We request computational resources to advance molecular design at the SciLifeLab Drug Discovery and Development (DDD) Platform. This project unites three research groups from Uppsala University, Karolinska Institutet, and Lund University under the PULSE postdoctoral program to design antibodies (Lund), cyclic peptides (KI and UU), and DNA-encoded libraries (UU) using AI-driven drug discovery workflows. These projects integrate large-scale data-driven machine learning with physics-based computational chemistry to accelerate therapeutic discovery. Deep learning models trained on DNA-encoded library (DEL) datasets will enable the selection of high-affinity compounds from ultra-large chemical spaces. Cyclic peptide design will combine intracellular selection and next-generation sequencing with AI-based prioritization to identify molecular glues that modulate protein–protein interactions. Antibody design will employ AlphaFold3 in iterative redesign loops to refine antibody–antigen interfaces, guided by experimental binding and enrichment data. All AI-based approaches will be complemented by molecular dynamics (MD) simulations and free-energy calculations using GROMACS to validate structural predictions and provide physical interpretability. The requested allocation will provide sufficient computational resources for all three participating groups, in particularly PULSE postdoctoral researchers and additional team members to work efficiently within a shared high-performance computing environment.