 4 Independent Validation of the Algorithm 5 Deployment and Monitoring 1 Define a Narrative AI Use Case Cats
  14. 14. ©PistoiaAlliance What is it?
  15. 15. ©PistoiaAlliance What’s happening? What is it?
  16. 16. ©PistoiaAlliance 16 I/O library optimized for scale + speed Self- documenting container optimized for scientific data + metadata Users who need both features HDF5 + Deep Learning 1 6 HDF5 already integrated into every major DL Framework (TensorFlow, Caffe, Keras, etc.)
  17. 17. ©PistoiaAlliance v v v What does the HDF Group do? • HDF5 Community Edition + Enterprise Edition • Connectors: ODBC + Cloud (Beta) • Add-Ons: compression + encryption • HDF Support Packages (Basic + Pro + Premier) • Support for h5py + PyTables + pandas (NEW) • Training • HDF: new functionality + performance tuning for specific use cases • HPC software engineering with scientific expertise • Deep Learning expertise Products Support Consulting 1 7
  18. 18. ©PistoiaAlliance Questions? Comments? Dave Pearah, CEO David.Pearah@hdfgroup.org www.hdfgroup.org
  19. 19. ©PistoiaAlliance Poll Question 3: What is your company’s primary use for AI/Deep learning A. Early Discovery/ Pre-clinical B. Development & Clinical C. Imaging Analysis D. Other E. Don’t use AI
  20. 20. Sean Ekins, CEO, Collaborations Pharmaceuticals, Inc. Deep Learning in Pharmaceutical Research
  21. 21. ©PistoiaAlliance AI in Pharma is not new! 222 October, 2017 • Neural Networks • Genetic algorithms • SVM • ‘Used’ for decades • Why it never took off: – Compute power – Lack of training data – Limited support – Most Scientists did not believe them…needed a paradigm shift – Pharma mergers culled 10,000’s scientists DEEP LEARNING
  22. 22. ©PistoiaAlliance Big data in 2002 vs 2017 232 October, 2017 Now -TB data ~19,000 cpds
  23. 23. ©PistoiaAlliance HTS phenotypic screen Molecule Screening database Machine learning models Vendor library Top scoring molecules assayed in vitro Bernoulli Naive Bayes, Logistic linear regression, AdaBoost Decision Trees, Random Forest, Support Vector Machines (SVM), Deep Neural networks (DNN) Speeding drug discovery with AI ▶ Molecular pattern recognition of biological data ▶ Descriptors identify these patterns ▶ Define active and inactive features ▶ Used to generate predictions for drug activity at a certain target (organism, protein of interest)
  24. 24. ©PistoiaAlliance What is Deep Learning 252 October, 2017
  25. 25. ©PistoiaAlliance Deep Learning uses 262 October, 2017 • facial recognition algorithms – Facebook tagging photos • self-driving cars • robot assistants http://tinyurl.com/hak4lcv http://tinyurl.com/y8vjv8lp
  26. 26. ©PistoiaAlliance Deep Learning in Pharmaceutical Research 272 October, 2017 • Bioinformatics – Protein disorder – Refine docking complexes – Model CLIP-seq data – High content image analysis data – Biomarkers – Protein contacts – Cancer diagnosis • Pharmaceutical – Solubility – Gene expression data – Formulation – QSAR – Merck DL out performed random forests in 11 /15 and 13/15 datasets – Tox21 Where else could we apply DL in drug discovery? Pharmacoeconomics?
  27. 27. ©PistoiaAlliance Gaps in Deep Learning for Pharmaceutical research 282 October, 2017 • TensorFlow • Deeplearning4j • Facebook (Torch) • Microsoft (CNTK) • Which metrics to use? • Which descriptors? • Are the DL over training? • Lack of prospective testing.
  28. 28. ©PistoiaAlliance Recent Deep Learning papers 292 October, 2017
  29. 29. ©PistoiaAlliance Comparison of TB Machine-Learning Models (1µM) 302 October, 2017 Logistic Regression (LR) Adaboosted Decision Trees (ADA) Random Forest (RF) Naive-bayes (BNB) Support Vector Machines (SVM) Deep Neural Networks (DNN) ▶ TB data from literature ▶ ~19,000 molecules ▶ ECFP6 descriptors ▶ Used previously with Bayesian methods ▶ Multiple metrics ▶ 5 fold cross val ▶ Classic ML -Open source Scikit-learn http://scikit- learn.org/stable/ ▶ Deep Neural Networks (DNN) using Keras https://keras.io/, and Tensorflow www.tensorflow.org,
  30. 30. ©PistoiaAlliance Small scale Machine Learning comparison 312 October, 2017 • Comparing different algorithms and using FCFP6 fingerprints • Deep learning seems to improve model ROC statistics in 4/6 cases. • Data sets range from 100s – >300K • All classification models • Next steps evaluate all the datasets in ChEMBL, PubChem, ToxCast etc 31 Korotcov et al., Submitted
  31. 31. ©PistoiaAlliance Building Machine Learning models Assay Central 322 October, 2017 • Curate data and build models • Provide models and collections as jar files Add DL algorithm to Assay Central
  32. 32. ©PistoiaAlliance Acknowledgments 332 October, 2017 • Kim Zorn Assay Central Guru • Alex Clark Assay Central • Thomas Lane PhD intern UNC • Dan Russo PhD intern Rutgers • Jacob Gerlach High School Intern • Valery Tkachenko Deep Learning Consultant • Alex Korotcov Deep Learning Consultant • Thanks also to: Renee Arnold, Peter Swaan Funding from NIGMS NIH R43GM122196
  33. 33. ©PistoiaAlliance Poll Question 4: What is the greatest barrier to application of AI at your org A. Technical & skills expertise B. Access to data C. Data quality D. Management support/understanding E. Other
  34. 34. Peter Henstock - Business Technology, Pfizer Inc. Why is pharma lagging in the AI arena whereas other industries are already transformed
  35. 35. ©PistoiaAlliance AI Works
  36. 36. ©PistoiaAlliance What does Waze do? • Obtain public data: maps & locations • Acquire & organize data for AI analyses – Leverage historical traffic data – Integrate new traffic information • Utilize AI algorithms – Fastest route predictions • Present timely information through UI
  37. 37. ©PistoiaAlliance Why Isn’t AI Working Yet for Pharma? drugwaze Rescreening 55% chance of new series 6 weeks $1.2MM Optimization 14% issue series 1 Solubility cause 23% issue series 2 Safety cause 5% issue series 3 8.2 months to Phase 1 Predicted FDA approval chance: 37% Recommended actions: 1) Resolve the
  38. 38. ©PistoiaAlliance Keys to Success • Obtain public data • Acquire & organize data for AI analyses • Utilize AI algorithms • Present timely information through UI
  39. 39. ©PistoiaAlliance Need for a Chief Data Officer Value Proposition https://www.123rf.com/photo_17347316_businessman-pulling-rope-on-white-background.html $ $ $ Acquire and organize data for AI
  40. 40. ©PistoiaAlliance Analytics First, Then AI • Readiness for Analytics & AI –Curated data sources –Automated data management processes –Structured data analytics • “If your company isn’t good at analytics, it’s not ready for AI” – Harvard Business Review June 7, 2017
  41. 41. ©PistoiaAlliance Keys to Success • Obtain public data • Acquire & organize data for AI analyses • Utilize AI algorithms • Present timely information through UI
  42. 42. ©PistoiaAlliance Harvard Business Review October 2012
  43. 43. ©PistoiaAlliance Modern Data Scientist Math Statistics AI Hacking Database Computing Story Telling Visualization Domain Knowledge Analysis
  44. 44. ©PistoiaAlliance AI & Pharma Skillset Intersection https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science- Machine-Learning-and-Big-Data-1 Software Engineering Bioinformatics Architecture & Systems Clinical Statistics HPC/Linux Farm AI & Machine Learning Scientists
  45. 45. ©PistoiaAlliance Does Pharma Have the Right Skills? ManagementBusiness Computer Science Biology Chemistry Medicine Law Statistics Physics BS MS/MBA PhD/MD/JD
  46. 46. ©PistoiaAlliance Does Pharma Have the Right Skills? ManagementBusiness Computer Science Biology Chemistry Medicine Law Statistics Physics BS MS/MBA PhD/MD/JD Need depth & breadth across AI areas
  47. 47. ©PistoiaAlliance http://skrullemperor.deviantart.com/art/Deer-in-Headlights-120323487
  48. 48. ©PistoiaAlliance Threat of High Salaries for “Expertise” Paul Minton: Waiter ($20K)  data scientist ($100K) “As Tech Booms, Workers Turn to Coding for Career Change”. July 28, 2015 New York Times
  49. 49. ©PistoiaAlliance https://www.linkedin.com/pulse/body-language-does-work-business-owners-andrew-r-mackey
  50. 50. ©PistoiaAlliance AI is a harder concept to grasp • Pharma & IT grasp replacement technologies – Virtual machine replaces physical machine – Cloud storage replaces local disks – Agile replaces waterfall method – High Throughput Screening replaces “screening” – High Content Screening replaces imaging • AI and Machine Learning – Provide a data-driven complement to many disciplines – Apply from early discovery to marketing – Span journals, data, omics, images, decision-making
  51. 51. ©PistoiaAlliance Volume of Tasks • Easy to develop AI solutions around a single task – Waze navigates – Amazon sells – LinkedIn links – Facebook advertises • Pharma/Biotech tasks are varied – Text mining for targets – Screening and imaging technologies – Using ‘Omics – Drug optimization – Clinical trials – Patient reports and communication – Predictions on activity, safety, trial enrollment, outcomes…
  52. 52. ©PistoiaAlliance Machine Learning Methods of AI ML Mastery
  53. 53. ©PistoiaAlliance Big Data Landscape http://mattturck.com/2016/02/01/big-data-landscape/
  54. 54. ©PistoiaAlliance http://arthurmcarthurs.blogspot.com/2011/06/deer-in-headlights.html
  55. 55. ©PistoiaAlliance AI Is Having a Stifled Impact in Pharma • Bottom-Up Proof Cycle – Scientific domain culture – Continually need to prove AI’s value to every group – Leveraging 1 data set at a time for 1 AI problem – Gains are localized to small groups • Minimal investment – Sitting on more data than most industries – Failing to analyze and leverage this data – Hiring less AI expertise than small tech startups – Relying on expensive external collaborations
  56. 56. ©PistoiaAlliance How to Succeed 1) Organize the data for AI “Data, rather than software, is the barrier” 2) Invest in AI talent “Simply downloading and “applying” open-source software to your data won’t work. AI needs to be customized to your business context and data. This is why there is currently a war for the scarce AI talent that can do this work.” 3) Develop an AI strategy “After understanding what AI can and can’t do, the next step for executives is incorporating it into their strategies. [This] is the beginning, not the end….” What Artificial Intelligence Can and Can’t do Now” Harvard Business Review Nov 9, 2016 Andrew Ng
  57. 57. ©PistoiaAlliance Audience Q&A Please use the Question function in GoToWebinar
  58. 58. ©PistoiaAlliance Beyond BMI: Body Composition Phenotyping in the UK Biobank The next Pistoia Alliance Discussion Webinar: Date: October 25, 2017 check http://www.pistoiaalliance.org/events/ for the latest information
  59. 59. info@pistoiaalliance.org @pistoiaalliance www.pistoiaalliance.org

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