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. 2014 Oct 27;54(10):2996-3004.
doi: 10.1021/ci500445u. Epub 2014 Oct 7.

Computational prediction and validation of an expert's evaluation of chemical probes

Nadia K Litterman  1 , Christopher A Lipinski, Barry A Bunin, Sean Ekins
Affiliations

Computational prediction and validation of an expert's evaluation of chemical probes

Nadia K Litterman et al. J Chem Inf Model. .

Abstract

In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count, and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods, we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date.

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Figures

Figure 1
Figure 1
Relative contribution of each criteria for considering compounds as undersirable.
Figure 2
Figure 2. Visualizing chemical property space of desirable and undesirable probes
A. Principal component analysis (PCA) of the 307 desirable (yellow) and undesirable (blue) NIH probes. 76.6% of the variance was explained with three principal components. B. PCA of the NIH chemical probes (yellow) and 2320 Microsource spectrum compounds (blue). 81.9% of variance was explained by 3 principal components.
Figure 3
Figure 3. Comparison of the expert's evaluation of the NIH chemical probes with PAINS and BadApple Filters
Desirable NIH chemical probes are less likely to be filtered by PAINS or BadApple as promiscuous than those scored as undesirable. (Fisher's exact test, p>0.0001 for PAINS and p=0.04 for BadApple).
Figure 4
Figure 4. Comparison of desirability scores with Bayesian learning predicted scores for each test set, QED, BadApple, and ligand efficiency metrics
The colors on the heat map correspond to the value of the indicated metric for each probe, listed vertically. The scale was normalized internally with green corresponding to the optimal condition within each metric.
See this image and copyright information in PMC

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