Editorial: Advancements in computational studies of drug toxicity.

Editorial on the Research Topic "Advancements in computational studies of drug 13 toxicity" 14 Computational approaches to studying drug toxicity have been and continue to be an important tool 15 in drug discovery. In silico methods are especially appealing for their ease of use and reduction of 16 resources and expenses. Advances in machine learning and deep learning have made substantial 17 advances in recent years, offering new applications in the study of drug toxicity. Drug toxicity 18 concerns (e.g., mutagenicity, endocrine toxicity, or cardiotoxicity) identified during screening and 19 optimization can prevent otherwise strong therapeutic candidates from proceeding. Inability to satisfy 20 drug safety criteria is a primary driver of drug withdrawal and termination during clinical 21 development. Our goal is to review key advances in computational studies of drug toxicity, especially 22 those that may be pragmatically useful in drug discovery. 23In this Research Topic, five unique advancements in computational studies of drug toxicity have 24 been published. We present each accepted publications in chronological order: 25