OpenNovelty.org

Take Note: Your Molecular Dataset Is Probably Aligned

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
molecular machine learningdatasetsorientation biasequivariance3D orientation
Abstract:

Massive training datasets are fueling the astounding progress in molecular machine learning. Since these datasets are typically generated with computational chemistry codes which do not randomize pose, the resulting geometries are usually not randomly oriented. While cheminformaticians are well aware of this fact, it can be a real pitfall for machine learners entering the burgeoning field of molecular machine learning. We demonstrate that molecular poses in the popular datasets QM9, QMugs and OMol25 are indeed biased. While the fact can easily be overseen by visual inspection alone, we show that a simple classifier can separate original data samples from randomly rotated ones with high accuracy. Second, we validate empirically that neural networks can and do exploit the orientedness in these datasets by successfully training a model on chemical property regression using the molecular orientation as sole input. Third, we present visualizations of all molecular orientations and confirm that chemically similar molecules tend to have similar canonical poses. In summary, we recall and document orientational bias in the prevalent datasets that machine learners should be aware of.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper demonstrates that widely used molecular datasets (QM9, QMugs, OMol25) contain systematic orientational bias, showing that classifiers can distinguish original from randomly rotated samples and that neural networks can predict properties using orientation alone. Within the taxonomy, this work occupies the 'Dataset Alignment Analysis' leaf under 'Orientational Bias Detection and Characterization'. Notably, this leaf contains only the original paper itself—no sibling papers appear in the same category. This isolation suggests the specific focus on detecting non-random molecular orientations in training datasets through classifier-based approaches represents a relatively sparse research direction within the broader field of orientational bias in molecular machine learning.

The taxonomy reveals that neighboring research directions address related but distinct challenges. The sibling leaf 'Bias Mitigation in Data Generation' contains two papers focused on correcting orientational bias during dataset construction, while 'Error Analysis and Bias-Variance Decomposition' examines how such biases contribute to prediction errors. The broader parent branch 'Orientation-Dependent Property Prediction' encompasses work that intentionally exploits molecular orientation for predicting mechanical, thermal, and electronic properties. The paper's positioning suggests it addresses a diagnostic step—identifying that bias exists—rather than mitigation strategies or applications where orientation serves as a meaningful physical feature.

Among thirty candidates examined across three contributions, none were identified as clearly refuting the paper's claims. For the demonstration of orientational bias in datasets, ten candidates were examined with zero refutable matches. Similarly, validation that networks exploit this bias and the visualization method each examined ten candidates without finding overlapping prior work. This absence of refutation within the limited search scope suggests the specific combination of classifier-based bias detection, orientation-as-sole-input validation, and visualization of canonical poses may represent a novel methodological package. However, the search examined only top-K semantic matches plus citation expansion, not an exhaustive literature review.

Based on the limited search scope of thirty candidates, the work appears to occupy a sparsely populated niche within orientational bias research. The taxonomy structure confirms that while orientation-dependent phenomena are studied across multiple domains (materials, biology, imaging), the specific task of diagnosing dataset-level alignment artifacts through statistical and classifier-based methods has minimal direct precedent among examined papers. The analysis cannot rule out relevant work outside the semantic search radius or in adjacent fields not captured by the taxonomy construction process.

Taxonomy

Core-task Taxonomy Papers
19
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: orientational bias detection in molecular machine learning datasets. The field addresses how spatial orientation and alignment artifacts can systematically skew predictions and representations in computational molecular science. The taxonomy reveals several interconnected branches: one focuses on detecting and characterizing these biases in existing datasets, another on predicting properties that inherently depend on molecular orientation, and a third on building representations that explicitly account for directional information. Additional branches examine directional regulatory motifs in biological systems and polymer design challenges where orientation plays a critical role. Works such as Molecular Orientation Energetic Shifts[1] and Error Distribution QM9[11] illustrate how orientation-dependent effects can introduce subtle but consequential errors, while methods like Overcoming Preferred Orientation CryoEM[4] and Anisotropic Particle Potentials[12] demonstrate domain-specific strategies for handling directional dependencies. Recent efforts highlight trade-offs between detecting bias versus exploiting orientation as a meaningful feature. Some studies treat orientation as a nuisance variable requiring correction or alignment, whereas others leverage directional cues for improved prediction of anisotropic properties, as seen in Orientation Dependent Work Function[15] and AlN Diamond Thermal Transport[5]. Molecular Dataset Aligned[0] sits squarely within the bias detection and characterization branch, specifically focusing on dataset alignment analysis. Its emphasis on identifying systematic orientation artifacts in training data contrasts with neighboring work on orientation-aware representation learning, which seeks to encode directional information constructively. This positioning suggests that Molecular Dataset Aligned[0] aims to diagnose and quantify alignment issues before they propagate into model predictions, complementing efforts like Transcription Factor Binding Orientation[3] and Directional Regulatory Motifs[9] that study inherently directional biological phenomena.

Claimed Contributions

Demonstration of orientational bias in popular molecular datasets

The authors empirically show that molecular geometries in widely-used datasets (QM9, QMugs, OMol25) exhibit systematic orientational bias rather than random orientations. They train a simple classifier that distinguishes canonical poses from randomly rotated ones with high accuracy, even under substantial noise and partial rotations.

10 retrieved papers
Validation that neural networks exploit orientational bias

The authors validate that models can exploit orientation bias by successfully regressing molecular properties using only molecular orientation (normalized principal components) as input, achieving performance exceeding what would be expected for randomly oriented data.

10 retrieved papers
Visualization method showing chemically similar molecules share similar orientations

The authors present visualizations of molecular orientations across entire datasets and demonstrate that structurally similar molecules exhibit similar canonical poses, confirming systematic orientation patterns in the data.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Demonstration of orientational bias in popular molecular datasets

The authors empirically show that molecular geometries in widely-used datasets (QM9, QMugs, OMol25) exhibit systematic orientational bias rather than random orientations. They train a simple classifier that distinguishes canonical poses from randomly rotated ones with high accuracy, even under substantial noise and partial rotations.

Contribution

Validation that neural networks exploit orientational bias

The authors validate that models can exploit orientation bias by successfully regressing molecular properties using only molecular orientation (normalized principal components) as input, achieving performance exceeding what would be expected for randomly oriented data.

Contribution

Visualization method showing chemically similar molecules share similar orientations

The authors present visualizations of molecular orientations across entire datasets and demonstrate that structurally similar molecules exhibit similar canonical poses, confirming systematic orientation patterns in the data.

Table of Contents