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COFFEE-PRESC: A Fast Prescreening Method Using Compound Retrieval by Pairwise Positional Relationship of

Masayoshi Shimizu1, Satoshi Yoneyama1, Keisuke Yanagisawa1,2

  • 1Department of Computer Science, School of Computing, Institute of Science Tokyo, Meguro-ku, Tokyo 152-8550, Japan.

Journal of Chemical Information and Modeling
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

We developed COFFEE-PRESC, a fast, fragment-based prescreening method for drug discovery. This approach significantly accelerates the evaluation of massive compound libraries, improving efficiency and accuracy in virtual screening.

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Bioinformatics

Background:

  • Protein-ligand docking is crucial for structure-based virtual screening in early drug discovery.
  • Exhaustive screening of billions of compounds is computationally infeasible due to long calculation times per compound.

Purpose of the Study:

  • To introduce COFFEE-PRESC (COmpound Filtering by Fragment pair-based Efficient Evaluation for PRESCreening), a novel, rapid prescreening method.
  • To enable efficient computational screening of ultralarge compound libraries.

Main Methods:

  • COFFEE-PRESC employs a fragment-based approach, docking representative fragments to the target protein.
  • It enumerates favorable protein-fragment poses and pairs them to assess spatial relationships.
  • Compounds with structures similar to fragment pairs are retrieved via similarity searches, ensuring no spatial collisions.

Main Results:

  • COFFEE-PRESC demonstrated a 32-fold increase in speed compared to existing tools like Spresso.
  • The method achieved higher accuracy than Spresso in prescreening ultralarge compound libraries.
  • The fragment set effectively covers a large and diverse chemical space.

Conclusions:

  • COFFEE-PRESC offers a computationally efficient and accurate solution for prescreening ultralarge compound libraries.
  • Its speed and accuracy make it a valuable tool for accelerating drug discovery pipelines.
  • The developed code is publicly available under an MIT license.