1. PyMine – A PyMol Plugin to Integrate and Visualize Chemical and Biological Data for Drug Discovery Zhijun Li, Ph.D. Departments of Chemistry & Biochemistry University of the Sciences in Philadelphia USA

2. Drug discovery is a costly process http://www.chemistry-blog.com/2012/01/04/tedtalk-medicine-for-the-99-hes-about-99-wrong/.

3. Cheminformatics and bioinformatics can facilitate drug discovery Target identification Target modeling In silico screening and design Krasky et al., Genomics, 2007, 89: 36-43.

4. Data integration remains a challenge www.ncbi.nlm.nih.gov

5. Current Approaches for Bioinformatics Data Integration Service-oriented web approach Data warehousing approach Other approaches

6. PyMine Development PyMine Development

7. PyMol Molecular visualization system Open source Leading software package http://ww.pymol.org

8. Databases UniProt PDB ChEMBL IBIS HUMSAVAR KEGG

9. Integration

10. Interface and Output Chaudhari and Li., BMC Research Notes, 2015, submitted. Panel InterfacePyMol 3D visualizationOutput Directory

11. Case Study Case Study

12. Human Malic Acid Enzymes Major enzymes in the production of NADPH Also closely associated with the TCA cycle, a central metabolic hub In mammalian cells, two major malic enzyme isoforms (ME1 and ME2) have been identified

13. Malic enzymes reciprocally module p53 Jiang et al., Nature, 2013, 493: 689-695.

14. PyMine Study and CADD Approach ME1 structure Catalytic site Known inhibitors Ligand-based approach using ShapeSignature Structure-based approach using Glide Assay testing

15. CADD Results

16. Summary Developed an innovative and automated tool to integrate and visualize chemical and biological data for drug discovery. PyMine integrates data from six high-quality chemical and biological databases and maps imported structural information onto receptor 3D structure. PyMine imports and/or generates files that can be used directly for drug discovery projects. https://github.com/rrchaudhari/PyMine

17. Future Development To include support for additional chemical/biological databases. To apply inductive logic programming in order to generate and prioritize data driven hypotheses. To automate cheminformatics workflows based on imported structural data to test generated hypothesis.

18. Acknowledgement PhRMA Foundation NSF NIHUPenn- ITMAT Dr. Jun Gao Dr. Vagmita Pabuwal Dr. Jhenny Galan Dr. Andrew Heim Usha K. Muppirala Rajan Chaudhari Daoyuan Dong Tejashree Redij David Cho Univ. of Pennsylvinia Dr. Wenchao Song; Dr. Xiaolu Yang CHOP Dr. Hakon Hakonarson, Duke University Dr. Chin-Ho Chen; Dr. Li Huang Wistar Institute Dr. Jose Conejo-Garcia University of Sydney, Australia Dr. John Christodoulou Meharry Medical College Dr. Hua Xie Univ. of Missouri Dr. Gerald L. Hazelbauer Usciences Dr. Zhiyu Li; Dr. Preston Moore