Chemistry Informatics in Academic Laboratories: Lessons Learned

Transcription

1 Chemistry Informatics in Academic Laboratories: Lessons Learned Michael Hudock Center for Biophysics & Computational Biology University of Illinois at Urbana-Champaign

2 My Background Ph.D. candidate, Biophysics & Computational Biology, University of Illinois at Urbana- Champaign. Associate Research Scientist, Discovery Technologies group at Bristol-Myers Squibb prior to graduate school. Strong interest in the interface of computers and chemistry, graduate work in computational modeling with chemoinformatics.

3 Talk Outline Chemoinformatics System Our Basic Requirements Registration / Results / Reports / Research Build vs. Buy Infrastructure / Cost / Maintenance / Development Results & Lessons Learned Short-term impact / long-term impact Future Directions New, advanced SAR modules

4 Our Laboratory

5 Our Basic Requirements Registration ~50 assays Results Reports Research Y= c + a b + c d +

6 A Decision Point Commercial Solution "Out of the box" functionality Restrictive Infrastructure Requirements Expensive, Perhaps Recurring Costs Completely Customizable? Programming Expertise Testing & Deployment Data Backup Custom Solution Decision to develop a custom solution that would meet, at first, our most basic requirements, with capability to expand at a later date.

7 Client-Server Architecture Multiple client platforms supported All code resides on the server Data all stored in one location

8 Specific Implementation Modular architecture allows new components to be quickly and easily added.

9 Database Architecture

10 Compound Registration

11 Input Results

12 Structures & Data United Using ChemAxon Marvin Java Applet

13 Retrieve Data Easily

14 Real-Time Data Analysis New analysis tools can be added quickly in response to user requests

15 Finding Patterns in a Few Clicks === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 26 Provide SAR tools to all users, help detect trends. === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure Class cluster cluster2 === Confusion Matrix === a b <-- classified as 12 1 a = cluster b = cluster2

16 Additional Modules Easily Added Additional modules added over time as needed

17 Initial Impact Initial Development: 1 FTE, 1 month Updates & New Code: 1 FTE, 3 days/month Intuitive interface, short end user training Pre- Chemoinformatics Chemoinformatics What is the structure of compound 700? 20 sec. 20 min. Correlate assay A with assay B 5 sec. 30 min. for compounds 65% similar to cpd sec. 45 min. or instead, with assays B N 15 sec. 5 hours Will addition of CH 2 to 352 decrease activity? 10 sec. 25 min. Is assay A activity related to TPSA? 5 sec. 20 min. An informatics solution, commercial or custom, can have large positive impact on productivity - even for relatively small amounts of data.

18 Longer-Term Impact >1,000 unique compounds, >11,000 fittings Used daily by group members (~30) Data easily shared with entire group Trends now routinely identified publications Mindset: paper to electronic

19 How can I do this? Identify and implement basic requirements first, don t go overboard Programming typically requires functional understanding of databases and programming language such as PHP*. CS students, temporary help or computer savvy graduate students might be able help Use third-party components when appropriate, e.g. for plotting, displaying structures System can evolve over time, with sophisticated capabilities added with additional experience *Good Books: PHP and MySQL Web Development, Welling & Thompson, Web Database Applications with PHP & MySQL, Williams & Lane, 2004.

20 Acknowledgements CINF Division for the invitation to present National Institutes of Health Professor Eric Oldfield and members of the Oldfield Research Group, Department of Chemistry, University of Illinois at Urbana-Champaign Professor Eric Oldfield Yongcheng Song Yonghui Zhang Fenglin Yin Kilannin Krysiak Sujoy Mukherjee Dushyant Mukkamala Rong Cao Kyle Bergan