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Related Experiment Video

Updated: Jun 13, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

A Bond-Level Sequence Framework for Molecular Representation Learning with Structural Constraints.

Haoran Fan¹, Haoqiang Qi¹, Xin Huang^1,2

¹National Engineering Research Center of Industrial Crystallization Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.

Molecules (Basel, Switzerland)

|June 12, 2026

Summary

This study introduces a novel bond-level sequence framework for molecular modeling, improving drug discovery and materials design by addressing limitations of graph neural networks and Transformers. The model shows promising performance on topology-sensitive tasks.

Keywords:

bond-level representation molecular property prediction self-supervised learning structural degeneracy transformer

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Last Updated: Jun 13, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

Computational chemistry
Machine learning in drug discovery
Materials informatics

Background:

Graph neural networks (GNNs) face bottlenecks like over-squashing.
SMILES-based Transformers lack topological constraints and have vocabulary imbalance.
Existing methods present challenges for accurate molecular property prediction.

Purpose of the Study:

To propose a new molecular representation learning framework using bond-level sequences.
To develop a structure-aware hybrid attention mechanism for molecular modeling.
To offer a parameter-efficient alternative to existing GNN and SMILES-based models.

Main Methods:

Modeling molecules as sequences of directed bond tokens.
Implementing a structure-aware hybrid attention mechanism with topological constraints.
Utilizing multi-scale consistency learning with atom-centric group masking and contrastive losses for pre-training.
Incorporating macro-scale physicochemical descriptors as global attribute bias.

Main Results:

The lightweight model (3.5M parameters) shows dataset-dependent performance on MoleculeNet benchmarks.
Promising results were observed on topology-sensitive tasks, notably MUV.
Ablation studies confirmed the contributions of bond-level connectivity, global attribute bias, and structured masking.

Conclusions:

The proposed bond-level sequence framework offers an effective alternative for molecular modeling.
This approach provides a parameter-efficient option for future molecular learning systems.
The study highlights the potential of bond-level representations for drug discovery and materials design.