So much to do, so little time

Trying to squeeze sense out of chemical data

Archive for the ‘pains’ tag

Metabolite Similarity & Dirty Compounds

with one comment

Edit 10/9/14 – Updated statistics for the 1024 bit fingerprints

There’s been some discussion about a paper by O’Hagan et al that have proposed a Rule of 0.5 that states that 90% of approved drugs exhibit a Tanimoto similarity > 0.5 to one or more human metabolites. Their analysis is based on metabolites listed in Recon2, a reconstruction of the human metabolic network. The idea makes sense and there’s an in depth discussion at In the Pipeline.

Given the authors’ claim that

a successful drug is likely to lie within a Tanimoto distance of 0.5 of a known human metabolite. While this does not mean, of course, that a molecule obeying the rule is likely to become a marketed drug for humans, it does mean that a molecule that fails to obey the rule is statistically most unlikely to do so

I was interested in seeing how this rule of thumb holds up when faced with compounds that are not supposed to make it through the drug development pipeline. Since PAINS appear to be the structural filter du jour, I decided to look at compounds that failed the PAINS filter. I worked with the 10,000 compounds included in Saubern et al. Simon Saubern provided me the set of 861 compounds that failed the PAINS filters, allowing me to extract the set of compounds that passed (9139)

Chris Swain was kind enough to extract the compound entries from the Matlab dump provided by O’Hagan et al. This file contained InChI representations for a subset of the entries. I extracted the 2980 valid InChI strings and converted them to SMILES using ChemAxon molconvert 6.0.5. The processed data (metabolite name, InChI and SMILES) are available here. However, after deduplication, there were 1335 unique metabolites

Now, O’Hagan et al for some reason, used the 166 bit MACCS keys, but hashed them to 1024 bits. Usually, when using a keyed fingerprint, the goal is to retain the correspondence between bit position and substructure. The hashing step results in a loss of such correspondence. So it’s a bit surprising that they didn’t use some sort of path (Daylight) or environment (ECFPn) based fingerprint. Since I didn’t know how they hashed the MACCS keys, I calculated 166 bit MACCS keys and 1024 bt ECFP6 and extended path fingerprints using the CDK (via rcdk). Then for each compound in the PAINS pass or fail set, I computed the similarity to each of the 1335 metabolites and identified the maximum similarity (termed NMTS in the paper) and then plotted the distribution of these NMTS values between the PAINS pass and fail sets.


First, the similarity cutoff proposed by the authors is obiously dependent on the fingerprint. So while the bulk of the 166 bit MACCS similarities are > 0.5, this is not really meaningful. A more relevant comparison is to 1024 bit fingerprints – both are hashed, so should be somewhat comparable to the authors choice of hashed MACCS keys.

The path fingerprints lead to an NMTS of ~ 0.25 for both PAINS pass and fail sets and the ECFP6 leads to an NMTS of ~ 0.18 for both sets. Though the difference in medians between the pass and fail sets for the path fingerprint is statistically significant (p = 1.498e-05, Wilcoxon test), the difference itself is very small: 0.005. (For the circular fingerprint there is no statistically significant difference). However, the PAINS pass set does contain more outliers with values > 0.5. In that sense the proposed rule does separate the two groups. Of the top of my head I don’t know whether the WEHI screening deck that was the source of the 10,000 compounds was designed to be drug-like. At the same time all this might be saying is there is no relationship between metabolite-likenes and PAINS-likeness.

It’d be interesting to see how this type of analysis holds up with other well known filter rules (REOS, Lilly etc). A related thing to look at would be to see how druglikeness scores compare with NMTS values.

Code and data are available in this repository

Written by Rajarshi Guha

October 7th, 2014 at 5:47 pm

Visualizing PAINS SMARTS

without comments

A few days ago I had made available a SMARTS version of the PAINS substructural filters, that were converted using CACTVS from the original SLN patterns. I had mentioned that the SMARTSViewer application was a handy way to visualize the complex SMARTS patterns. Matthias Rarey let me know that his student had converted all the SMARTS to SMARTSViewer depictions and made them available as a PDF. Given the complexity of many of the PAINS patterns, these depictions are a very nice way to get a quick idea of what is supposed to match.

(FWIW, the SMARTS don’t reproduce the matches obtained using the original SLN’s – but hopefully the depictions will help anybody who’d like to try and fix the SMARTS).

Written by Rajarshi Guha

December 2nd, 2010 at 1:18 am

Posted in software,cheminformatics

Tagged with , ,

PAINS Substructure Filters as SMARTS

with 2 comments

Sometime back Baell et al published an interesting paper describing a set of substructure filters to identify compounds that are promiscuous in high throughput biochemical screens. They termed these compounds Pan Assay Interference Compounds or PAINS. There are a variety of functional groups that are known to be problematic in HTS assays. The reasons for exclusion of molecules with these and other groups range from reactivity towards proteins to poor developmental potential or known toxicity. Derek Lowe has a nice summary of the paper.

The paper published the substructure filters as a collection of Sybyl Line Notation (SLN) patterns. Unfortunately, without access to Sybyl, it’s difficult to reuse the published patterns. Having them in  SMARTS form would allow one to use them with many more (open source or commercial) tools. Luckily, Wolf Ihlenfeldt came to the rescue and provide me access to a version of the CACTVS toolkit that was able to convert the SLN patterns to SMARTS.

There are three files, p_l15, p_l150 and p_m150 corresponding to tables S8, S7 and S6 from the supplementary information. The first column is the pattern and the second column is the name for that pattern taken from the original SLN files. While all patterns were converted to SMARTS, the conversion process is not perfect as I have not been able to reproduce (using the OEChem toolkit with the Tripos aromaticity model) all the hits that were obtained using the original SLN patterns.

(As a side note, the SMARTSViewer is a really handy tool to visualize a SMARTS pattern – which is great since many of the PAINS patterns are very complex)

Written by Rajarshi Guha

November 14th, 2010 at 8:41 pm