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NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer.

Xiaojuan Shen1, Tao Zeng1, Nianhang Chen1

  • 1School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.

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|April 27, 2024
PubMed
Summary
This summary is machine-generated.

We developed NIMO, a computational tool using transformer neural networks, to expand the chemical diversity of natural products for drug discovery. NIMO excels at generating novel molecules and optimizing existing structures.

Keywords:
deep learningfragmentationmolecular generationnatural productstransformer

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Area of Science:

  • Medicinal Chemistry
  • Computational Drug Discovery
  • Artificial Intelligence in Drug Design

Background:

  • Natural products (NPs) are crucial for drug discovery due to their diverse biological activities and structural complexity.
  • Expanding the chemical space of NPs is essential to identify novel therapeutics.
  • Current methods for NP exploration face limitations in accessing novel chemical scaffolds.

Purpose of the Study:

  • To introduce NIMO, a novel computational tool for generating and optimizing natural product-like molecules.
  • To leverage transformer neural networks and motif extraction for enhanced molecular generation.
  • To explore NIMO's capabilities in various molecule generation tasks, including structure-guided and activity-oriented design.

Main Methods:

  • Development of NIMO, a computational tool based on transformer neural networks.
  • Implementation of two tailor-made motif extraction methods to convert molecular graphs into semantic motif sequences.
  • Training of molecular generative models using NIMO-generated motif sequences under diverse task scenarios.
  • Evaluation of NIMO's performance in structure-guided, activity-oriented, and pocket-based molecule generation.

Main Results:

  • NIMO demonstrated excellent performance in generating molecules de novo (from scratch).
  • The tool showed high efficacy in optimizing molecular structures based on a given scaffold.
  • NIMO successfully recognized syntactic patterns and structure-property relationships for effective molecular generation.

Conclusions:

  • NIMO represents a significant advancement in computational approaches for natural product drug discovery.
  • The tool effectively expands the accessible chemical space of natural products.
  • NIMO shows promise for accelerating the identification of new drug candidates through efficient molecule generation and optimization.