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Area of Science:

  • Computational chemistry
  • Artificial intelligence in chemistry

Background:

  • Chemical notation systems like SMILES and IUPAC are crucial for representing molecular structures.
  • Rule-based systems for translating between these notations can be complex and difficult to maintain.

Purpose of the Study:

  • To develop and evaluate a Transformer-based artificial neural network for translating between SMILES and IUPAC chemical notations.
  • To demonstrate the feasibility of using neural networks for chemical notation translation in production environments.

Main Methods:

  • Development of a Transformer-based neural network architecture.
  • Training and evaluation of models for both SMILES to IUPAC (Struct2IUPAC) and IUPAC to SMILES (IUPAC2Struct) translation.
  • Comparison of performance against existing rule-based solutions.

Main Results:

  • The developed neural network models achieve performance comparable to established rule-based solutions.
  • The models demonstrate high accuracy, computational speed, and robustness, making them suitable for production.
  • A showcase highlights the model's ability to facilitate rapid development while maintaining accuracy.

Conclusions:

  • Neural-based solutions can effectively translate between chemical notations like SMILES and IUPAC.
  • These findings suggest that artificial neural networks can replace complex rule-based systems, potentially reducing development costs and effort.
  • The study encourages the adoption of neural approaches for chemical informatics tasks.