FPSim2, a simple Python3 molecular similarity tool

FPSim2 is a new tool for fast similarity search on big compound datasets (>100 million) being developed at ChEMBL. We started developing it as we needed a Python3 library able to run either in memory or out-of-core fast similarity searches on such dataset sizes.

It's written in Python/Cython and features:

A fast population count algorithm (builtin-popcnt-unrolled) from https://github.com/WojciechMula/sse-popcount using SIMD instructions.
Bounds for sub-linear speed-ups from 10.1021/ci600358f
A compressed file format with optimised read speed based in PyTables and BLOSC
Use of multiple cores in a single search
In memory and on disk search modes
Simple and easy to use

Source code is available on github and Conda packages are also available for either mac or linux. To install it type:

conda install rdkit -c rdkit

conda install fpsim2 -c efelix

Try it with docker (much better performance than binder):

docker pull eloyfelix/fpsim2
docker run -p 9999:9999 eloyfelix/fpsim2
open http://localhost:9999/notebooks/demo.ipynb in a browser

Or if you prefer to try it without installing anything (yet)... Click on the binder image!

Data files used in the demos are also available to download.

I would also like to thank Andrew Dalke and Greg Landrum for their blogs, they have been very useful resources!

Comments

Unknown said…

very cool! how does it compare to chemfp?

24 January 2019 at 20:30

Eloy said…

thanks!

I can only compare it to chemfp 1.5 version, which is the opensource one.

FPSim2 is Python3 compatible, can use multiple threads in a single query and has a fast loading compressed file format.
SMILES, InChI and molfiles can be used as an input for a search, but this also comes with a cost.
FPSim2 can also run searches without loading all FPs in memory at once. This enables Raspberry Pi to run Unichem (>150 million) similarity searches :)

chemfp, as a more mature software has many more extra features like calculating full similarity matrices for example.

FPSim2 still needs some optimisations, features and a benchmark after some of this work is done.

25 January 2019 at 15:43

Miquel Duran-Frigola said…

Very nice tool. I am trying to move from chemfp to using FPSim2... Are you planning to include the calculation of similarity matrices? Thanks!

27 August 2019 at 08:31

Eloy said…

Glad you find it useful! This is a feature that we considered to implement but it won't probably happen before next ChEMBL (26) release.

27 August 2019 at 18:39

Improvements in SureChEMBL's chemistry search and adoption of RDKit

Dear SureChEMBL users, If you frequently rely on our "chemistry search" feature, today brings great news! We’ve recently implemented a major update that makes your search experience faster than ever. What's New? Last week, we upgraded our structure search engine by aligning it with the core code base used in ChEMBL . This update allows SureChEMBL to leverage our FPSim2 Python package , returning results in approximately one second. The similarity search relies on 256-bit RDKit -calculated ECFP4 fingerprints, and a single instance requires approximately 1 GB of RAM to run. SureChEMBL’s FPSim2 file is not currently available for download, but we are considering generating it periodicaly and have created it once for you to try in Google Colab ! For substructure searches, we now also use an RDKit -based solution via SubstructLibrary , which returns results several times faster than our previous implementation. Additionally, structure search results are now sorted by

Improved querying for SureChEMBL

Dear SureChEMBL users, Earlier this year we ran a survey to identify what you, the users, would like to see next in SureChEMBL. Thank you for offering your feedback! This gave us the opportunity to have some interesting discussions both internally and externally. While we can't publicly reveal precisely our plans for the coming months (everything will be delivered at the right time), we can at least say that improving the compound structure extraction quality is a priority. Unfortunately, the change won't happen overnight as reprocessing 167 millions patents takes a while. However, the good news is that the new generation of optical chemical structure recognition shows good performance, even for patent images! We hope we can share our results with you soon. So in the meantime, what are we doing? You may have noticed a few changes on the SureChEMBL main page. No more "Beta" flag since we consider the system to be stable enough (it does not mean that you will never

ChEMBL brings drug bioactivity data to the Protein Data Bank in Europe

In the quest to develop new drugs, understanding the 3D structure of molecules is crucial. Resources like the Protein Data Bank in Europe (PDBe) and the Cambridge Structural Database (CSD) provide these 3D blueprints for many biological molecules. However, researchers also need to know how these molecules interact with their biological target – their bioactivity. ChEMBL is a treasure trove of bioactivity data for countless drug-like molecules. It tells us how strongly a molecule binds to a target, how it affects a biological process, and even how it might be metabolized. But here's the catch: while ChEMBL provides extensive information on a molecule's activity and cross references to other data sources, it doesn't always tell us if a 3D structure is available for a specific drug-target complex. This can be a roadblock for researchers who need that structural information to design effective drugs. Therefore, connecting ChEMBL data with resources like PDBe and CSD is essen

ChEMBL 34 is out!

We are delighted to announce the release of ChEMBL 34, which includes a full update to drug and clinical candidate drug data. This version of the database, prepared on 28/03/2024 contains: 2,431,025 compounds (of which 2,409,270 have mol files) 3,106,257 compound records (non-unique compounds) 20,772,701 activities 1,644,390 assays 15,598 targets 89,892 documents Data can be downloaded from the ChEMBL FTP site: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_34/ Please see ChEMBL_34 release notes for full details of all changes in this release: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_34/chembl_34_release_notes.txt New Data Sources European Medicines Agency (src_id = 66): European Medicines Agency's data correspond to EMA drugs prior to 20 January 2023 (excluding vaccines). 71 out of the 882 newly added EMA drugs are only authorised by EMA, rather than from other regulatory bodies e.g.

ChEMBL 26 Released

We are pleased to announce the release of ChEMBL_26 This version of the database, prepared on 10/01/2020 contains: 2,425,876 compound records 1,950,765 compounds (of which 1,940,733 have mol files) 15,996,368 activities 1,221,311 assays 13,377 targets 76,076 documents You can query the ChEMBL 26 data online via the ChEMBL Interface and you can also download the data from the ChEMBL FTP site . Please see ChEMBL_26 release notes for full details of all changes in this release. Changes since the last release: * Deposited Data Sets: CO-ADD antimicrobial screening data: Two new data sets have been included from the Community for Open Access Drug Discovery (CO-ADD). These data sets are screening of the NIH NCI Natural Product Set III in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296183, DOI = 10.6019/CHEMBL4296183) and screening of the NIH NCI Diversity Set V in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296182, DOI = 10.601

The ChEMBL-og

Search This Blog