Researchers at ETH Zurich and Roche Pharma have developed an innovative closed-loop workflow that advances chemical reaction prediction, particularly in data-scarce drug discovery environments. This development is part of a broader trend where machine learning and computational chemistry techniques are increasingly applied to various domains, including heterogeneous catalysis, perovskite modeling, low-k dielectric material discovery, and CO2 conversion to methanol. Recent studies have utilized deep learning architectures such as convolutional neural networks and transformers to model nonradiative recombination in perovskites and have applied explainable AI to metal-organic framework design for CO2 capture. Other notable advancements include machine learning-guided protein engineering to improve stereoselectivity, the use of hybrid machine learning and molecular mechanics for free energy calculations, and AI frameworks for optimizing synthesis sequences in autonomous laboratories. These efforts collectively demonstrate the growing integration of machine learning approaches with molecular simulations and experimental data to accelerate material discovery, catalyst design, and drug-target interaction predictions.