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NVIDIA Technical Blog

Evaluating GenMol as a Generalist Foundation Model for Molecular Generation

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Table of Contents

  1. SAFE Overview
  2. Lead Optimization
  3. Comparing SAFE-GPT and GenMol for Drug Discovery Tasks
  4. Molecular Generation and Exploration of Chemical Space

SAFE Overview

  • Description: The Sequential Attachment-based Fragment Embedding (SAFE) representation simplifies scaffold decoration, linker design, and motif extension tasks into sequence completion tasks, enabling intuitive, fragment-based molecular design.
  • Key Points:
    • Versatility: Well-suited for scaffold decoration, linker design, and motif extension tasks.
    • Representation: Preserves molecular scaffolds and accommodates complex structures without intricate graph-based models.

Lead Optimization

  • Description: Allows for dynamic replacement of molecular fragments for iterative design refinement and optimization.
  • Example Output: The figure shows output where fragment library and QED-scoring oracle facilitate GenMol inference for guided optimization.

Comparing SAFE-GPT and GenMol for Drug Discovery Tasks

  • SAFE-GPT:
    • Built on autoregressive transformer architecture.
    • Suitable for fragment-constrained tasks like scaffold decoration and linker design.
    • Outperforms models like f-RAG and REINVENT in goal-directed lead optimization.
  • GenMol:
    • More flexible and unified framework for diverse drug discovery applications.
    • Suitable for hit generation and lead optimization tasks without fine-tuning.

Molecular Generation and Exploration of Chemical Space

  • Description: SAFE-GPT uses GPT architecture for sequential, autoregressive decoding, making it applicable to de novo and fragment-constrained molecule generation.
  • Applications:
    • SAFE-GPT: Ideal for motif extension and scaffold generation with strict fragment constraints.
    • GenMol: Better suited for researchers needing a flexible framework for various drug discovery tasks.

Test GenMol as an NVIDIA NIM now or explore code examples on GitHub to learn more about using GenMol for goal-directed hit optimization, lead optimization, and more.