Abstract The overall goal of my research program is to develop and apply state-of-the-art machine learning and molecular modeling tools to facilitate the rational design of modulators of important cellular pathways for therapeutic use. Protein-protein interactions (PPIs) are central factors in cellular signaling and biological networks, and their mis-regulations lead to diseases states. Thus PPIs are biologically compelling targets for drug discovery. Despite a few notable successes, most PPIs have not been successfully targeted and remain challenging for therapeutic intervention. The fundamental challenge derives from their intrinsic structural features: the binding surfaces of many PPIs are generally large in area, flat, and dynamic. PPIs are often transient and involve multivalent contacts. One of the most promising PPI inhibitor discovery strategies is to use miniature protein domain mimetics (PDMs) to reproduce the key interface contacts utilized by nature. PDMs are advantageous as medium-sized molecules with high surface complementarity and a broader set of contact points than typical small molecules, but are still limited because—by definition—only a portion of the total PPI binding energy is captured in the interaction. The binding affinity of the synthetic domains is often lower than the cognate full-length proteins. In last five years, we have significantly advanced a pocket-guided rational design approach based on AlphaSpace to tackle this challenge. We have successfully optimized a PDM to target the KIX domain of the p300/CBP coactivator by introducing non-natural amino acids to improve pocket-fragment binding; rationally designed a novel NEMO coiled coil mimic that disrupts virus-induced NF-κB signaling and induces cell death; and successfully targeted a new binding pocket on MDM2 and MDMX with a potent dual inhibitor by elaborating hydrogen-bond stabilized alpha-helix mimetics. Meanwhile, we have developed state-of-the-art scoring functions for protein-ligand docking as well as virtual screening, advanced deep learning models to predict molecular properties and chemical reactions, and established strong and fruitful collaborations with several outstanding experimental labs in chemical biology and biophysics to discover new modulators of biomolecular interactions. These significant advances set the stage for us to further push the frontier of integrating machine learning and molecular modeling for rational drug design. Our focus in the next few years will be to establish a robust pocket-guided design platform based on AlphaSpace and machine learning for PPI orthosteric inhibitor optimization, provide physical/chemical insights and develop novel computational strategies for allosteric modulator discovery, and explore chemical space with deep sequence/graph/geometric representation learning for multi-objective molecular design. Our modulator design efforts in close collaborations with our experimental colleagues will not only rigorousl...