A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It

Champions College Prep; Muhammadjon Tursunbadalov (1); Mustafojon Tursunbadalov (1) ((1) School of Science; Technology; United States)

REVIEW 2 major objections 5 minor 19 references

A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It

T0 review · 2 major / 5 minor · reviewed 2026-07-11 · grok-4.5

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Pith's one-line read On imbalanced virtual-screening data, standard conformal prediction hits its global coverage target while leaving the minority class badly under-covered; class-conditional calibration restores per-class reliability.

desk verdict Solid applied paper: known marginal-vs-Mondrian gap is severe and invisible on imbalanced MoleculeNet tasks, multi-architecture evidence is clean, and the conservation identity predicts the measured shortfalls. read the letter →

arxiv 2607.06605 v1 pith:S7PBS5MZ submitted 2026-07-07 cs.LG stat.ML

Muhammadjon Tursunbadalov (1) , Mustafojon Tursunbadalov (1) ((1) School of Science , Technology , Champions College Prep , United States) This is my paper · ORCID

classification cs.LGstat.ML

keywords conformalpredictionuncertaintyquantificationvirtualscreeningclassimbalanceselectivemolecularpropertyMondrian

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Conformal prediction is sold in drug discovery as an honest reliability guarantee: choose an error rate, and the true label lands in the prediction set at least that often. This paper shows that the standard (marginal) form of that guarantee can be dangerous on the imbalanced datasets screening actually uses. Across four molecular tasks, including a held-out clinical-toxicity endpoint, overall coverage stays near 90 percent while minority coverage collapses—down to 64.8 percent on blood–brain-barrier penetration and 4.2 percent on clinical-trial toxicity. The shortfall is not a model quirk: fingerprint random forests, graph networks, and a frozen chemical language model all reproduce it, and its size tracks how well the base model is already calibrated on rare labels. A simple conservation identity explains why: overall coverage is a prevalence-weighted average, so majority over-coverage is paid for by minority under-coverage, amplified by the imbalance ratio. Aggregate accuracy and overall coverage stay reassuringly high, which is why the failure is easy to miss. Class-conditional (Mondrian) calibration restores minority coverage to target on every dataset for a modest increase in set size, and a cost model shows that abstaining on the right compounds flips a campaign from net-negative to net-positive utility.

What carries the argument

The conservation identity of coverage: minority shortfall equals majority surplus times the imbalance ratio (π_maj/π_min). It follows directly from overall coverage being the prevalence-weighted average of per-class coverages, predicts measured BBBP gaps to within about one point, and orders severity across the four datasets.

What would settle it

On a large held-out imbalanced molecular endpoint run with the same split, score, and target, if marginal minority coverage stayed near 90 percent without majority over-coverage, or if Mondrian failed to restore minority coverage while overall coverage remained near target, the central claim would be false.

Watch

Extended reading notes

Core claim

On imbalanced molecular property datasets, standard marginal conformal prediction satisfies its global coverage guarantee while systematically under-covering the minority class—measured as low as 64.8 percent on BBBP and 4.2 percent on ClinTox at a 90 percent target—because overall coverage is the class-prevalence-weighted average of per-class coverages. The minority shortfall therefore equals the majority surplus amplified by the imbalance ratio. The collapse appears across three unrelated architectures and two nonconformity scores, is hidden by ordinary aggregate metrics, localizes to generic scaffolds present in both classes, and is repaired by class-conditional Mondrian calibration.

Load-bearing premise

The argument treats measured coverages as readable under the usual exchangeability of calibration and test compounds; when that is deliberately broken by scaffold splits the numbers get noisier and Mondrian slightly over-covers.

Editorial extensions

If this is right

On any imbalanced screening task, report coverage per class, not only overall.
Default to Mondrian (class-conditional) conformal calibration; the cost is modestly larger sets on the hard class.
Overall accuracy, overall coverage, and selective accuracy on accepted compounds can all look healthy while minority reliability collapses.
Confident minority errors concentrate on generic scaffolds (e.g., bare benzene and pyridine) that occur in both classes and carry little class signal.
A one-number minority coverage gap can flag the failure before a campaign trusts the sets.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same amplification should appear wherever calibrated abstention meets rare labels—rare-disease diagnosis, fraud, adverse-event prediction—even though the paper only measures chemistry.
A pre-calibration safety check that estimates the minority coverage gap from prevalence and baseline minority calibration alone could warn teams before any conformal wrapper is applied.
Probability recalibration methods that leave a single pooled quantile unchanged will not close the gap; the fix has to act on the calibration partition.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The punchline is simple. Marginal conformal prediction hits its global 90% target on imbalanced screening tasks while minority coverage collapses (64.8% on BBBP, 4.2% on held-out ClinTox). Mondrian restores it. The paper shows this cleanly across RF/ECFP, GCN, and frozen ChemBERTa, two scores (LAC and APS), random and scaffold splits, with multi-seed Wilcoxon tests at floor p-values.

What is actually new is not the theory—they correctly cite Vovk and Angelopoulos & Bates for the marginal/conditional distinction and Mondrian as the fix. The contribution is the systematic measurement on real chemistry, the elementary coverage-conservation identity (minority shortfall = majority surplus × imbalance ratio) that predicts the BBBP gap to within a point and orders severity across four datasets, the scaffold localization to generic benzene/pyridine cores, and a cost model that flips utility from net-negative to net-positive. Aggregate metrics stay healthy throughout, which is why the failure is easy to miss. That package is useful for anyone doing UQ in virtual screening.

Soft spots are real but proportionate. ClinTox minority test sets are tiny (~28 compounds), so percentages are coarse. ChemBERTa is frozen, not fine-tuned. They report only α=0.10 and use illustrative costs (though the sign of the Mondrian advantage is robust across a wide grid). Scaffold splits deliberately break exchangeability and produce noisier numbers, which the paper itself flags. None of this reverses the direction of the multi-architecture, multi-seed evidence. Code and MoleculeNet data are public.

This is for practitioners and method people who care about calibrated abstention under imbalance. It should change the default recommendation (report per-class coverage; use Mondrian on imbalanced tasks). A serious editor should send it to referees; the claims are empirical, the methods standard, and the evidence multi-seed and multi-architecture. I would engage with it.

Referee Report

2 major / 5 minor

Summary. The paper shows that on imbalanced MoleculeNet tasks (BACE, BBBP, Tox21 SR-ARE, held-out ClinTox), standard marginal split conformal prediction meets its global 90% coverage target while substantially under-covering the minority class (down to 64.8% on BBBP and 4.2% on ClinTox). The shortfall is reproduced across a random forest on ECFP fingerprints, a graph convolutional network, and a frozen ChemBERTa model (p < 0.001 each), and across both LAC and APS nonconformity scores. An elementary conservation identity (Eq. 7) equates the minority shortfall to the majority surplus amplified by the imbalance ratio π_maj/π_min; it predicts the BBBP gap to within about one point and orders severity across datasets. Aggregate accuracy, overall coverage, and selective-prediction summaries remain healthy, so the failure is easy to miss. Class-conditional (Mondrian) calibration restores minority coverage to target on every dataset and architecture at a modest cost in set size. Failures concentrate on generic scaffolds (benzene, pyridine); a simple cost model shows that correct abstention flips minority utility from net-negative to net-positive.

Significance. If the result holds, it is a practically important warning for the growing use of conformal prediction in virtual screening and other imbalanced decision settings. The multi-architecture, multi-score, multi-seed design with bootstrap CIs, Wilcoxon tests, a held-out ClinTox task, and scaffold-split robustness is stronger than a typical empirical note. The conservation identity is elementary but useful: it turns a known theoretical gap into a quantitative, pre-checkable prediction of severity from class prevalences alone. Code and fixed seeds are promised, which supports reproducibility. The contribution is correctly framed as empirical and explanatory rather than as a new method; that framing is appropriate and strengthens the paper.

major comments (2)

Section 4.3 / Table 3 (ClinTox row): the near-total collapse (4.2% minority coverage) is load-bearing for the severity claim, yet the minority test set is only ~28 compounds per split. The paper notes coarseness and slight Mondrian overshoot, but the main text still presents 4.2% and MCG ≈ 86 as headline numbers without reporting per-seed ranges or binomial uncertainty. Adding those (or a leave-one-out style sensitivity) would make the strongest claim fully defensible rather than directionally clear but imprecise.
Section 5, Eq. (7) and the BBBP prediction (63.6% predicted vs 64.8% measured): the identity is presented as predicting the gap 'to within one point,' yet the text suppresses finite-sample quantile corrections. Because the paper's explanatory claim rests on this numerical agreement, either restore the finite-sample form of the identity or report the residual after applying the same (n+1) correction used in Eq. (2), so readers can judge how much of the one-point residual is accounting versus approximation.

minor comments (5)

Table 5 reports LAC marginal minority coverage as 64.1% while Table 3/4 and the abstract use 64.8%; the discrepancy is small but should be reconciled or explained (different seeds/rounding).
Figure 1 reliability diagrams and Figure 6 score histograms would benefit from explicit class labels and a shared x-axis scale so the majority-mass vs minority-tail contrast is immediately readable.
Section 3.5 / Eq. (5): the cost model is illustrative and the sensitivity sweep (Figure 4) is welcome; still, state once that B_tp, C_fp, C_lab are not estimated from real campaign data so the utility numbers are not over-interpreted as economics.
Limitations correctly note the single α = 0.10; a one-sentence statement that the conservation identity is α-independent (only the realized surplus depends on α) would clarify that the mechanism is not tied to that choice.
Minor typography: 'Whatmakesthefailuredangerous' and similar run-together words in the Introduction PDF text should be fixed for readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the conservation identity is an elementary algebraic rearrangement of weighted coverage, openly presented as definitional, while the core claims are independent multi-seed empirical measurements.

full rationale

The paper's load-bearing results are empirical: measured minority coverage under marginal conformal prediction falls well below the 90% target (64.8% BBBP, 38.9% Tox21, 4.2% ClinTox) while overall coverage hits target, the collapse reproduces across three architectures and two scores at p<0.001, Mondrian restores it, and aggregate metrics hide it. These are obtained from repeated random/scaffold splits on public MoleculeNet data with fixed seeds; nothing is fitted then re-predicted. The sole candidate for circularity is the Section 5 conservation identity (Eq. 7), which rearranges the definitional identity overall coverage = π_maj cov_maj + π_min cov_min into minority shortfall = (π_maj/π_min) imes majority surplus. The paper itself states that it "follows directly from the definition of weighted coverage" and is "not a new theoretical result but as an explanatory tool," and the numerical check (predicted 63.6% vs measured 64.8%) is simply consistency of the measured coverages under the finite-sample overall of 90.2%. Mondrian is the standard literature remedy (Vovk), MCG/CII are trivial derived summaries, and the cost model plugs measured rates into an illustrative utility. No self-citation is load-bearing, no uniqueness theorem is imported, no free parameter is fitted and renamed a prediction, and no ansatz is smuggled. The derivation chain is therefore self-contained and non-circular.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

Empirical ML paper whose central claims rest on standard conformal theory (exchangeability, split conformal quantiles, Mondrian per-class quantiles) plus ordinary experimental choices. No deep free parameters are fitted to produce the main coverage gaps; the conservation identity is definitional once prevalences and per-class coverages are observed. Cost-model constants and α=0.10 are illustrative or conventional and are sensitivity-checked or fixed by the field.

free parameters (2)

target error rate α = 0.10
Fixed at the conventional 0.10 throughout; no sweep. Central qualitative claim is expected to hold at other α but is untested.
decision-cost constants (B_tp, C_fp=C_fn, C_lab) = 1 / 5 / 0.5
Chosen illustratively (1, 5, 0.5); sensitivity grid shows sign of Mondrian advantage is robust, so not load-bearing for the qualitative conclusion.

assumptions (4)

standard math Exchangeability of calibration and test points yields the finite-sample marginal coverage guarantee for split conformal prediction.
Invoked throughout Methods 3.3 and Results; standard Vovk/Angelopoulos theory. Scaffold splits deliberately violate it as a robustness check.
standard math Class-conditional (Mondrian) quantiles deliver per-class coverage ≥1−α when each class has its own calibration scores.
Standard Mondrian taxonomy (Vovk 2013); used as the fix in Eq. (4).
domain assumption Base models handle the majority class better than the minority (lower nonconformity scores), which is the normal situation under imbalance.
Stated in Section 5 and visible in Figure 6; required for the majority surplus that the conservation identity amplifies.
domain assumption MoleculeNet binary tasks (BBBP, BACE, Tox21 SR-ARE, ClinTox CT-TOX) after RDKit parsing are representative of imbalanced virtual-screening problems.
Dataset choice in Section 3.1; ClinTox treated as held-out check within the same benchmark family.

invented entities (1)

minority coverage gap (MCG) and coverage imbalance index (CII) independent evidence
purpose: One-number diagnostics to make the invisible per-class failure easy to report.
Simple derived summaries of already-measured coverages; not new physical or statistical objects. independent_evidence is true because they are directly computable from any conformal run.

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Cite this review

Pith. "Pith review of A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It." pith.science (2026). https://pith.science/paper/S7PBS5MZ

@misc{pith2026260706605,
  author       = {Pith},
  title        = {Pith review of: A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S7PBS5MZ}},
  note         = {Machine review of arXiv:2607.06605}
}

read the original abstract

Conformal prediction is being adopted in drug discovery to put an honest number on model reliability: pick an error rate alpha, and the method returns prediction sets containing the true label with probability at least 1 - alpha. We show this guarantee can be dangerous on imbalanced datasets. Across four datasets, standard (marginal) conformal prediction hits its global 90% coverage target while leaving the minority class badly exposed: realized minority coverage falls to 64.8% on blood-brain-barrier penetration and to 4.2% on clinical-trial toxicity, where the rare class is nearly abandoned. The failure is not tied to one model: a random forest, a graph network, and a frozen chemical language model all reproduce it (p < 0.001 in every case), with severity tracking baseline calibration on rare labels rather than architecture. A conservation identity explains the effect: the minority's shortfall equals the majority's surplus amplified by the imbalance ratio, predicting the measured gap to within one point and ordering severity across datasets. The failure survives realistic scaffold splits and a second conformal score, while aggregate accuracy and overall coverage stay reassuringly high, which is exactly why it is easy to miss. Class-conditional (Mondrian) conformal prediction closes the gap on every dataset, restoring minority coverage to target for a modest increase in prediction-set size. We localize the failures to generic molecular scaffolds - plain benzene and pyridine cores occurring in both classes - propose a one-number diagnostic, and show with a cost model that abstaining on affected compounds flips a screening campaign from net-negative to net-positive utility. Our contribution is demonstrating on real chemistry how severe and invisible this known conformal-theory gap becomes under imbalance, and laying out a practical protocol restoring per-class reliability.

Figures

Figures reproduced from arXiv: 2607.06605 by the authors.

**Figure 1.** Reliability diagrams for three base classifiers on BBBP (out-of-fold). Deviation from the diagonal is mild; expected calibration error stays between 0.04 and 0.10. Ordinary calibration being acceptable is what makes the conformal coverage failure in [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗

**Figure 2.** Per-class coverage on BBBP at a 90% target (random forest, ten seeds). Marginal calibration over-covers the majority and leaves the minority at 64.8%. Mondrian calibration brings both classes to target. method buys its global guarantee by over-covering the easy class and under-covering the hard one ( [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Marginal minority-class coverage against a 90% target (dashed) on three datasets ordered by imbalance. The shortfall deepens from BACE to BBBP to Tox21; Mondrian calibration returns every dataset to target [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Sensitivity of the Mondrian utility advantage ∆U = Umond − Umarg to the cost assumptions, on BBBP minority decisions (n = 1000). ∆U is positive across every combination of error cost and lab-assay cost, so class-conditional calibration is the better decision rule regar…

**Figure 5.** Figure 5: The three Bemis–Murcko scaffolds with the most confident minority-class errors under marginal calibration on BBBP. The worst blind spots are the most generic cores (benzene, pyridine), which appear in both classes and carry little class signal. result but as an explana…

**Figure 6.** Figure 6: Calibration nonconformity scores on BBBP, split by class. The majority (penetrant) scores concentrate near zero; the minority (non-penetrant) scores spread to high values. The single marginal threshold is pulled toward the majority mass and slices through the minority…

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Reference graph

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Pith tools

Reviewed July 11, 2026 · model on record in the stance chip above.

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work page Pith review arXiv 2023

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Least Ambiguous Set-Valued Classifiers With Bounded Error Levels.Journal of the American Statistical Association2019, 114, 223–234

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