The integration of Artificial Intelligence (AI) and Machine Learning (ML) into drug discovery is promising and addresses a crucial need for faster, more efficient processes. AI/ML enhances the precision and speed of identifying potential therapeutic compounds. AI/ML algorithms can analyze vast heterogeneous datasets, integrate directly with experiments, predict molecular behaviors, and identify novel drug candidates with a higher likelihood of success in clinical trials. This reduces both the time and costs associated with traditional drug discovery methods. Furthermore, AI-driven models facilitate a deeper understanding of disease mechanisms, leading to more targeted and effective treatments. Our group is developing predictive and generative methods using graph/geometric modeling and explanation mechanisms, specifically targeting neurodegenerative diseases.