Patent Searching using Bayesian Statistics

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2 Patent Searching using Bayesian Statistics Willem van Hoorn, Exscientia Ltd Biovia European Forum, London, June 2017

3 Contents Who are we? Searching molecules in patents What can Pipeline Pilot do for you? And what it cannot Proof of concept implementation 3

4 Pioneers of Centaur drug discovery The best of Artificial Intelligence and Human drug discovery expertise combined to deliver radical improvements in productivity Artificial Intelligence AI IA oversee Specialist drug design algorithms evolve, design, and propose what to make Intelligence Augmentation Empowered humans make final decisions and strategy Multiple applications for small molecule discovery Single target compounds Bispecific small molecules Phenotypic drug design 4

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6 Growth Through Collaborations Automated design for single targets Phenotypic Drug Discovery platform Bispecific small molecules immuno-oncology Bispecific small molecule discovery agreement in metabolic disease Company founded First identification of bispecfic small molecules for dual targets Bispecific small molecules targeting 2 distinct GPCRs CNS disease First milestone reached first candidate delivered Global pharma (to be announced Q2 2017) Multi-target discovery agreement Value potential 4M 50:50 240M+ 6

7 Searching structures in patents I have a brilliant new compound idea, is it novel? Patent text searching is easy (Google) Molecular structure searching is not For a quick answer: manual substructure search in SciFinder, SureChEMBL, etc Issue: need a substructure query This is an issue if you want to do this a lot, automatically 7

8 Searching patents - assumptions Structures from patents are available Claimed compounds in patent have common substructure Claimed structures are novel This looks like a set amenable to modelling 8

9 A trivial example Example patent (random): EP A1 Bayesian model: Good : structures from EP A1 Bad : structures from random 200k patents High scoring molecule shown Red atoms: high contribution to score ( novel ) Phenyl is common and has low contribution In a similarity search both would be equally important 9

10 Learn Molecular Categories Training set Set of molecules from multiple categories Typical user case: activity classes Here: category = patent Model prediction Probabilities molecule belongs to category What distinguishes molecule of category A from molecule of category B Prediction made on full structure 10

11 Example with four categories Each column is a category Each row is a fingerprint feature Cell = NormalizedProbability ~ weight of fingerprint Pos: feature associated with being in this class Neg: feature associated with not being in this class Insignificant: < value <

12 A patent model Downloaded SureChembl mapping files (Oct 2015) ~1.7M patents with ~20M Claimed structures (including duplicates) Selected random 1000 patents Random split: 80% training (152k), 20% test set (38k) Multi-category Bayesian model based on ECFP_6 A model with 997 categories was derived. Some patents contained few compounds, none left in 80% Predicted patent for test set 12

13 Result: ~75% ranks in top 3 Frequency (log scale) Rank of true patent in top 997 This looks promising 13

14 Fails: reagents, wrong structures Note these are Claimed structures (really?) Corpus frequency: Bayesian score If these are the failures I am not worried 14

15 The model matrix does not scale Exponential growth: adding a category adds a column, adding a compound adds 0 rows (most likely >0) Building a full patent model runs out of memory 15

16 Redundancy in multi-cat models 8878 rows x 4 categories = 35,512 values But only 1074 unique & significant values ( NP > 0.05) 16

17 Store the model in a database Databases are designed to deal with large data volumes Normalisation removes redundancy Indexing for fast lookup sql query to evaluate model Need the normalised probabilities And building 1.7M models is too slow 17

18 Bayes in PilotScript This is much faster since it skips the publication of the model in xmldb 18

19 Component Does scripted model work? For a random patent (EP A1) Create model by script and Learn Good Molecules Evaluate on SampleDrugs.sd Scores are close (not identical!) but correlate well Script Component Script 19

20 Model stored in database Runtime: ~10.5 hours (Core i7 laptop, 8 CPU, 32 Gb) 20

21 Model scores by sql query* select pt.sc_patent_id, sum(np.normalised_probability) as score from eps_normalised_probabilities np, eps_fingerprints fp, eps_patents pt where np.fingerprint_id = fp. fingerprint_id and np.patent_id = pt. patent_id and fp.feature in ( fingerprints of query structure ) group by pt.number order by score desc Runtime: 2-3 minutes per compound * Need to correct for features not in this patent, see slide in backups 21

22 A test Query: Sildenafil Top-ranked patents: Top ranked patent lists (all?) known PDE5 inhibitors in Claim All other patents also contain Sildenafil However: original Sildenafil patent not found? 22

23 US A Original Sildenafil patent It is not in the 1.7M patents model Yet it is in SureChEMBL, with structures In downloaded copy, all structures are annotated as Description, not Claims. Therefore ignored This is frustrating 23

24 Conclusions Have prototype that shows technique works However it is not yet useful SureChEMBL data not yet accurate enough (Oct 2015) Claims vs Description, etc Multiple structures are wrong 24

25 EXSCIENTIA LTD, LAB 12 - DUNDEE INCUBATOR JAMES LINDSAY PLACE, DD1 5JJ, UNITED KINGDOM CONTACT@EXSCIENTIA.CO.UK

26 SureChEMBL 26

27 Searching virtual libraries Training set: ~100k compounds, ~5k categories 27

28 Evaluate model scores For each compound, fingerprints Keep top 1000 get patent name 1. get sum(np) for known fp 3. get Laplacian, Pactive for patent 2. get array of known FP 4. correct for unknown fingerprints Runtime: 2-3 minutes 28

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