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arxiv: 2605.04265 · v1 · submitted 2026-05-05 · 🧬 q-bio.BM · q-bio.QM

Recognition: unknown

Benchmarking open-source tools for in silico antiviral drug discovery

Daniel C. Elton, Preston W. Estep

Pith reviewed 2026-05-08 17:00 UTC · model grok-4.3

classification 🧬 q-bio.BM q-bio.QM
keywords antiviral drug discoverybinding affinity predictionmachine learning toolsmolecular dockingbenchmarkingDrugFormDTABoltz-2GNINA
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The pith

Benchmarking shows Boltz-2 and fine-tuned DrugFormDTA provide the strongest predictions of antiviral binding affinities among 15 open-source tools.

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

The paper argues that computational tools can speed up antiviral discovery for outbreaks where approved drugs are missing for most viral families. It curates a dataset of 43,005 viral protein-ligand measurements from BindingDB and other sources, finding that 31 percent of entries needed careful splitting of polyprotein sequences to be usable for machine learning. The authors then benchmarked 15 open-source tools on a test set of 853 antiviral compounds across 16 protein targets from 10 virus species. Results identify Boltz-2 and DrugFormDTA as top machine-learning performers and GNINA as the best docking method, with fine-tuning raising DrugFormDTA's correlation from 0.5 to 0.7. This supplies a practical starting point and a public drug library for faster repurposing and combination design.

Core claim

After curating 43,005 viral protein-ligand binding measurements and splitting polyprotein sequences where needed, the authors benchmarked 15 open-source binding affinity tools on 853 antiviral compounds spanning 16 targets from 10 virus species. Boltz-2 and DrugFormDTA ranked highest among machine-learning approaches while GNINA led among docking tools, with clear performance differences across individual viral proteins. Fine-tuning DrugFormDTA on the cleaned antiviral dataset raised its Pearson correlation from 0.5 to 0.7.

What carries the argument

A custom-curated dataset of 43,005 binding measurements used to fine-tune DrugFormDTA and evaluate 15 tools on 853 antiviral compounds across 16 viral protein targets.

Load-bearing premise

The curated dataset of 43,005 measurements accurately reflects true binding affinities after polyprotein splitting, and the 853-compound test set has no leakage or biases that would inflate the reported correlations.

What would settle it

Running the top tools on a fresh independent set of measured antiviral binding affinities for the same viral proteins and verifying whether correlations near 0.7 still hold.

read the original abstract

Antivirals are uniquely positioned to be deployed quickly during a new outbreak, especially when repurposed from approved drugs. Yet there are no FDA-approved antivirals for the majority of viral families with pandemic potential. Here we lay out the case for investing in technologies and techniques for antiviral drug discovery and designing antiviral combinations. We present a survey of open source datasets and computational tools for in silico antiviral drug discovery, with a particular focus on the latest AI-based systems and docking tools. We then present our custom dataset of 43,005 viral protein-ligand binding measurements that we curated from BindingDB and other sources. Importantly, we found that 31% of viral protein binding data in BindingDB required polyprotein sequences to be carefully split before the data were suitable for training or testing ML models. Using our custom dataset we fine-tuned the DrugFormDTA binding affinity prediction model (Khokhlov et al. 2025). We then benchmarked 15 open-source binding affinity prediction tools on a custom test set of 853 antiviral compounds spread across 16 different protein targets from 10 virus species. Models tested include Boltz-2, GNINA, FlowDock, Interformer, AutoDock-GPU, and others. We found that Boltz-2 and DrugFormDTA ranked highest overall among ML-based approaches, and GNINA did best among docking approaches, with notable variance across specific viral proteins. Fine-tuning DrugFormDTA on our custom cleaned antiviral dataset boosted performance from $r=0.5$ to $r=0.7$. As part of this work we also compiled a library of approved drugs and a comprehensive list of investigational and approved antiviral drugs that can be viewed at https://antivirals-database.radvac.org. Together, this work provides a foundation for future work towards new tools and platforms for rapid drug repurposing and rapid design of antiviral combinations.

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

3 major / 1 minor

Summary. The manuscript surveys open-source datasets and computational tools for in silico antiviral drug discovery. It introduces a curated dataset of 43,005 viral protein-ligand binding measurements from BindingDB and other sources, noting that 31% required splitting of polyprotein sequences. The authors fine-tune the DrugFormDTA model on this dataset and benchmark 15 open-source tools, including ML-based (Boltz-2, DrugFormDTA) and docking (GNINA) approaches, on a test set of 853 antiviral compounds across 16 protein targets from 10 virus species. They report that Boltz-2 and DrugFormDTA perform best among ML methods, GNINA among docking, with fine-tuning improving Pearson r from 0.5 to 0.7, and provide additional resources like a library of approved antivirals.

Significance. If the reported performance improvements and rankings hold under rigorous validation, this work provides a valuable benchmark and curated resources for antiviral drug discovery, particularly useful for rapid repurposing during outbreaks. The custom dataset and fine-tuning demonstration highlight the potential of domain-specific data curation to enhance ML models for binding affinity prediction.

major comments (3)
  1. [Dataset curation and fine-tuning description] The description of the custom dataset curation and fine-tuning (abstract and methods) lacks any explicit statement of the train-test split protocol, including compound ID or SMILES deduplication steps to ensure the 853-compound test set is strictly disjoint from the 43,005 training measurements. This detail is load-bearing for the central claim that fine-tuning boosts Pearson r from 0.5 to 0.7.
  2. [Dataset curation section] The paper notes that 31% of BindingDB viral entries required polyprotein splitting before use, but provides no validation, controls, or discussion confirming that the resulting fragment labels still reflect true experimental binding affinities rather than sequence artifacts (abstract and dataset section).
  3. [Results and benchmarking section] No error bars, confidence intervals, or statistical tests are reported for the correlation values or tool rankings across the 16 viral targets, weakening the strength of the performance claims and variance observations.
minor comments (1)
  1. [Abstract] The abstract and text could more clearly distinguish between the full curated set used for fine-tuning and any held-out validation during fine-tuning itself.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their detailed and constructive review. The comments identify important areas for clarification and strengthening of the manuscript. We address each major comment below and will revise the manuscript accordingly to improve transparency and rigor.

read point-by-point responses

  1. Referee: The description of the custom dataset curation and fine-tuning (abstract and methods) lacks any explicit statement of the train-test split protocol, including compound ID or SMILES deduplication steps to ensure the 853-compound test set is strictly disjoint from the 43,005 training measurements. This detail is load-bearing for the central claim that fine-tuning boosts Pearson r from 0.5 to 0.7.

    Authors: We agree that an explicit description of the split protocol is essential for validating the fine-tuning results. In the revised manuscript, we will add a dedicated subsection in Methods detailing the train-test split. The 853-compound test set was constructed by first identifying all antiviral compounds from the curated sources, then removing any entries sharing identical compound IDs or canonical SMILES strings with the remaining 43,005 training measurements. We will include the exact deduplication procedure, the number of compounds removed during this step, and a summary table showing overlap statistics before and after filtering. revision: yes

  2. Referee: The paper notes that 31% of BindingDB viral entries required polyprotein splitting before use, but provides no validation, controls, or discussion confirming that the resulting fragment labels still reflect true experimental binding affinities rather than sequence artifacts (abstract and dataset section).

    Authors: We acknowledge the need for greater transparency on this curation step. In the revised dataset section, we will expand the description of the polyprotein splitting procedure, including the criteria used to identify cleavage sites and the rationale that binding measurements in BindingDB are typically reported against specific domains or fragments. We will add a limitations paragraph discussing the possibility of sequence artifacts and note that, where possible, we cross-checked a subset of split entries against literature-reported affinities for the isolated domains. Full validation against orthogonal experimental data is beyond the scope of the current work but will be flagged as an area for future improvement. revision: partial

  3. Referee: No error bars, confidence intervals, or statistical tests are reported for the correlation values or tool rankings across the 16 viral targets, weakening the strength of the performance claims and variance observations.

    Authors: We agree that quantitative uncertainty estimates and statistical comparisons would strengthen the benchmarking results. In the revised Results section, we will recompute all Pearson r values with bootstrap-derived 95% confidence intervals (1,000 resamples per target) and report them alongside the point estimates. We will also add pairwise statistical tests (e.g., Steiger’s test for dependent correlations or Wilcoxon signed-rank tests on per-target performance) to evaluate whether observed differences between tools are significant, with p-values corrected for multiple comparisons. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmarking on external held-out data

full rationale

The paper performs direct empirical benchmarking of existing tools (Boltz-2, GNINA, DrugFormDTA etc.) against BindingDB-derived measurements and a stated custom test set of 853 compounds. Fine-tuning DrugFormDTA is an explicit training step on the 43k set followed by separate evaluation; no equations, predictions, or uniqueness claims reduce by construction to author-defined quantities or self-citations. The central results are falsifiable correlations on external data, not self-referential derivations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the reliability of public binding databases and the correctness of the authors' polyprotein-splitting procedure. No free parameters are introduced beyond standard ML training; no new physical entities are postulated.

axioms (1)
  • domain assumption Binding measurements in BindingDB and other sources accurately reflect experimental affinities after polyprotein splitting
    This underpins the entire curated dataset of 43,005 measurements and the subsequent fine-tuning and benchmarking.

pith-pipeline@v0.9.0 · 5655 in / 1419 out tokens · 47098 ms · 2026-05-08T17:00:40.496358+00:00 · methodology

discussion (0)

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