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Kwon Research Group

Texas A&M University College of Engineering

Latent Space Optimization for Molecular Design

Optimizing drug-like molecules traditionally relies on trial-and-error approaches or brute-force computational searches, making it difficult to explore chemical space efficiently. This project introduces a latent space optimization framework that utilizes transformer-generated molecular embeddings to refine molecular properties and identify optimal drug candidates.

Highlights of this approach:

  • Optimization in structured latent space – Instead of optimizing raw molecular descriptors, the model refines properties within a learned chemical space.
  • Efficient exploration of molecular candidates – Identifies compounds with desirable pharmacokinetic profiles, balancing bioavailability, metabolic stability, and toxicity.
  • Seamless integration with predictive models – Connects molecular design and property prediction, reducing the need for exhaustive screening.

This data-driven approach streamlines molecular optimization, reducing reliance on computationally expensive searches and enabling the more efficient discovery of novel drug candidates.

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