Chemical intelligence that makes hidden knowledge effortlessly reachable
The knowledge, that is being produced and stored in the forms of reports, patents and scientific journal articles is expanding exponentially. Although, the unstructured nature of such contents impose constraints for seamless information access and scientific decision support. Chemistry is a unique field in this regard, for two reasons. First, the nomenclature is verbose in a sense that a chemical structure can be represented with various synonyms, for example traditional name, IUPAC name or a wide range of brand names or chemical formats (SMILES). Second, the navigation in the knowledge base, with queries related to the encapsulated chemical space, calls for peculiar search methods like similarity-based or substructure searches. Our study highlights computational approaches to turn chemistry related knowledge stored in all the open access articles easily accessible. We present our results obtained on this large corpus through the following workflow: i) large-scale conversion of text content to chemical objects, ii) automated preparation of databases to store and organize relevant data, and iii) analysis of the collected chemistry space. Extraction of chemical objects was done from nearly 1.9M articles that stretches the chemical space of open access scientific literature with ChemLocator application. Chemical space was analysed with calculation of fingerprint-based chemical similarity matrix and clustering by MadFast Similarity Search. In order to explore the scaffold diversity of this exclusive chemical space, the obtained set was fragmented to yield rings and ring systems. Hidden relationships were explored by combining text and chemical information in graph data model and related visualization. In summary, our use-case highlights the potential of novel technologies to pre-process, search and explore the information network enfolded in large document sets on the field of chemistry.
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Chemical intelligence that makes hidden knowledge effortlessly reachable
- 1. CHEMICAL INTELLIGENCE THAT MAKES HIDDEN KNOWLEDGE EFFORTLESSLY REACHABLE Jozsef David
- 2. ChemAxon 1998 2018 Foundation (2 founders + 2 PhD students), CEO: Ferenc Csizmadia (nick: Csizi) (find him in the room) 160 employees worldwide 30+ products
- 3. HQ - Budapest (Hungary) Development, Consultancy Prague (Czech Republic) Sales, Consultancy and Application Scientists Boston and San Diego (United States)
- 4. ChemAxon around the Globe Distributor s Spain, India, China, Korea, Japan, Singapore, Australia Clients 500+ Partners 50+ Users 300 000+
- 5. Agenda (and process) EXTRACTIO N BUILD GRAPH ANALYSIS FURTHER USE 15 days 4-5 weeks 2 weeksTime spent:
- 7. Data set INPUT • Documents from ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/ • size: ~30 Gb (compressed) • format: xml • Document count: ~1.9 million OUTPUT • Chemical database • Distinct structures: ~211.000 • All occurrences: ~53 million • MSSQL DB: 800 GB • Graph DB: 10.5 GB • Free text DB: 110 GB
- 8. CHEMLOCATOR Tools MADFAST SEARCH HARDWARE INSTANT JCHEM CALCULATORS STANDARDIZER KNIME NODES PROVIDER TYPE MEMORY CPU COUNT DISK SIZE Amazon M4.4xlarge 64 GB 16 1.5 TB
- 9. EXTRACTION
- 11. Corporate ID OSR / OCR Chemical files and formats Nomenclature
- 12. CONTENT ANALYSIS
- 13. 232351 723042 1990 1995 2000 2005 2010 2015 Number of articles published per year based on ScienceDirect cumulative 2524 277666 1990 1995 2000 2005 2010 2015 Number of open access publications per year availablein PubMed Central cumulative
- 14. Growing number of published molecules Published molecules in the open access documents
- 16. found in chembl_2 3 ambiguous structures new to chembl_23 open access compounds 15% 80% 5% ChEMBL biologicall y relevant othernon- metabolite s open access compounds 7% 93% ChEBI Comparison
- 19. 0 0.3 0.6 0.9 0 0.3 0.6 0.9 percentage of compounds containing these rings Ring popularity % Rings
- 20. 0.41 0.04 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.00 0.10 0.20 0.30 0.40 percentageoccurrenceof mostabundantrings Ring popularity
- 21. -150 -100 -50 0 50 100 150 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 rankdifferencetochembl_23 Rank difference to chembl_23 of most abundant ring types - Popularity is the same - Missing in ChEMBL - More popular in the Open Access set - More popular in ChEMBL
- 22. Chemical space analysis- Similarity matrix was calculated for the entire extracted compound set (~4.8*1010 comparisons) with MadFast Similarity Search - Similarity space - 77% (169k) compounds have the most similar structure within 0.3 Tanimoto dissimilarity (Tds) - ~20% have a pair within 0.15 Tds Min(Tds) per compound Cummulativecount 0 40000 80000 120000 160000 200000 0.03 0.06 0.09 0.12 0.15 0.18 0.21 0.24 0.27 0.3
- 23. Chemical space analysis- Degree of vertices were calculated in the chemical graph of OA articles, where structures are connected if the Tanimoto dissimilarity is <0.3 - Statistics - Modus: 1, Median: 19, Max: 1857 - 21% have higher degree than 100 - High degree nodes are peptides (top 100 are peptides) - Related to linear hash fingerprint, peptides from the same residues in different order are not distinguished - Linear hash fingerprint based Jarvis- Patrick clustering resulted in: - 62708 clusters (largest cluster has 19690 compounds) - 46169 singletons
- 24. Name Count peptide 1 673 902 glucose 1 629 175 amino acid 1 187 805 cholesterol 623 803 ethyl alcohol 581 079 sodium chloride 493 985 fatty acid 451 170 phosphate 437 956 dimethyl sulfoxide 368 572 glass 366 384 Which chemical entities are being discussed in OA scientific literature frequently? - Statistics based chemical entity occurence in publications - Common and generic chemical entities are in top 10 - Most frequently mentioned vitamin is vitamin-D2 (258.1k documents) - Most frequently mentioned drug is Cisplatin (220.5k documents), second is the penicillin-core (178.1k documents)
- 25. Which chemical entities are being discussed in OA scientific literature frequently? ~50% of the structures (110.7k) are mentioned in 1 publication only 15% of the structures (33.4k) are mentioned in at least 10 publications 76% of the ChEMBL drugs (Phase=4, Mw>0) are present
- 26. How many structures are extracted from documents? Lo w - There are three major classes based on number of compounds mentioned: - Low: not chemistry focused (amino acid, carbon dioxide, glass, peptide) - Medium: SAR, medicinal chemistry, chemical biology, pharmacology, biotechnology journals - High number: large-scale screening, QSAR/QSPR, method development, benchmark studies - The number of compounds per article provides a possibility for data cleaning and filtering Mediu m Hig h
- 27. How many structures are extracted from documents? 1016 compounds screened, in total 1018 converted Lo w Mediu m Hig h
- 29. Graph path finding DB size 10.42 GB Nodes count 13 769 389 Relationshi ps 124 442 817 Relationshi p types Keys, tags, similarity, contains, references, created (by authors)
- 30. donepezil dopamine GOAL • Find a route from one chemical space to another hopping through molecules • In maximum 5 steps ~8 sec
- 31. Comparing the chemical space of two drug targets
- 36. THANK YOU Jozsef David, Dora Barna, Akos Tarcsay David Malatinszky, Gabor Imre, Peter Biro, Catalin Boldan