Derrick DeConti - PAINS train

1. PAINS Train PAINS Train Identifying false positives in drug screens Identifying false positives in drug screens Derrick DeConti Derrick DeConti Insight Health Data Science Insight Health Data Science

2. Drug Screens Target of Interest

3. Non-specific (promiscuous) Specific Target D Target D Target A Target A Target B Target B Target C Target C

4. Pan Assay Interference Compounds (PAINS)

5. Current Practice ●Poor sensitivity ●Experience – Learned heuristics ●Poor precision ●Structural similarity – Hard filters ●High FDR

6. Machine Learning Approach ●Based on chemical structure ●Creates own substructure classification ●Provide a likelihood of promiscuity

7. Demo

8. Labeled Data PAINS Non-PAINS – Baell 20101 – ChEMBL – Empirically derived 1. Baell, J. B., & Holloway, G. a. (2010). New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry, 53(7), 2719–2740. doi: 10.1021/jm901137j

9. Transformation of Data with RDKit [ 1 0 0 ..., 0 1 0] 2,048 bit vector

10. Clustering of PAINS

11. Clustering of PAINS Overlap in cluster-based classification

12. Predictive Classification Random Forest ●Binary format of data ●Interdependence within vector Testing ●5-fold validation ●20% left out for validation set

13. Secondary Chemical Set ●Neglected tropical disease compound set ●Composed of two structural classes ●Drug-like molecules

14. Validation ●Spike in PAINS – Into secondary chemical set ●Test previously trained random forest – Versus cluster based method

15. Comparison of Methods

16. FDA Drug Promiscuity ●1,055 FDA approved drugs – 38 classified as promiscuous ●86% cluster closely

17. About Me Bioinformatics Genomics

18. Random Forest Optimization

19. Secondary Chemical Set

20. K-means clustering within PAINS