10 Generative Models for De Novo Design
This chapter covers
- Challenges of navigating chemical space and limitations of traditional methods.
- How generative models, particularly autoencoders, can learn a compressed "latent space" representation of molecules.
- The architectural components of autoencoders, including tokenization, embedding layers, and encoder-decoder structures.
- Why standard autoencoders fail at generating novel molecules and how Variational Autoencoders (VAEs) solve this with a probabilistic approach.
- Advanced techniques like Recurrent Neural Networks (GRUs), cyclical annealing, and sophisticated tokenization that create powerful generative models for chemistry.
The journey of discovering a new drug is often likened to finding a needle in a colossal haystack. It's a process fraught with challenges, immense costs, and high attrition rates. At its heart, drug discovery is a molecular design problem: identifying or creating a molecule with the precise set of properties needed to safely and effectively treat a disease. This chapter delves into computational techniques that aim to make this quest more efficient and targeted, designing novel molecules with desired characteristics.