MolGramTreeNet: A multimodal molecular property prediction model via grammar tree-constrained molecular representation.

Yekang Zhang, Weichen Liu, Huijuan Zhao, Jinsong Shao, Sanjeevi Pandiyan, Min Yao, Kan Ni, Li Wang

Abstract read

In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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

8 authors.

Yekang ZhangSchool of Information Science and Technology, Nantong University, Nantong, Jiangsu 226019, China.

Weichen LiuNantong Institute of Liver Disease, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong University, Nantong, Jiangsu 226006, China.

Huijuan ZhaoDepartment of Immunology, Medical School, Nantong University, Nantong, Jiangsu 226001, China.

Jinsong ShaoSchool of Information Science and Technology, Nantong University, Nantong, Jiangsu 226019, China.

Sanjeevi PandiyanResearch Center for Intelligent Information Technology, Nantong University, Nantong, Jiangsu 226001, China.

Min YaoDepartment of Immunology, Medical School, Nantong University, Nantong, Jiangsu 226001, China.

Kan NiDepartment of Breast Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu 226001, China.

Li WangResearch Center for Intelligent Information Technology, Nantong University, Nantong, Jiangsu 226001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular property prediction is pivotal for drug discovery. However, existing methods that rely on linear SMILES or 2D graphs often overlook explicit hierarchical structures and chemical grammar constraints. To address this limitation, we propose MolGramTreeNet, a multimodal framework that leverages grammar tree-guided representations. The framework parses SMILES into hierarchical trees using context-free grammar, explicitly encoding chemical rules. The architecture employs a dual-path encoder: a Transformer captures tree-based hierarchical semantics, while a Graph Convolutional Network extracts 2D topological features. These fused features generate comprehensive molecular representations. Experiments conducted across ten datasets demonstrate that MolGramTreeNet outperforms eight baseline models, achieving performance improvements of 2.86%-21.47%. Ablation studies confirm the grammar tree's contribution. This approach significantly enhances both accuracy and interpretability, thereby advancing AI-driven drug discovery.

Indexed as

Computational chemistryMaterials chemistryMolecules

Identifiers

PMID41782819

DOI10.1016/j.isci.2026.114928

PMCPMC12955662

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC

Read underepoch 390

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.