In this proposal, we request compute time on Alvis and Dardel, as well as storage on Mimer, for ongoing and expanded research projects in the AI Laboratory for Molecular Engineering, headed by Dr. Rocío Mercado Oropeza in the CSE Department at Chalmers. This proposal supports six PhD students, three postdoctoral researchers, 1-6 MSc students (depending on the term), and one faculty member, all working on AI-driven molecular engineering and molecular dynamics. These projects span various aspects of data-driven molecular engineering, including single-cell image and omics analysis, molecular dynamics simulations, and AI methods for biochemical applications such as generative AI for drug and materials discovery. We aim to address challenges in representation learning and predictive modeling for molecular systems. Our research objectives are to: (1) Develop large-scale multi-modal neural networks for single-cell data and cell image analysis. (2) Train advanced generative models for molecular design and optimization. (3) Apply molecular dynamics simulations to study biomolecular interactions, which will be used to design surrogate models for important biochemical properties in our generative models. (4) Train language models for synthesizability-constrained molecular generation, metabolite prediction, and molecular representation. These efforts will lead to novel tools and methods applicable to AI-driven molecular engineering, with anticipated publications in leading computer science and bioinformatics journals. All code, models, and datasets built will be released open-source and our findings published in primarily computer science venues. This Medium Storage application is a complement to the following Medium Compute application: AI for Molecular Engineering (NAISS 2024/5-630)