De Novo Drug Design

The Challenge

The chemical space of possible drug-like molecules contains approximately 10^60 compounds—more than there are atoms in the universe. Traditional medicinal chemistry explores this space one molecule at a time. We need to explore it intelligently and exhaustively.

Our Approach

We employ generative AI models that learn the principles of molecular design from existing drugs and biological data, then create entirely novel molecules optimized for specific therapeutic targets.

Key Methodologies

• •Generative adversarial networks (GANs): Neural networks that generate novel molecular structures with desired properties
• •Variational autoencoders (VAEs): Models that learn compressed representations of molecular space, enabling intelligent exploration
• •Reinforcement learning: AI agents that iteratively design molecules, receiving rewards for desired properties like binding affinity, selectivity, and synthesizability
• •Transformer models: Treating molecules as sequences (SMILES notation) and applying natural language processing techniques to molecular generation