Evidence map›Paper›PMID 42828700›Full record

ArticleJournal of computer-aided molecular design2026

Conformer-covariance diagnostics for aqueous solubility.

Sarmad Waleed, Shams Ul Islam, Muhammad Saleem, Mudassar Ahmed

Abstract read
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Article in Journal of computer-aided molecular design, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sarmad WaleedDepartment of Mathematics, COMSATS University Islamabad, Islamabad Campus, Islamabad, 44000, Pakistan. sarmadwaleed@gmail.com.
Shams Ul IslamDepartment of Mathematics, COMSATS University Islamabad, Islamabad Campus, Islamabad, 44000, Pakistan.
Muhammad SaleemDepartment of Mathematics, Sir Syed CASE Institute of Technology, Islamabad, 46000, Pakistan.
Mudassar AhmedDepartment of Space Science, Institute of Space Technology, Islamabad, 44000, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aqueous solubility prediction is central to drug discovery. Most molecular learning pipelines represent each molecule with a single graph and one selected conformer, offering limited leverage to study conformer-ensemble variability. This study presents the Geometric Sheaf Neural Network (GSNN), a sheaf-diffusion architecture whose bond restriction maps are computed from Procrustes-aligned conformer-ensemble covariance tensors and used as structured geometric gates. Its symmetry guarantee is conditional: intermediate vector features transform covariantly when coordinates and restriction maps are rotated consistently, while the scalar prediction is invariant. On a curated subset of the Aqueous Solubility Database (AqSolDB; N = 7188), five leakage-free runs on the paired conformer/spectral test set (n = 1435) give R

Indexed as

Drug DiscoveryNeural Networks, ComputerWaterBoosting Machine Learning AlgorithmsMolecular ConformationPharmaceutical PreparationsSolubilityPharmaceutical PreparationsWaterAqueous solubilityConditional SE(3) equivarianceConformational variabilityGeometric modelingMolecular property predictionSheaf neural networks

Identifiers

PMID42828700

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.