9 Structure-based Drug Design with Active Learning
This chapter covers
- How three-dimensional protein structures can be leveraged to guide rational drug design through computational modeling of protein-ligand interactions.
- Implementing a complete protein-ligand docking workflow to predict and evaluate how small molecules bind to target proteins.
- Using deep learning approaches to create surrogate models that dramatically accelerate virtual screening of ultra-large compound libraries.
- Building active learning systems that efficiently identify promising drug candidates while minimizing computational resources.
- Extending active learning techniques to more rigorous binding affinity predictions with free energy perturbation for lead optimization.
Structure-based drug design (SBDD) leverages knowledge of the three-dimensional structure of a biological target, such as a protein involved in a disease, to guide the design and selection of molecules that can interact with and modulate its function. A key computational technique within SBDD is molecular docking, which simulates the interaction between a small molecule (i.e., a potential drug) and the target protein. Docking predicts how well a molecule binds to the protein and can be used to screen large libraries of compounds to identify promising drug candidates.