Drug development is a time‐consuming, expensive and extremely risky procedure. Up to 90% of drug concepts are discarded due to challenges such as safety, efficacy and toxicity resulting in significant losses for the investor. The use of artificial intelligence (AI), namely machine learning and deep learning algorithms, to improve the drug discovery process is one technique that has arisen in recent years. AI has been effectively used in drug discovery and design. This chapter includes these machine learning approaches in depth, as well as their applications in medicinal chemistry. The current state‐of‐the‐art of AI supported pharmaceutical discovery is discussed, including applications in structure and ligand‐based virtual screening, de novo drug design, drug repurposing, and factors related, after introducing the basic principles, along with some application notes, of the various machine learning algorithms. Finally, obstacles and limits are outlined, with an eye towards possible future avenues for AI‐supported drug discovery and design.