arXiv AI
Jun 30
A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method
arXiv:2602. 02320v4 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure.
SIGMA: Semantic Identifier Grouping for Molecular Autoregression
arXiv:2603. 25062v2 Announce Type: replace Abstract: Autoregressive molecular models assign probability to molecular serializations even though chemical identity is invariant to serialization.
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arXiv AI
Jun 3
Rethinking Molecular Text Representations for LLMs: An Empirical Study
arXiv:2606. 03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use.
arXiv Machine Learning
Jun 5
MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry
arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.
arXiv Machine Learning
Aug 5
Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language
arXiv:2608. 03855v1 Announce Type: new Abstract: Transformer models have revolutionized natural language processing (NLP), and text-based molecular representations like SMILES have successfully extended these architectures to chemistry.
arXiv Machine Learning
Jun 9
VQ-Atom: Semantic Discretization of Local Atomic Environments for Molecular Representation Learning
arXiv:2605. 16823v2 Announce Type: replace Abstract: Large language models succeed by combining large-scale pretraining with meaningful discrete tokens.
arXiv AI
Aug 12
BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning
arXiv:2604. 06336v2 Announce Type: replace-cross Abstract: Fragment-level representations provide a natural way to capture recurring molecular substructures and reuse their learned representations across molecules.
arXiv Machine Learning
Jun 18
Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment
arXiv:2606. 18703v1 Announce Type: new Abstract: Pretrained biological language models expose per-token probability distributions through masked-token prediction, providing the likelihood interface central to sequence design, variant scoring, and mechanistic interpretation.
arXiv AI
Aug 6
Interpreting GFlowNets for Drug Discovery: What probes can and cannot show
arXiv:2511. 19264v2 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) construct molecules through sequential decisions, but their internal policies remain opaque, limiting adoption in drug discovery, where chemists need interpretable rationales for proposed structures.
arXiv AI
Jun 11
Augmenting Molecular Language Models with Local $n$-gram Memory
arXiv:2606. 12113v1 Announce Type: cross Abstract: Transformer-based language models for SMILES strings suffer from a locality gap: standard character-level tokenization fragments chemically meaningful motifs, forcing models to repeatedly learn local syntax at the expense of long-range dependencies.
arXiv AI
Jul 29
Beyond Predictive Accuracy: A Reliability-Aware Audit of Molecular Representations for Human Olfaction
arXiv:2607. 24848v1 Announce Type: cross Abstract: Pretrained molecular encoders are commonly evaluated through downstream prediction, but predictive accuracy alone does not establish that a learned representation captures reproducible scientific structure, adds information beyond strong conventional baselines, or transfers out of distribution.
arXiv Machine Learning
Jun 11
Probabilistic Contrastive Pretraining for Multi-task ADME Property Prediction
arXiv:2606. 11508v1 Announce Type: new Abstract: Accurate prediction of absorption, distribution, metabolism, and excretion (ADME) properties is critical to drug discovery, but remains challenging because ADME endpoints are noisy, interdependent, and often data-limited.
arXiv Machine Learning
Jul 3
Probing Chemical Language Models: Effects of Pre-training and Fine-tuning
arXiv:2607. 02140v1 Announce Type: new Abstract: Chemical language models (CLMs) are trained with linearized representations such as SMILES, yet it remains unclear which chemically meaningful substructures they encode.