Automating Drug Design Using Robot Scientists

1. Automating Drug Design Using Robot Scientists Ross D. King, University of Manchester, ross.king@manchester.ac.uk

2. The Concept of a Robot Scientist Computer systems capable of originating their own experiments, physically executing them, interpreting the results, and then repeating the cycle. Background Knowledge Analysis Hypothesis Formation Results Interpretation Experiment selection Final Theory Robot

3. Motivation • Robot Scientists have the potential to increase the productivity of science. They can work cheaper, faster, more accurately, and longer than humans. They can also be easily multiplied. • Enabling the high-throughput testing of hypotheses. • Robot Scientists have the potential to improve the quality of science. • by enabling the description of experiments in greater detail and semantic clarity.

4. Robot Scientist Timeline • 1999-2004 Initial Robot Scientist Project • Limited Hardware • Collaboration with Douglas Kell (Aber Biology), Steve Oliver (Manchester), Stephen Muggleton (Imperial) King et al. (2004) Nature, 427, 247-252 • 2004-2011 Adam Project – Yeast Functional Genomics • Sophisticated Laboratory Automation • Collaboration with Steve Oliver (Cambridge). King et al. (2009) Science, 324, 85-89 • 20011-2014 Eve Project – Drug Design for Tropical Diseases

5. Adam

6. Adam

7. Adam • Functional genomics • In yeast (S. cerevisiae) ~15% of the 6,000 genes still have no known function. • First machine to autonomously discover scientific knowledge.

8. Eve

9. Automating Early Drug Development Robot Scientist Synthetic Biology Lead Compound Assay Design Learn and Test QSAR Library screen Hit Confirmation

10. Application Domain Malaria Shistosomaisis Leishmania Chagas

11. Why Tropical Diseases? • Millions of people die of these diseases, and hundreds of millions of people suffer infection. • It is clear how to cure these diseases – kill the parasites. • They are “neglected”, so avoid competition from the Pharmaceutical industry.

12. Synthetic Biology based Assays • Eve utilizes a standardized form of screening assay that combines advantages of: • computational assays (generality) • biochemical assays (targeted) • utilizing live cells (biological realism, and early screening for toxicity) • These are cheap (few £k) and quick (few weeks) to engineer.

13. Synthetic Biology based Assays • Our idea is to engineer cells to be Assay computers. • These computers will accurately estimate a biological function that corresponds to the set of desired assay properties. • The function estimated is the utility of a compound against a disease. • E.g. ((inhibit P. vivax DHFR) ∧ (¬ inhibit H. sapiens DHFR) ∧ (¬ cytotoxic)).

14. Synthetic Biology Workflow

15. Enzymes Targeted DihydrofolateReductase (DHFR) N-myristoyltransferase Phosphoglyceratekinase

16. Eve AI

17. The Experimental Cycle Background Knowledge Analysis Hypothesis Formation Experiment(s) selection Final Theory Robot Results Interpretation

18. Model v Real-World Experimental Predictions Biological System Logical Model Experimental Results

19. Representation for QSARs • Eve wishes to learn quantitative structure activity relationships (QSARs). Functions that predict compound activity from structure. • The standard method is to use attributes. Technically these are propositions that are true for the compounds, e.g. partial charge, a fingerprint, etc. Eve currently uses a form of fingerprint. • Compounds have relational structure. Propositions are provably inefficient at representing this. It is potentially much better to use predicated logic.

20. The Experimental Cycle Background Knowledge Analysis Hypothesis Formation Experiment(s) selection Final Theory Robot Results Interpretation

21. Inferring Hypotheses • Science is based on the hypothetico-deductive method. • In the philosophy of science. It has often been argued that only humans can make the “leaps of imagination” necessary to form hypotheses. • QSAR learning is a form of inductive hypothesis formation.

22. Learning QSARs • Almost every form of statistical and machine learning method you can think of has been applied to QSAR learning. • Leading methods are logistic regression, support vector machines, random forests. … • Eve currently uses Gaussian process models. Has the advantages of being generative and outputting probabilities – helps active learning.

23. The Experimental Cycle Background Knowledge Analysis Hypothesis Formation Experiment(s) selection Final Theory Robot Results Interpretation

24. Active Learning 1 • Active learning is the branch of machine learning where the machine can select its own experiments. • Eve uses active learning to select compounds to test the QSAR hypotheses. • This selection task is comparable to that in many other areas of science and engineering: identify or design artifacts that have optimal performance. • It has an extra ingredient reminiscent of reinforcement learning:finding the right balance between exploring compound space, and exploiting regions with highly active compounds.

25. Active Learning 2 • A successful approach was found to be a combination of selecting compounds with high estimated activity T, and high estimated variance, i.e. select the example where: T + b√var(T) is maximal • It is generally inefficient to assay (or synthesize) a single compound in a QSAR cycle, so batches of N compounds should be selected (for Eve N=64). This greatly increases the computational complexity of choosing the best experiment.

26. The Experimental Cycle Background Knowledge Analysis Hypothesis Formation Consistent Hypotheses Experiments(s) Experiment(s) selection Final Theory Robot Results Interpretation

27. Eve’s Automation of Pipeline • Standard library screening is brute force: • Eve uses intelligent screening • In the standard “pipeline” the 3 processes are not integrated. • In Eve automated and integrated. Library screening Hits Hit confirmation Confirmed hits Predicted hits Learn QSAR/Intelligent screening Lead Offline validation

28. Eve’s Hardware Highlights of Eve's hardware: • Acoustic liquid handling • High throughput 384 well plates • Two industrial robot arms • Automated 60x microscope • Liquid handlers, fluorescence readers, barcode scanners, dry store, incubator, tube decapper ...

29. The Experimental Cycle Background Knowledge Analysis Hypothesis Formation Experiment(s) selection Final Theory Robot Results Interpretation

30. Hit or Not? • Growth curves were fit to the time course, and growth parameters derived. • Machine learning was used to distinguish between: hit compounds, non-hits, toxic compounds, and Autofluorescent compounds. • The property of being a hit is not a Boolean function – quantitative.

31. The Experimental Cycle Background Knowledge Analysis Hypothesis Formation Experiment(s) selection Final Theory Robot Results Interpretation

32. Closing the Loop • We have physically implemented all aspects of Eve. • To the best of our knowledge Eve is the first laboratory automation system that can execute cycles of QSAR learning and testing. • To the best of our knowledge Eve is the first laboratory automation system that integrates: library screening, hit conformation, and QSAR learning.

33. Table of Results

34. Intelligent v Brute-force Screening 1 • We wished to compare our AI based screening against the standard brute-force approach: “begin at the beginning and go on till you come to the end: then stop” (Lewis Carroll). • While simple to automate standard screening is slow and wasteful of resources, since every compound in the library is tested. It is also unintelligent, as it makes no use of what is learnt during screening. • Use money to decide.

35. Intelligent v Brute-force Screening 2 • Developed an econometric model for the relative costs of the two approaches. • Use simulation runs based on Eve’s screening data to compare approaches. • Intelligent screening is most cost-effective with larger libraries, more valuable compounds, and fast cycles of screening and testing. Such regimes are standard for pharmaceutical screening,

36. Acknowledgments ABERYSTWYTH / MANCHESTER Wayne Aubrey Amanda Clare Douglas Kell Maria Liakata Chuan Lu Magda Markham Katherine Martin Ronald Pateman Jem Rowland Andrew Sparkes Larisa Soldatova Mike Young Ken Whelan CAMBRIDGE Steve Oliver Elizabeth Bilsland Pınar Pir Harry Moss Michael de Clare Mark Carrington LEUVEN Kurt De Grave Luc De Raedt Jan Ramon Support from BB/F008228/1 from the UK Biotechnology & Biological Sciences Research Council and a contract from the European Commission under the FP7 Collaborative Programme, UNICELLSYS.