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The CHEESE Explorer is an AI-based molecular space exploration tool designed to generate and visualise chemical representations based on the trained latent space and is based on shape or electrostatic similarity of molecules.

This tool offers a fast and efficient way to explore molecular datasets, making it an invaluable asset for researchers in the field of medicinal chemistry and drug discovery.

Key Features

Chemical Representations:

  • CHEESE Explorer generates chemical representations swiftly, focusing on shape or electrostatic similarity.

Dimensionality Reduction:

  • The generated representations can be plotted using dimensionality reduction techniques, enabling users to visualise the dataset effectively.

Visualisation and Exploration:

  • Users can color the dataset by clusters or specific properties.
  • The tool allows for the exploration of data points (molecules) that are close to each other in the latent space.

Interpretable Latent Space:

  • CHEESE Explorer ensures that the distances between data points in the latent space correlate highly with their real similarities.
  • Unlike other AI models, CHEESE Explorer guarantees the interpretability of its latent space. For example:

    • Molecules with a 0.9 cosine similarity or a 0.1 Euclidean distance have proportionally the same fingerprint, shape, or electrostatic similarity, depending on the model.

Clustering and Similarity-Network Computation:

  • The tool supports subsequent clustering or similarity-network computation, facilitating deeper analysis and insights.

Demonstration video