ChemoVerse: Manifold traversal of latent spaces for novel molecule discovery

Understand

In order to design a more potent and effective chemical entity, it is essential to identify molecular structures with the desired chemical properties.

• Recent advances in generative models using neural networks and machine learning are being widely used by many emerging startups and researchers in this domain to design virtual libraries of drug-like compounds.
• Although these models can help a scientist to produce novel molecular structures rapidly, the challenge still exists in the intelligent exploration of the latent spaces of generative models, thereby reducing the randomness in the generative procedure.
• In this work we present a manifold traversal with heuristic search to explore the latent chemical space.