Existing structure-based drug design (SBDD) approaches typically keep a fixed, rigid protein pocket in place while generating ligands (therapeutic drug molecules). However, this approach has led to a number of problems including steric clashes with the protein pocket and ligands with poor binding affinity. This project aims to train an SBDD model which allows for flexibility in the protein pocket by training a flow-matching generative model to map from apo (unbound) protein structure to holo (ligand-bound) protein structures. This project builds on our previous work training unconditional ligand generative models using flow matching, called SemlaFlow. Conditioning SemlaFlow on protein pockets and allowing the model to generate flexible pockets has the potential to significantly improve the validity and quality of the generated ligands. We have prepared a dataset of apo-holo-ligand systems with which we intend to train a large scale generative model for this task.