Árpád Figyelmesi (ChemAxon, Hungary) During the recent years like other segments, cheminformatics also entered the field of big data. As we see quick transition from the traditional methods towards the direction of need for handling significantly large sets of data often in unstructured or semistructured forms. Words and phrases like, terabytes, scalability, NoSQL, cloud solutions are integrated in our everyday language. I would like to present a few case studies to highlight key features of this transition. Such as sowing techniques and technologies for handling different aspects of this new area.

1. CHEMINFORMATICS
SCALING-UP FOR THE AGE
OF BIG DATA
Árpád Figyelmesi
29th ICIC Heidelberg 2017

2. Big data
Big data is a term for data sets that are so large or complex that traditional data
processing application software is inadequate to deal with them.
(Wikipedia)
Big data is high-volume, high-velocity and/or high-variety information assets that
demand cost-effective, innovative forms of information processing that enable
enhanced insight, decision making, and process automation.
(Gartner – IT Glossary)

3. TRADITIONAL SEARCH

4. Why searching?
• Find relevant chemical patterns (substructure search)
• Find similar compounds (similarity search)
• Find building blocks (superstructure search)
• Compare libraries (overlap analysis)
• Identify compound families (clustering)

5. Screening
Library
250k-1M
Real life chemical data

6. Corporate
Compound
Repository
3M
Chemical data in patent literature

7. Enumerated virtual compounds

8. Old „traditional” vs New „traditional”

9. SureChEMBL real-time search

10. SOMETHING LESS TRADITIONAL

11. MadFast Similarity Search
• Ultra fast chemical similarity
search
• In-memory data storage
• Optimized multi-threaded
implementation

12. Near real-time search of 1 billion

13. 100k x 1M exhaustive search in a few mins

14. Chemical white-space analysis
• Find potential drug analogs/novel ring systems
• From synthetically feasible virtual chemical space
• As close as possible to known drugs
• As far as possible from patented compounds

15. Case study
• Filter drugs from ChEMBL
• Search analogs in GDB-13
• Analyze overlap with
SureChEMBL
https://www.chemaxon.com/app/uploads/2017/03
/mfss-study-poster-23-1-imre-gabor-1.pdf

16. Results

17. Results

18. Results

19. THE FUTURE IS NOW

20. Extreme fast search engines
• Search time is not a question anymore
• Hits as you draw
• Real-time clustering for search suggestions and
drill down
New ways of interactions fundamentally change
the chemical patent search

21. Beyond the search
• Real-time analysis of extreme large
compound libraries
• Proactive exploration of synthetically
feasible and patentable chemical space

22. THANK YOU
Árpád Figyelmesi