ClipMol: A Molecular Representation Learning Framework for CCS Prediction via SMILES-InChI Dual-View Chemical Language Alignment.

MolPACL: Molecular Property Prediction Based on Prompt Augmentation and Contrastive Learning.

MolPACL is proposed, a prompt-augmentation-based supervised contrastive learning framework that incorporates high-level chemical semantics while preserving molecular identity, and achieves strong performance on both classification and regression tasks while reducing training cost.

PG-MLD: Physics-Guided Molecular Representation Learning via Dynamic 3D Trajectory Distillation

FragBERTa: a fragment-aware molecular representation model with sequential attachment-based fragment embeddings

FragBERTa is introduced, a molecular fragment-aware transformer-based representation language model pretrained using masked language modeling on Sequential Attachment-based Fragment Embedding (SAFE) representations, suggesting that fragment-based string representations offer advantages over atom-level representations for scaffold-sensitive and interaction-driven tasks.

Explicitness in SMILES representation via ExACT: improved tokenization for aqueous solubility prediction

The representation of molecular structure in textual form plays a central role in data-driven cheminformatics, not just for deep learning models that often rely on sequence-based inputs, but also for classic machine learning pipelines which are still relevant in this field. The Simplified Molecular Input Line Entry System (SMILES) representation and its explicit variants differ substantially, yet the implications of this difference for tokenization and downstream property prediction remain insufficiently characterized. This paper systematically investigates the role of explicitness in SMILES representation and its impact on aqueous solubility prediction. Four SMILES variations ranging from basic and canonical to explicit and explicit canonical are reviewed to illustrate how increasing explicit chemical detail alters the available information content. Building on this analysis, a novel tokenization approach termed ExACT (Explicit Atom level Context Tokenization) is introduced, which directly leverages atom level explicitness and local chemical context. In addition, a general strategy for enhancing existing tokenization methods through increased explicitness is proposed and demonstrated by upgrading the current state of the art atom-in-SMILES method to an explicit variant. All approaches are evaluated on the AqSolDB dataset using consistent machine learning pipelines and cross validation protocols. The results show that increased explicitness systematically improves predictive performance across tokenization strategies, yielding higher coefficients of determination, lower mean absolute and root mean squared errors, and reduced variance across folds. The proposed ExACT method achieves the best overall performance, largely outperforming traditional molecular fingerprint representations while remaining statistically comparable to the best competing tokenization methods. Furthermore, the efficiency analysis demonstrated that ExACT offers additional computational advantages, including lower dimensionality and the elimination of the decoding step, resulting in faster feature computation. These findings demonstrate that explicit SMILES representations encode chemically meaningful information that can be effectively exploited through context-aware tokenization, providing an interpretable and computationally efficient alternative representation for molecular property prediction. Scientific contribution This work investigates the role of explicitness in SMILES and its impact on aqueous solubility prediction, proposes a novel ExACT tokenization method and a general strategy for enhancing existing tokenization methods through increased explicitness.

MSMPP: Molecular Property Prediction by Integrating Multi-scale Multi-view information with pretrained 3D molecular large model representation.

Evaluations on eight MoleculeNet datasets show that MSMPP significantly outperforms state-of-the-art models, demonstrating its effectiveness in integrating multi-view intra-molecular features, inter-molecular features and cross-task information.

Path-weighted atom vectors and ChemBERTa fusion for predicting physicochemical properties

Experiments show that PWAV generally improves over classical fingerprint descriptors within learned models and achieves competitive performance relative to established external baselines on several endpoints, positioning PWAV as a competitive and chemically transparent component for hybrid molecular property prediction, rather than as a replacement for domain-specific benchmark systems.