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cheML.io: an online database of ML-generated molecules.

Rustam Zhumagambetov1, Daniyar Kazbek1, Mansur Shakipov1

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Researchers created a database of novel virtual molecules using machine learning (ML) algorithms trained on commercial compounds. This open-access resource allows users to search, browse, and generate custom molecules for drug discovery.

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Area of Science:

  • Computational Chemistry
  • Machine Learning
  • Cheminformatics

Background:

  • * Machine learning (ML) algorithms are increasingly used for *de novo* molecule generation.
  • * Existing compound databases may not cover the full chemical space for drug discovery.
  • * There is a need for accessible, diverse virtual molecule libraries.

Purpose of the Study:

  • * To develop an open-access database of virtual molecules generated by ML algorithms.
  • * To provide a user-friendly web interface for accessing and exploring these molecules.
  • * To enable on-demand molecule generation to meet specific user requirements.

Main Methods:

  • * Utilized 10 different ML frameworks for *de novo* molecule generation.
  • * Trained ML algorithms on samples from the ZINC database of commercially available compounds.
  • * Merged generated molecules and their calculated properties into a unified database.
  • * Developed a web interface with a drawing widget for molecule searching and retrieval.

Main Results:

  • * Created a comprehensive database of ML-generated virtual molecules.
  • * Integrated calculated properties for each generated molecule.
  • * Implemented a web interface for intuitive data browsing and searching.
  • * Enabled users to retrieve molecules with desired structures and properties.
  • * Established a mechanism for on-demand molecule generation and database expansion.

Conclusions:

  • * The developed database offers a valuable resource for drug discovery and chemical research.
  • * The ML-driven approach expands chemical diversity beyond existing commercial libraries.
  • * The interactive web interface facilitates efficient exploration and generation of novel molecular entities.