Revising the Topliss Decision Tree
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The document discusses revising the Topliss decision tree for analog synthesis based on 30 years of additional medicinal chemistry literature and data from the ChEMBL bioactivity database. It describes creating a "Matsy decision tree" directly from experimental matched molecular series data in ChEMBL, which supports most of the Topliss tree but suggests some differences. The authors developed a data-driven approach not limited to the original Topliss trees to make predictions backed by experimental evidence from targeted datasets.
![Matched Series of length 3
[Cl, F, NH2]](https://image.slidesharecdn.com/medi-revisingtoplisstreeaug2014-140811093251-phpapp02/85/Revising-the-Topliss-Decision-Tree-9-320.jpg)
![Matched Series of length 3
[4-Cl-Ph, 4-F-Ph, 4-NH2-Ph]](https://image.slidesharecdn.com/medi-revisingtoplisstreeaug2014-140811093251-phpapp02/85/Revising-the-Topliss-Decision-Tree-10-320.jpg)
![Ordered Matched Series
[4-Cl-Ph > 4-F-Ph > 4-NH2-Ph]
3.5
2.1
1.6
pIC50](https://image.slidesharecdn.com/medi-revisingtoplisstreeaug2014-140811093251-phpapp02/85/Revising-the-Topliss-Decision-Tree-11-320.jpg)
Revising the Topliss Decision Tree
- 1. Revising the Topliss decision tree …based on 30 years of medicinal chemistry literature Noel O’Boyle and Roger Sayle NextMove Software Jonas Boström AstraZeneca 248th ACS National Meeting Aug 2014
- 3. Topliss Tree for Substituted Phenyl Topliss, J. G. Utilization of Operational Schemes for Analog Synthesis in Drug Design. J. Med. Chem. 1972, 15, 1006–1011.
- 4. Features of the Topliss Tree • Maximize the chances of synthesizing the most potent compound in the series as soon as possible • Based on inferring Hansch structure-activity relationship from relative potencies of R groups – Electronic (σ), hydrophobic (π), steric (Es) • General scheme – for any target – for any scaffold
- 5. ChEMBL Bioactivity database • July 2008 - ChEMBL established with Wellcome Trust grant – John Overington, EMBL-EBI • Open access source of bioactivity data abstracted from the literature – Chemical structures, activity values, activity type, assay description, journal article name, target – www.ebi.ac.uk/chembl/ Gaulton et al. Nucleic Acids Res. 2012, 40, D1100
- 6. ChEMBL Bioactivity database • ChEMBL 19 – July 2014 – 57k papers • 94% from Bioorg. Med. Chem. Lett., J. Med. Chem., J. Nat. Prod., Bioorg. Med. Chem., Eur. J. Med. Chem., Antimicrob. Agents Chemother., Med. Chem. Res. – 1.4 million compounds with 12 million activities – 1.1 million assays against 10k targets
- 7. 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 1977 1982 1987 1992 1997 2002 2007 2012 Count Year Number of articles extracted from a particular year
- 8. Matched (Molecular) series • Recent concept in cheminformatics (*) – … not so recent in medicinal chemistry • Series of structural analogs – same scaffold – different R groups at a single position * “Matching molecular series” introduced by Wawer and Bajorath J. Med. Chem. 2011, 54, 2944
- 9. Matched Series of length 3 [Cl, F, NH2]
- 10. Matched Series of length 3 [4-Cl-Ph, 4-F-Ph, 4-NH2-Ph]
- 11. Ordered Matched Series [4-Cl-Ph > 4-F-Ph > 4-NH2-Ph] 3.5 2.1 1.6 pIC50
- 12. 1 10 100 1000 10000 100000 1000000 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 Frequency Series length Matched series in ChEMBL19 IC50 binding assays Length 2: 240,967 212,494 Length 3: 59,753 52,666 Length 4: 27,779 24,306 Length 5: 15,892 13,834 Length 6: 10,619 9,203 Method described in O’Boyle, Boström, Sayle, Gill. Using Matched Molecular Series as a Predictive Tool To Optimize Biological Activity. J. Med. Chem. 2014, 57, 2704. (ChEMBL16)
- 13. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Frequency R Groups sorted by frequency Analysis of the 16268 matched series containing at least 4 substituted phenyls
- 14. Find R Groups that increase activity A > B Query A > B > C C > A > B D > A > B > C D > A > C > B E > D > A > B … R Group Observations Obs that increase activity % that increase activity D 3 3 100 E 1 1 100 C 4 1 25 … … … O’Boyle, Boström, Sayle, Gill. Using Matched Molecular Series as a Predictive Tool To Optimize Biological Activity. J. Med. Chem. 2014, 57, 2704.
- 15. Example
- 16. Example II
- 20. Topliss Decision Tree (1st if lower cutoff)
- 24. Topliss Decision Tree (21st) “Assuming that the –σ effect is the most probable explanation…”
- 30. Matsy Decision Tree (Take II)
- 31. Target specific subsets 4-Cl > H Everything Kinases Class A GPCRs
- 32. Account for Lipophilic Efficiency • ΔLiPE = ΔpIC50 – ΔLogP • The “%>” value is based on the number of times a particular R group has greater pIC50 – i.e. ΔpIC50 > 0 • Redefine it to only include cases where the increase in pIC50 was larger than any increase in LogP – i.e. ΔpIC50 > 0 and ΔLiPE > 0
- 33. 4-Cl > H ΔLiPE > 0
- 34. ΔLiPE > 0 3,4-diCl > 4-Cl > H
- 35. Data-driven approach • Not limited to the two trees in the Topliss paper • All predictions backed by experimental data – Can drill-down into the data, look at targets, scaffolds – Can restrict experimental data used to particular targets, use in-house data rather than ChEMBL • Does not explain why, only that it happens
- 36. Conclusions • In the main, the Topliss Tree is supported by published data – Largest difference is recommendation of 4-OMe rather than 4-OH – Suggestion of 4-CF3 is also problematic • We have generated the corresponding ‘Matsy Tree’ derived from experimental data
- 37. drag-and-drop interface to Matsy
- 38. Revising the Topliss decision tree …based on 30 years of medicinal chemistry literature Using Matched Molecular Series as a Predictive Tool To Optimize Biological Activity J. Med. Chem. 2014, 57, 2704. Want to hear more? Poster COMP 394 Tuesday 6:00-8:00pm Marriott Marquis Interested in an evaluation copy of Matsy? Come by our booth noel@nextmovesoftware.com