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Early-Enrichment Hit Discovery via Reversible-Work c(t) Estimation in Metadynamics (CTMD).

Venkata Sai Sreyas Adury1, Pratyush Tiwary2,3, Xinyu Gu3

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We introduce c(t)-based metadynamics (CTMD), a fast, physics-based protocol for early enrichment in drug discovery screening. CTMD accurately ranks small-molecule binders, outperforming AI methods and saving significant resources.

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Molecular Dynamics

Background:

  • Virtual screening for small-molecule binders is hindered by false positives from approximate scoring functions and rigid-receptor models.
  • Accurate free energy calculations can address these limitations but are computationally expensive.
  • AI-based co-folding methods offer lower cost but lack consistent early enrichment and can suffer from memorization.

Purpose of the Study:

  • To introduce a physics-based, high-throughput protocol for early enrichment in hit-triaging.
  • To address the limitations of current virtual screening and AI-based co-folding methods.
  • To provide a fast, accurate, and robust alternative for ranking small-molecule binders.

Main Methods:

  • Developed c(t)-based metadynamics (CTMD), a protocol utilizing the nonequilibrium reversible-work estimator c(t).
  • CTMD computes binding stability from short, independent well-tempered metadynamics trajectories without converged free energy calculations.
  • Evaluated CTMD's performance across diverse targets and chemotypes for early enrichment.

Main Results:

  • CTMD provides robust early enrichment, outperforming AI-based co-folding methods like Boltz-2, which showed enrichment proportional to training set similarity.
  • CTMD is fast, transferable with minimal parameter tuning, and resistant to memorization-driven artifacts.
  • AI co-folding methods demonstrated concerning performance even with active site modifications.

Conclusions:

  • CTMD is an immediately deployable, physics-based alternative for early enrichment in drug screening.
  • Its simplicity and effectiveness position it between fast docking/AI methods and expensive free energy calculations.
  • CTMD is expected to save significant financial and human capital in drug discovery campaigns.