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The chemfp project.

Andrew Dalke1

  • 1Andrew Dalke Scientific AB, Trollhättan, Sweden. dalke@dalkescientific.com.

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|January 12, 2021
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Summary
This summary is machine-generated.

The chemfp project developed fast cheminformatics fingerprint similarity search tools, including the BitBound algorithm. While open-source funding models were challenging, chemfp offers a valuable benchmark for evaluating search performance.

Keywords:
FOSSFormatHigh-performanceMolecular fingerprintsOpen sourcePerformance benchmarkSimilarity searchingTanimoto

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Area of Science:

  • Computational chemistry and cheminformatics
  • Software engineering and algorithm development

Background:

  • The chemfp project aimed to advance text-based exchange formats for dense binary cheminformatics fingerprints.
  • It sought to establish a high-performance BitBound algorithm implementation for benchmarking similarity search.
  • The project explored sustainable funding models for pure open-source software in cheminformatics.

Purpose of the Study:

  • To promote the FPS format and develop a high-performance BitBound algorithm for cheminformatics fingerprint similarity search.
  • To establish a benchmark for evaluating new similarity search implementations using open-source tools.
  • To share lessons learned from funding a pure open-source software project through commercial sales.

Main Methods:

  • Developed and benchmarked the BitBound algorithm for efficient similarity searching of large chemical fingerprint datasets.
  • Evaluated the performance of a commercial chemfp version capable of processing 130 million 1024-bit fingerprint Tanimotos per second.
  • Analyzed the impact of memory bandwidth and latency on single-threaded search performance, and optimized multi-threaded search using popcount sorting.

Main Results:

  • The FPS format had limited success but influenced the FPB format; chemfp provides a reference baseline for similarity search tools.
  • The commercial chemfp version achieves high throughput, testing 130 million Tanimotos/sec on a single core.
  • A k=1000 nearest-neighbor search on 1.8 million ChEMBL fingerprints averages 27 ms/query, and on 970 million PubChem fingerprints averages 220 ms/query.
  • Optimized multi-threaded search using popcount sorting enabled N×N similarity matrix construction for 1 million fingerprints in approximately 30 minutes.
  • Single-threaded search is memory bandwidth bound, challenging previous assumptions of CPU-bound limitations.

Conclusions:

  • Chemfp represents one of the fastest CPU-based similarity search implementations, highlighting the importance of memory bandwidth.
  • The project's exploration of open-source software funding via commercial sales faced sustainability challenges.
  • Lessons learned from the chemfp project offer guidance for future open-source software development and funding in cheminformatics.