PrePeP: A Tool for the Identification and Characterization of Pan Assay Interference Compounds

ABSTRACT

Pan Assays Interference Compounds (PAINS) are a significant problem in modern drug discovery: compounds showing non-target specific activity in high-throughput screening can mislead medicinal chemists during hit identification, wasting time and resources. Recent work has shown that existing structural alerts are not up to the task of identifying PAINS. To address this short-coming, we are in the process of developing a tool, PrePeP, that predicts PAINS, and allows experts to visually explore the reasons for the prediction. In the paper, we discuss the different aspects that are involved in developing a functional tool: systematically deriving structural descriptors, addressing the extreme imbalance of the data, offering visual information that pharmacological chemists are familiar with. We evaluate the quality of the approach using benchmark data sets from the literature and show that we correct several short-comings of existing PAINS alerts that have recently been pointed out.

References

1. 2017. The Open Source Chemistry Toolbox. (dec 2017). https://openbabel.org
2. 2017. Public database of chemical molecules and their activities against biological assays. (2017). https://pubchem.ncbi.nlm.nih.gov/about.html
3. 2017. RDKit: Open-Source Cheminformatics. (2017). http://www.rdkit.org
4. Courtney Aldrich, Carolyn Bertozzi, Gunda I. Georg, Laura Kiessling, Craig Lindsley, Dennis Liotta, Kenneth M. Merz Jr., Alanna Schepartz, and Shaomeng Wang. 2017. The ecstasy and agony of assay interference compounds. (2017).
5. Pieter Swart et al. Aric Hagberg, Dan Schult. 2017. Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. (dec 2017). https://networkx.github.io
6. Jonathan Baell and Georgina Holloway. 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) (2010), 2719--2740.
7. Jonathan B. Baell. 2016. Feeling nature's PAINS: Natural products, natural product drugs, and pan assay interference compounds (PAINS). Journal of natural products 79, 3 (2016), 616--628.
8. Jonathan B. Baell and Walters, Michael A. 2015. Chemical con artists foil drug discover. Nature 7519, 513 (2015), 481--483.
9. M. R. Berthold, N. Cebron, T. R. Dill, F. and Gabriel, T. Kötter, T. Meinl, C. Ohl, P.and Sieb, K. Thiel, and B. Wiswedel. 2007. KNIME: The Konstanz Information Miner. Springer, Chapter Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007).
10. BIOVIA. 2017. BIOVIA Pipeline Pilot. (2017). http://accelrys.com/products/collaborative-science/biovia-pipeline-pilot/
11. BIOVIA. 2017. CTfile Formats. (2017). http://accelrys.com/products/collaborative-science/biovia-draw/ctfile-no-fee.html
12. N. Bosc, B. Wroblowski, C. Meyer, and P. Bonnet. 2017. Prediction of Protein Kinase-Ligand Interactions through 2.5D Kinochemometrics. J. Chem Inf Model. 57, 1 (2017), 93--101.
13. Leo Breiman. 1996. Bagging Predictors. Machine Learning 24, 2 (1996), 123--140.
14. Leo Breiman, Jerome Friedman, Charles J. Stone, and R. A. Olshen. 1984. Classification and Regression Trees. Chapman &Hall, New York. 358 pages.
15. Björn Bringmann, Albrecht Zimmermann, Luc De Raedt, and Siegfried Nijssen. 2006. Don't Be Afraid of Simpler Patterns. In 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Johannes Fürnkranz, Tobias Scheffer, and Myra Spiliopoulou (Eds.). Springer, 55--66.
16. Matthieu Brucher. 2017. Library of Machine Learning tools in Python. (dec 2017). http://scikit-learn.org
17. Stephen J. Capuzzi, Eugene N. Muratov, and Alexander Tropsha. 2017. Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS. Journal of chemical information and modeling 57, 3 (2017), 417--427.
18. Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine learning 20, 3 (1995), 273--297.
19. John G. Cumming, Andrew M. Davis, Sorel Muresan, Markus Haeberlein, and Hongming Chen. 2013. Chemical predictive modelling to improve compound quality. Nature reviews Drug discovery 12, 12 (2013), 948.
20. Jayme L. Dahlin, J. Willem M. Nissink, Jessica M. Strasser, Subhashree Francis, LeeAnn Higgins, Hui Zhou, Zhiguo Zhang, and Michael A. Walters. 2015. PAINS in the assay: chemical mechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS. Journal of medicinal chemistry 58, 5 (2015), 2091--2113.
21. Richard Eglen, Terry Reisine, Philippe Roby, Nathalie Rouleau, Chantal Illy, Roger Bosse, and Martina Bielefeld. 2008. The Use of AlphaScreen Technology in HTS: Current Status. Journal of Current Chemical Genomics 1 (2008), 2--10.
22. J. M. Gally, S. Bourg, Q. T. Do, S. Aci-Sèche, and P. Bonnet. 2017. VSPrep: A General KNIME Workflow for the Preparation of Molecules for Virtual Screening. Molecular Informatics 36 (2017).
23. Rajarshi Guha. 2008. On the interpretation and interpretability of quantitative structure-activity relationship models. Journal of computer-aided molecular design 22, 12 (2008), 857--871.
24. Daylight Chemical Information Systems, Inc. {n. d.}. Simplified Molecular Input Line Entry System. ({n. d.}). http://www.daylight.com/smiles/index.html
25. Akihiro Inokuchi and Takashi Washio. 2008. A fast method to mine frequent subsequences from graph sequence data. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. IEEE, 303--312.
26. Swarit Jasial, Ye Hu, and Jürgen Bajorath. 2017. How frequently are pan-assay interference compounds active? Large-scale analysis of screening data reveals diverse activity profiles, low global hit frequency, and many consistently inactive compounds. Journal of Medicinal Chemistry 60, 9 (2017), 3879--3886.
27. Ulf Johansson, Cecilia Sönströd, Ulf Norinder, and Henrik Boström. 2011. Tradeoff between accuracy and interpretability for predictive in silico modeling. Future medicinal chemistry 3, 6 (2011), 647--663.
28. Michihiro Kuramochi and George Karypis. 2001. Frequent subgraph discovery. In Data Mining, 2001. ICDM 2001, Proceedings IEEE international conference on. IEEE, 313--320.
29. Sylvain Lozano, Guillaume Poezevara, Marie-Pierre Halm-Lemeille, Elodie Lescot-Fontaine, Alban Lepailleur, Ryan Bissell-Siders, Bruno Cremilleux, Sylvain Rault, Bertrand Cuissart, and Ronan Bureau. 2010. Introduction of jumping fragments in combination with QSARs for the assessment of classification in ecotoxicology. Journal of chemical information and modeling 50, 8 (2010), 1330--1339.
30. Thomas Mendgen, Christian Steuer, and Christian D. Klein. 2012. Privileged scaffolds or promiscuous binders: a comparative study on rhodanines and related heterocycles in medicinal chemistry. Journal of medicinal chemistry 55, 2 (2012), 743--753.
31. Jean-Philippe Métivier, Alban Lepailleur, Aleksey Buzmakov, Guillaume Poezevara, Bruno Crémilleux, Sergei O. Kuznetsov, Jérémie Le Goff, Amedeo Napoli, Ronan Bureau, and Bertrand Cuissart. 2015. Discovering structural alerts for mutagenicity using stable emerging molecular patterns. Journal of chemical information and modeling 55, 5 (2015), 925--940.
32. Shinichi Morishita and Jun Sese. 2000. Transversing itemset lattices with statistical metric pruning. In Proceedings of the 19th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. 226--236.
33. Kathryn M. Nelson, Jayme L. Dahlin, Jonathan Bisson, James Graham, Guido F. Pauli, and Michael A. Walters. 2017. The essential medicinal chemistry of curcumin: miniperspective. Journal of medicinal chemistry 60, 5 (2017), 1620--1637.
34. Siegfried Nijssen and Joost Kok. 2006. Frequent subgraph miners: runtimes don't say everything. In Proceedings of the Workshop on Mining and Learning with Graphs. 173--180.
35. Siegfried Nijssen and Joost N. Kok. 2004. A quickstart in frequent structure mining can make a difference. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 647--652.
36. Martin Pouliot and Stephane Jeanmart. 2015. Pan Assay Interference Compounds (PAINS) and Other Promiscuous Compounds in Antifungal Research: Miniperspective. Journal of medicinal chemistry 59, 2 (2015), 497--503.
37. Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1135--1144.
38. Jürgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural networks 61 (2015), 85--117.
39. Sanjay Joshua Swamidass, Jonathan H. Chen, Jocelyne Bruand, Peter Phung, Liva Ralaivola, and Pierre Baldi. 2005. Kernels for small molecules and the prediction of mutagenicity,toxicity and anti-cancer activity. 359--368.
40. Natasha Thorne, Douglas S. Auld, and James Inglese. 2010. Apparent activity in high-throughput screening: origins of compound-dependent assay interference. Current opinion in chemical biology 14, 3 (2010), 315--324.
41. Tihomir Tomašič and Lucija Peterlin Mašič. 2012. Rhodanine as a scaffold in drug discovery: a critical review of its biological activities and mechanisms of target modulation. Expert opinion on drug discovery 7, 7 (2012), 549--560.
42. David J. Wood, David Buttar, John G. Cumming, Andrew M. Davis, Ulf Norinder, and Sarah L. Rodgers. 2011. Automated QSAR with a hierarchy of global and local models. Molecular informatics 30, 11-12 (2011), 960--972.
43. Marc Wörlein, Thorsten Meinl, Ingrid Fischer, and Michael Philippsen. 2005. A quantitative comparison of the subgraph miners MoFa, gSpan, FFSM, and Gaston. In European Conference on Principles of Data Mining and Knowledge Discovery. Springer, 392--403.
44. Xifeng Yan and Jiawei Han. 2002. gSpan: Graph-Based Substructure Pattern Mining. In ICDM. IEEE Computer Society, 721--724.
45. Jeremy J. Yang, Oleg Ursu, Christopher A. Lipinski, Larry A. Sklar, Tudor I. Oprea, and Cristian G. Bologa. 2016. Badapple: promiscuity patterns from noisy evidence. Journal of cheminformatics 8, 1 (2016), 29.
46. Albrecht Zimmermann, Björn Bringmann, and Ulrich Rückert. 2010. Fast, Effective Molecular Feature Mining by Local Optimization. In ECML/PKDD (3) (Lecture Notes in Computer Science), José L. Balcázar, Francesco Bonchi, Aristides Gionis, and Michèle Sebag (Eds.), Vol. 6323. Springer, 563--578.

Index Terms

• Information systems

  • Information retrieval

    • Specialized information retrieval

      • Structure and multilingual text search

        • Chemical and biochemical retrieval

  • Information systems applications

    • Decision support systems

      • Data analytics

• Mathematics of computing

  • Probability and statistics

    • Statistical paradigms

      • Exploratory data analysis