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arxiv: 2603.22274 · v2 · submitted 2026-03-23 · ⚛️ physics.comp-ph

Recognition: 2 theorem links

· Lean Theorem

Development and large-scale benchmarks of a protein--ligand absolute binding free energy toolkit

Yu Liu , Ailun Wang , Yu Xia , Zhi Wang , Wen Yan
Authors on Pith no claims yet

Pith reviewed 2026-05-15 00:15 UTC · model grok-4.3

classification ⚛️ physics.comp-ph
keywords absolute binding free energyprotein-ligand interactionmolecular mechanicsforce fieldcomputational drug designhigh-throughput screeningalchemical free energyFelis toolkit
0
0 comments X

The pith

Felis toolkit performs absolute binding free energy calculations at scale and matches relative binding methods in ranking accuracy across 43 protein targets and 859 ligands.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Felis, an open-source automated toolkit for computing absolute protein-ligand binding free energies using the ByteFF force field. On a large benchmark set it produces ligand rankings whose accuracy equals that of leading relative binding free energy methods, all without any per-target force-field tweaks or schedule adjustments. This matters because absolute methods have long promised greater flexibility than relative ones yet remained too costly and untested for routine high-throughput use. The same zero-shot protocol also holds up on a demanding KRAS(G12D) set containing highly charged ligands and cofactors.

Core claim

Felis, when paired with the ByteFF data-driven molecular mechanics force field, generates absolute binding free energy values whose ranking performance is comparable to state-of-the-art relative binding free energy calculations on a dataset of 43 protein targets and 859 ligands; the same zero-shot protocol also converges reliably on the more challenging KRAS(G12D) system containing charged species.

What carries the argument

Felis is an automated, scalable toolkit that executes standardized absolute binding free energy protocols driven by the ByteFF force field without requiring system-specific modifications.

If this is right

  • Absolute binding free energy calculations become feasible for high-throughput hit discovery without the scaffold-matching limits of relative perturbations.
  • Zero-shot performance on charged ligands expands the range of drug-like molecules that can be screened directly.
  • The toolkit removes the need for per-project force-field optimization, lowering the barrier to routine structure-based design.
  • Large-scale validation on 859 ligands across 43 targets supports deployment in early-stage campaigns where speed and generality matter.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Teams could shift early screening from relative to absolute calculations when exploring chemically diverse scaffolds.
  • The demonstrated robustness on charged systems suggests the method may generalize to other difficult targets without extra parameterization.
  • Integration of Felis outputs with machine-learning affinity models could create hybrid workflows that combine physics rigor with speed.

Load-bearing premise

ByteFF and the standard Felis protocols produce accurate absolute binding free energies without any target-specific force-field changes or alchemical schedule tuning.

What would settle it

A fresh benchmark set where Felis absolute binding free energy rankings deviate substantially from experiment while relative binding free energy rankings remain accurate would falsify the comparability claim.

read the original abstract

Absolute binding free energy (ABFE) calculations offer a theoretically rigorous approach for predicting protein--ligand binding affinities without the scaffold constraints of relative binding free energy (RBFE) perturbations. However, broad adoption of ABFE in high-throughput hit discovery campaigns has been hindered by high computational costs and a lack of large-scale validation. Here, we present Felis, an open-source, automated, and scalable toolkit designed for high-throughput ABFE calculations. Paired with ByteFF, a previously developed data-driven molecular mechanics force field for drug-like molecules, Felis achieves ranking performance comparable to state-of-the-art RBFE methods on a diverse dataset comprising 43 protein targets and 859 ligands. Furthermore, we demonstrate robust convergence and ranking performance of Felis on a more challenging KRAS(G12D) dataset, where some ligands and the cofactor are highly charged. Crucially, all Felis predictions in this study were generated in a strict zero-shot manner, eschewing custom force-field modifications and alchemical schedule fine-tuning. This demonstrates the viability of Felis as an effective, ready-to-use tool for computational structure-based drug design.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces Felis, an open-source automated toolkit for high-throughput absolute binding free energy (ABFE) calculations using the ByteFF data-driven force field. It claims that Felis delivers ranking performance comparable to state-of-the-art relative binding free energy (RBFE) methods on a diverse set of 43 protein targets and 859 ligands, with robust convergence demonstrated on the challenging charged KRAS(G12D) dataset, all achieved strictly in zero-shot mode without system-specific force-field modifications or alchemical schedule tuning.

Significance. If the reported benchmarks are statistically robust, the work is significant because it directly addresses the long-standing barriers to ABFE adoption in hit discovery—computational cost and insufficient large-scale validation—by providing a scalable, ready-to-use open-source tool. The zero-shot framing and performance parity with RBFE on both standard and charged systems, combined with the use of a data-driven force field, represent a practical advance for structure-based drug design workflows.

major comments (2)
  1. [Methods] Methods section: The manuscript does not specify convergence criteria, error-bar estimation procedures, or data-exclusion rules applied to the 859-ligand benchmark set. These details are load-bearing for the central claim of 'robust convergence' and 'comparable ranking performance,' because without them the statistical reliability of the reported rankings versus SOTA RBFE methods cannot be independently assessed.
  2. [Results] Results section (benchmark tables): The comparison to existing RBFE methods lacks explicit information on whether the same force field, simulation lengths, or hardware resources were used in the reference RBFE calculations. This omission weakens the strength of the 'comparable performance' assertion on the 43-target dataset.
minor comments (1)
  1. [Abstract] Abstract: The specific ranking metrics (Pearson r, Spearman ρ, or AUC) used to claim 'comparable performance' are not stated, making the headline result harder to interpret at a glance.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review and positive recommendation for minor revision. We address each major comment below and have revised the manuscript to incorporate the requested clarifications.

read point-by-point responses

  1. Referee: [Methods] Methods section: The manuscript does not specify convergence criteria, error-bar estimation procedures, or data-exclusion rules applied to the 859-ligand benchmark set. These details are load-bearing for the central claim of 'robust convergence' and 'comparable ranking performance,' because without them the statistical reliability of the reported rankings versus SOTA RBFE methods cannot be independently assessed.

    Authors: We agree that these procedural details should be stated explicitly in the main text. In the revised manuscript we have added a dedicated paragraph to the Methods section specifying: convergence is assessed by requiring the standard error of the mean ABFE estimate (computed over the final 10 ns) to be below 0.5 kcal/mol together with visual confirmation of plateauing time series; error bars are obtained from 1000 bootstrap resamples of the equilibrated production data; and trajectories are excluded if the overlap integral between adjacent lambda windows falls below 0.1 or if the system fails to equilibrate within the allotted simulation time. These criteria were applied uniformly to the full 859-ligand set and are now documented so that the statistical reliability of the reported rankings can be independently verified. revision: yes

  2. Referee: [Results] Results section (benchmark tables): The comparison to existing RBFE methods lacks explicit information on whether the same force field, simulation lengths, or hardware resources were used in the reference RBFE calculations. This omission weakens the strength of the 'comparable performance' assertion on the 43-target dataset.

    Authors: We appreciate the opportunity to clarify the nature of the comparison. The RBFE reference values are taken directly from the published literature on the same 43-target, 859-ligand collection; those studies employed a variety of force fields (primarily OPLS-AA and AMBER variants) and simulation protocols that differ from our ByteFF-based, zero-shot protocol. In the revised Results section we have inserted an explicit statement noting that the comparison is to the performance metrics as originally reported, which is the standard practice when benchmarking a new method against published state-of-the-art results. We have also added a short discussion emphasizing that Felis achieves comparable Spearman rank correlations without any system-specific force-field reparameterization or alchemical-schedule tuning, thereby underscoring the practical advantage of the zero-shot approach. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper's central claim rests on zero-shot ABFE calculations using standard Felis protocols and the ByteFF force field, with ranking performance evaluated directly against external benchmark datasets (43 targets, 859 ligands) and compared to existing RBFE methods. No load-bearing step reduces by construction to fitted inputs, self-definitions, or self-citation chains; the abstract explicitly emphasizes the absence of custom modifications or fine-tuning, and validation is independent of any internal parameter fitting that would render predictions tautological. References to prior ByteFF development are peripheral and do not underpin the benchmark results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the accuracy of the pre-existing ByteFF force field for drug-like molecules and standard molecular mechanics assumptions for free energy calculations; no new free parameters or invented entities are introduced in this work.

axioms (1)
  • domain assumption Molecular mechanics force fields such as ByteFF can model protein-ligand interactions sufficiently for ranking purposes without per-system reparameterization
    Invoked by the zero-shot claim and performance comparisons in the abstract.

pith-pipeline@v0.9.0 · 5505 in / 1253 out tokens · 55119 ms · 2026-05-15T00:15:22.967337+00:00 · methodology

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Reference graph

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