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Open Source Pharma: Crowd computing: A new approach to predictive modeling

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Presentation about "Predictive in silico models," given by Joerg Bentzien at the Open Source Pharma Conference. The event took place at Rockefeller Foundation Bellagio Center in July 2014.

Joerg Bentzien Bio:

Conference Agenda (see Day 1, Session 2):

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Open Source Pharma: Crowd computing: A new approach to predictive modeling

  1. 1. Predictive in silico models Crowd computing: A new approach to predictive modeling Jörg Bentzien Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014
  2. 2. Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 2 Introduction Ph.D. In Chemistry, Univ. Münster, Germany, Prof Martin Klessinger Photochemical [2+2] Cycloaddition reactions Post-Doctoral Studies at USC, Los Angeles, CA, Nobel Laureate Prof Arieh Warshel Enzymatic Reactions Xencor, Monrovia, CA Protein Design Boehringer Ingelheim Pharmaceuticals, Ridgefield,CT ComputationalChemist, Small Molecule Drug Design ADMETModeling Crowdsourcing with Kaggle, 2012 Bentzien et al. Drug DiscoveryToday (2013), 18, 472 - 478. Bentzien et al. J PhysChem B (1998), 102, 2293 - 2301 Hayes et al. J PNAS (2002), 99, 15926 - 15931 Bentzien, Klessinger J OrgChem (1994), 59, 4887 - 4894
  3. 3. 3 Why are we building predictive in silico Models? We cannot make and test every compound. • Reduce drug failure rates, de-risk compounds • Select and prioritize compounds before synthesis Predictive in silico models could help to achieve this task. Lack of efficacy and safety/toxicity are the main reasons why drugs fail in the clinic. Toxicity is the main reason for attrition in early drug development. Reasons for attrition in clinical trials: Arrowsmith, Nat. Rev. Drug Disc. 2013, 12, 569 Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 Efficacy Safety
  4. 4. 4 Principal of in silico modeling N S O N H N S O O OH Prediction of (ADMET) Observable In vivo effect In vitro effect Code in Machine readable form Calculate Descriptors: Physical Chemical Descriptors Molecular Properties Fingerprints Substructure Counts etc. Generate predictive Model: Random Forest SVM PLS CoMFA etc. Select: Training Set Test Set Validation Set Pi = f(x1,x2,x3,x4, ….)x1,x2,x3,x4, …. N N H O N N O N + O O OH S SH H Br 0 5 10 0 5 10 pIC50precited IC50 exp Positive Predicted Negative Predicted Positive Exper.i True Positive (TP) False Negative (FN) Negative Experi. False Positive (FP) True Negative (TN) Regression: Classification: Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 Find a relation between the chemical structure and the observable, Pi (e.g. genotoxicity), by first calculating descriptors, xi (e.g. physchem properties), and then using a mathematical algorithm that calculates the observable Pi for each structure.
  5. 5. BI 621,079 hCB2 cAMP EC50 = 1.6 nM O N H S Cl O O N N Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 5 Crowd-Sourcing applied to in silico Modeling: The general idea Traditional Model Building The KAGGLE approach Ames Positive predicted AM1 Ames Negative predicted AM1 Ames Positive experimental 167 (183) 16 Ames Negative experimental 21 53 (74) Potent Ames negative compound O F F F N N H Single Expert Modeller 3. Generate a Model 4. Find a solution 3. Generate a Model Taking advantage of the “crowd” one vs. many Potent Ames positive compound O N H 1. Define the problem 2. Prepare the Data
  6. 6. Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 6 The Kaggle Challenge:The Data Set Predicting a Biological Response 3/16/2012 – 6/15/2012 Data Set of 6512 compounds from Literature CADD-BI performed: Data Set Clean-Up (6252: 3401p/2851n) Random split into: Training Set (3751: 2034p/1717n) PublicTest Set (625: 329p/296n) Private Validation Set (1876: 1038p/838n) Pre-calculated Descriptors (1776) Participants had no knowledge of • the modeled endpoint • the descriptor types • the chemical structures BI offered $20,000 for the best three models Participants could use any technology they wanted BI will get the models Objectives: • Response to competition • Quality of the algorithms/models • Model transfer Task: Generate an Ames Classification model 1 = Ames positive 0 = Ames negative This Challenge does NOT test all aspects of predictive in silico modeling Important aspects, e.g. data set selection, descriptor selection/design, are missing Study is a machine learning exercise, a proof of concept Advantage: We know exactly what to expect, comparative benchmarks available
  7. 7. Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 7 The Kaggle Challenge:The Competition Predicting a Biological Response 3/16/2012 – 6/15/2012 Overwhelming response to competition! Best models perform better than standard benchmarks: Rank Log Loss Best Model 1 0.37356 Random Forest 352 0.41540 SVM 541 0.49503 Each Class Predicted with Probability 0.5 599 0.69250 On average 88 entries per day! Optimal model generated after ~20 Days 796 players (487 first time participants) 703 teams 8841 models submitted
  8. 8. The Kaggle Challenge: Measuring the Performance Different performance metrics for in silico classification models LogLoss Sensitivity Specificity CCR PPV NPV MCC Random Forest 352 0.41540 0.855 0.802 0.829 0.843 0.818 0.66 SVM 540 0.49503 0.792 0.743 0.768 0.793 0.743 0.55 Rank 1 0.37356 0.841 0.820 0.830 0.853 0.806 0.66 Rank 2 0.37363 0.855 0.803 0.829 0.843 0.818 0.66 Rank 3 0.37407 0.860 0.807 0.833 0.846 0.823 0.67 Rank 10 0.37641 0.860 0.810 0.835 0.849 0.824 0.67 Rank 50 0.38229 0.856 0.805 0.831 0.845 0.819 0.66 Rank 100 0.38958 0.869 0.794 0.831 0.839 0.830 0.67 Differencesin top models in logloss metric are small. Different statistical measures lead to different rankings. RF benchmarkhas high correct classification rate (CCR) and high MatthewCorrelationCoefficient. Benchmarks Positive Predicted Negative Predicted Positive Experi. True Positive (TP) False Negative (FN) Negative Experi. False Positive (FP) True Negative (TN) Positive Predicted Negative Predicted Rank 1 Rank 2 Rank 3 873 888 893 165 150 145 Rank 1 Rank 2 Rank 3 151 165 162 687 673 676 Positive Predicted Negative Predicted RF SVM 888 822 150 216 RF SVM 166 215 672 673 Positive Predicted Negative Predicted Rank 17 D27 896 781 142 257 Rank 17 D27 169 215 669 623 Other ModelsWinningTeams Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 8
  9. 9. Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 9 The Kaggle Challenge: Lessons learned Technology aspects: • 1st ranked team: R-software, blending of several different RandomForest models, with special feature selection and weighting techniques. Final models were merged using other machine learning techniques. • 2nd ranked team: R-software, RandomForest, derived new response variable pending on value and observed activity.This may lead to better separation between actives and inactives. • 3rd ranked team: R-software, RandomForest with special techniques to deal with imbalanced data sets. • The challenge was a success • There was a great response • Predictive in silico models were generated within a three months time frame • Models were at least as good as the literature • Social aspects of crowd-sourcing were observed
  10. 10. Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 10 The Kaggle Challenge: Lessons learned (continued) Performance aspects: • Model performance on par with best literature models, reached maximum performance for data set • Top ranking models are not significantly different from Random Forest benchmark • Quick turn-around (3 months), code made available • Model performance plateaued after 20 days A standard RandomForest model is a good starting point. In-house technology performs as well as more complex approaches. Social aspects of competition: • Very strong response: 703 teams, 8841 models submitted • People from all over the world participated: 1st place team from US (Harvard,Travelers insurance) 2nd place team from Russia graduate student from Moscow 3rd place from China graduate student from Beijing • Winning teams had no CompChem/Chemistry background • Formation of teams occurred during competition Bentzien at al. “Crowd computing: Using competitive dynamics to develop and refine highly predictive models”, Drug DiscoveryToday (2013), 18, 472 - 478.
  11. 11. Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 11 The Kaggle Challenge: Lessons learned (continued) Important aspects for successful crowdsourcing: Design the Crowdsourcing Challenge: Very clear defined task/objective Predefined precise metric to measure entries Provide adequate incentive/prize money for participants Participants: Hosting the challenge either through third party or self Internal/Restricted/Open Challenge Promote the crowd sourcing challenge among key expert leaders The Challenge: Right barrier for participation Fast turn-around/feedback to participants Gamification can provide additional incentive to participants can lead to synergies amongst participants After the Challenge: Clear follow-up of what to do with the results Does the challenge benefit to your Network/Organization?
  12. 12. Crowd-Sourcing : Other examples Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 12 http://www.nytimes.com/2012/11/24/science /scientists-see-advancesin- deep-learning-a-part-of-artificial- intelligence.html?_r=0 Lakhani et al., Nat Biotech, 2013, 31, 108-111. www.innocentive.com www.the-dream-project.com Prill et al., ScienceSignaling, 2011, 4, 1-6 www.kaggle.com www.topcoder.com www.grants4targets.com
  13. 13. Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 13 Crowd-Sourcing: A new way for solving problems(?) Will crowd-sourcing solve all the problems? Likely not. Crowd sourcing offers opportunities but it is not without risks. For crowd sourcing to be successful/innovative the task needs to be structured right. Murcko & Walters, “Alpha Shock” J Comput Aided Mol Des 2012, 26, 97-102 Kittur et al. “The Future of Crowd Work” 16th ACM Conference on Computer Supported Cooperative Work (CSCW 2013) Will crowd-sourcing be the future way of drug discovery? Maybe, …. Drug Discovery will definitely be different from what it is now. Potential framework for future crowd work. Requires • Intelligent work decomposition • sophisticated workflow design • high level of collaborative work • quality assurance. Simple crowd work • tendency to be mechanical • not innovative • has exploitive tendency Example: Amazon MechanicalTurk
  14. 14. Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 14 Acknowledgements Business Partners and Collaborates ADMET-WG: Jan Kriegl, Bernd BeckStefan, Scheuerer, Michael Durawa, Pierre Bonneau, Sanjay Srivastava, Michel Garneau, Hassan Kadhim, Matthias Klemencic, Christian Klein, Robert Happel, Gerald Birringer, Dustin Smith, Scott Oloff, Zheng Yang Toxicology: Warren Ku Patricia Escobar Ray Kemper External Collaborators: Ernst-Walter Knapp Özgür Demir-Kamuk AlexTropsha Curt Breneman John Pu Andy Fant Zhuo Zhen Medicinal Chemistry: Robert Hughes In silico VPR-team All the MedChem users Research IS: Scott Oloff DavidThompson (PAC) Zheng Yang Scott Whalen Cathy Farrell MiguelTeodoro IS-InnovationTeam Alex Renner Structural Research: Sandy Farmer Neil Farrow Ingo Mügge All CADD colleagues SKD: Will Loging Kaggle: KaggleTeam Kaggle Challenge Participants