Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery

Abstract

Abstract Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery. However, it is still critical for their adoption by the medicinal chemistry community to achieve models that, in addition to achieving high performance in their predictions, can be trusty explained to the end users in terms of their knowledge and background. Therefore, the investigation and development of explainable artificial intelligence (XAI) methods have become a key topic to address this challenge. For this reason, a comprehensive literature review about explanation methodologies for AI based models, focused in the field of drug discovery, is provided. In particular, an intuitive overview about each family of XAI approaches, such as those based on feature attribution, graph topologies, or counterfactual reasoning, oriented to a wide audience without a strong background in the AI discipline is introduced. As the main contribution, we propose a new taxonomy of the current XAI methods, which take into account specific issues related with the typical representations and computational problems study in the design of molecules. Additionally, we also present the main visualization strategies designed for supporting XAI approaches in the chemical domain. We conclude with key ideas about each method category, thoroughly providing insightful analysis about the guidelines and potential benefits of their adoption in medical chemistry. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning

Topics & Concepts