chapter nine
9 Structure-based Drug Design with Active Learning
This chapter covers
- How 3D protein structures can guide rational drug design.
- Implementation of a complete protein-ligand docking workflow.
- Using deep learning approaches to create surrogate models that dramatically accelerate virtual screening of ultra-large compound libraries.
- Active learning to identify drug candidates while minimizing computational resources.
- Extending active learning to 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 produces approximate scoring-function values that rank how well a molecule might bind to the protein and can be used to screen large libraries of compounds to identify promising drug candidates.