![TEP =
[𝐷𝑟𝑢𝑔 𝑿,𝑡 ] 𝑓𝑟𝑒𝑒
𝐾 𝑑
Target engagement potential (TEP)
A basis for molecular design?](https://image.slidesharecdn.com/pwkdataanalysissins-140806154014-phpapp02/85/Data-analytic-sins-in-property-based-molecular-design-2-320.jpg)
Data-analytic sins in property-based molecular design
- 1. Data-analytic sins in property-based molecular design Peter Kenny pwk.pub.2008@gmail.com | http://fbdd-lit.blogspot.com
- 2. TEP = [𝐷𝑟𝑢𝑔 𝑿,𝑡 ] 𝑓𝑟𝑒𝑒 𝐾 𝑑 Target engagement potential (TEP) A basis for molecular design?
- 3. Property-based design as search for ‘sweet spot’
- 4. Correlation • Strong correlation implies good predictivity – I have observed a correlation so you must use my rule • Multivariate data analysis (e.g. PCA) usually involves transformation to orthogonal basis • Applying cutoffs (e.g. MW restriction) to data can distort correlations • Noise and range limits in data
- 5. Quantifying strengths of relationships between continuous variables • Correlation measures – Pearson product-moment correlation coefficient (R) – Spearman's rank correlation coefficient () – Kendall rank correlation coefficient (τ) • Quality of fit measures – Coefficient of determination (R2) is the fraction of the variance in Y that is explained by model – Root mean square error (RMSE)
- 6. Preparation of synthetic data sets Kenny & Montanari (2013) JCAMD 27:1-13 DOI Add Gaussian noise (SD=10) to Y
- 7. Correlation inflation by hiding variation See Hopkins, Mason & Overington (2006) Curr Opin Struct Biol 16:127-136 DOI Leeson & Springthorpe (2007) NRDD 6:881-890 DOI Data is naturally binned (X is an integer) and mean value of Y is calculated for each value of X. In some studies, averaged data is only presented graphically and it is left to the reader to judge the strength of the correlation. R = 0.34 R = 0.30 R = 0.31 R = 0.67 R = 0.93 R = 0.996
- 8. r N 1202 R 0.247 ( 95% CI: 0.193 | 0.299) 0.215 ( P < 0.0001) 0.148 ( P < 0.0001) N 8 R 0.972 ( 95% CI: 0.846 | 0.995) 0.970 ( P < 0.0001) 0.909 ( P = 0.0018) Correlation Inflation in Flatland See Lovering, Bikker & Humblet (2009) JMC 52:6752-6756 DOI
- 9. Masking variation with standard error See Gleeson (2008) JMC 51:817-834 DOI Partition by value of X into 4 bins with equal numbers of data points and display 95% confidence interval for mean (green) and mean ± SD (blue) for each bin. R = 0.12 R = 0.29 R = 0.28
- 10. N Bins Degrees of Freedom F P 40 4 3 0.2596 0.8540 400 4 3 12.855 < 0.0001 4000 4 3 115.35 < 0.0001 4000 2 1 270.91 < 0.0001 4000 8 7 50.075 < 0.0001 “In each plot provided, the width of the errors bars and the difference in the mean values of the different categories are indicative of the strength of the relationship between the parameters.” Gleeson (2008) JMC 51:817-834 DOI The error of standard error ANOVA for binned data sets
- 11. Know your data • Assays are typically run in replicate making it possible to estimate assay variance • Every assay has a finite dynamic range and it may not always be obvious what this is for a particular assay • Dynamic range may have been sacrificed for thoughput but this, by itself, does not make the assay bad • We need to be able analyse in-range and out-of- range data within single unified framework – See Lind (2010) QSAR analysis involving assay results which are only known to be greater than, or less than some cut-off limit. Mol Inf 29:845-852 DOI
- 12. Depicting variation with percentile plots This graphical representation of data makes it easy to visualize variation and can be used with mixed in-range and out-of-range data. See Colclough et al (2008) BMCL 16:6611-6616 DOI
- 13. Binning continuous data restricts your options for analysis and places burden of proof on you to show that your conclusions are independent of the binning scheme. Think before you bin! Averaging the binned data was your idea so don’t try blaming me this time!
- 14. Correlation inflation: some stuff to think about • Model continuous data as continuous data – RMSE is most relevant to prediction but you still need R2 – Fitted parameters may provide insight (e.g. solubility is more sensitive than potency to lipophilicity) • When selecting training data think in terms of Design of Experiments (e.g. evenly spaced values of X) • Try to achieve normally distributed Y (e.g. use pIC50 rather than IC50) • Never make statements about the strength of a relationship when you’ve hidden or masked variation in the data (unless you want a starring role in Correlation Inflation 2) • To be meaningful, a measure of the spread of a distribution must be independent of sample size • Reviewers/editors, mercilessly purge manuscripts of statements like, “A negative correlation was observed between X and Y” or “A and B are correlated/linked”
- 15. Ligand efficiency metrics (LEMs) considered harmful • We use LEMs to normalize activity with respect to risk factors such as molecular size and lipophilicity • What do we mean by normalization? • We make assumptions about underlying relationship between activity and risk factor(s) when we define an LEM • LEM as measure of extent to which activity beats a trend? Kenny, Leitão & Montanari (2014) JCAMD 28:699-710 DOI
- 16. Scale activity/affinity by risk factor LE = ΔG/HA Offset activity/affinity by risk factor LipE = pIC50 ClogP Ligand efficiency metrics No reason that dependence of activity on risk factor should be restricted to one of these two linear models
- 17. Use trend actually observed in data for normalization rather than some arbitrarily assumed trend
- 18. There’s a reason why we say standard free energy of binding… DG = DH TDS = RTln(Kd/C0) • Adoption of 1 M as standard concentration is arbitrary • A view of a chemical system that changes with the choice of standard concentration is thermodynamically invalid
- 19. NHA Kd/M C/M (1/NHA) log10(Kd/C) 10 10-3 1 0.30 20 10-6 1 0.30 30 10-9 1 0.30 10 10-3 0.1 0.20 20 10-6 0.1 0.25 30 10-9 0.1 0.27 10 10-3 10 0.40 20 10-6 10 0.35 30 10-9 10 0.33 Effect on LE of changing standard concentration
- 20. Scaling transformation of parallel lines by dividing Y by X (This is how ligand efficiency is calculated) Size dependency of LE is consequence of non-zero intercept
- 21. Affinity plotted against molecular weight for minimal binding elements against various targets in inhibitor deconstruction study showing variation in intercept term Hajduk PJ (2006) J Med Chem 49:6972–6976 DOI Is it valid to combine results from different assays in LE analysis?
- 22. Offsetting transformation of lines with different slope and common intercept by subtracting X from Y (This is how lipophilic efficiency is calculated) Thankfully (hopefully?) nobody has ‘discovered’ lipophilicity-dependent lipophilic efficiency yet
- 23. Linear fit of ΔG for published data set Mortenson & Murray (2011) JCAMD 25:663-667 DOI
- 24. Ligand efficiency, group efficiency and residuals plotted for published data set
- 25. Some more stuff to think about • Normalize activity using trend actually observed in data (this means you have to model the data) • Residuals are invariant with respect to choice in standard concentration • Residuals can be used with other functional forms (e.g. non-linear and multi-linear)