Despite the promising advancements, several challenges remain, including the need for high-quality data, model interpretability, and integration with existing workflows. Addressing these challenges will be crucial for realizing ML's full potential in drug discovery. Future developments in ML techniques and their applications promise to further improve the drug discovery pipeline, offering new opportunities for innovation and discovery.