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Updated: Jul 12, 2026

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
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Published on: April 13, 2022

Navigating chemical-linguistic sharing space with heterogeneous molecular encoding.

Liuzhenghao Lv1,2,3, Hao Li2,3, Yu Wang1,2

  • 1Beijing Key Laboratory of Brain-inspired Spiking Large Models, School of Computer Science, Peking University, Beijing, China.

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|July 10, 2026
PubMed
Summary

This study introduces heterogeneous molecular encoding to enhance chemical language models, enabling a multi-modal understanding of molecules for improved chemical design and comprehension.

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Published on: January 10, 2017

Area of Science:

  • Computational Chemistry
  • Artificial Intelligence in Chemistry
  • Drug Discovery

Background:

  • Chemical language models (CLMs) traditionally use linear molecular representations (e.g., SMILES strings).
  • This linear approach creates a semantic gap, limiting CLMs' ability to capture the full complexity of molecular structures and bridge natural language with chemistry.
  • Existing CLMs struggle with comprehensive molecular understanding and design tasks.

Purpose of the Study:

  • To develop a novel framework for chemical language models that integrates multi-modal molecular information.
  • To enable a more comprehensive, multi-modal understanding of molecules beyond linear representations.
  • To improve bidirectional navigation between chemical language and molecular structures for design and comprehension tasks.

Main Methods:

  • Heterogeneous molecular encoding: integrating 1D sequences, 2D topology, 3D geometry, and molecular fragments.
  • Development of a query-based module for converting heterogeneous structural information into a unified representation.
  • Introduction of a chain-of-fragment mechanism for hierarchical molecular generation.
  • Construction of a million-scale dataset for multi-objective molecular design.

Main Results:

  • The proposed framework enables chemical language models to achieve a comprehensive, multi-modal understanding of molecules.
  • Demonstrated bidirectional navigation of the chemical-linguistic space.
  • Achieved consistent performance improvements in molecular comprehension and design tasks compared to existing methods.
  • Successfully generated a large-scale dataset to facilitate further research.

Conclusions:

  • Heterogeneous molecular encoding significantly enhances the capabilities of chemical language models.
  • The framework provides a unified approach to integrate diverse molecular information for advanced AI applications in chemistry.
  • This work opens new avenues for AI-driven molecular design and discovery.