Machine learning in medicinal chemistry has seen rapid development in recent years as has explainable artificial intelligence (XAI) . The need to interpret complicated machine learning methods has increased due to the increasing adoption of machine learning in public tools and services. In this context, Shapley additive explanations (SHAP) is a popular XAI method that attributes an importance score to every feature. The method is based on the Shapley value from game theory to determine fair contributions and has been widely adopted to explain predictions of otherwise opaque machine learning models. The basic technique is model agnostic, but the framework has faster algorithms for the explanation of specific machine learning methods such as random forest models and deep neural networks. While Shapley or "SHAP" values do not provide direct explainability of the model, the method can be used to highlight regions of importance in images or important words in natural language processing, allowing for an easy interpretation of the explanations.