An AI-Based Classifier to Search for Drug-Induced Liver Injury Literature

Dr Sanjay Rathee

10/17/20232 min read

DILI C is a novel tool to classify the literature as related to DILI or not. This is significant as it has the potential to aid researchers in drug development and research and clinical settings during the risk assessment. DILI C is implemented in such a way that it can be modified to classify any other drug’s adverse reactions and is not limited to DILI. Therefore, the DILI C code available at the GitHub link could be useful for researchers interested in drug-induced neural, cardiovascular, or renal toxicities for example. The Shiny app for DILI C provides the tool in a user-friendly and accessible way that can be easily used by nonprogrammers who have the literature they want to classify. Additionally, an ISMB extended video talk is available as a supplementary resource that explains the pipeline step by step (https://www.youtube.com/watch?v=j305yIVi_f8).

Drug-induced liver injury (DILI) is a class of adverse drug reactions (ADR) that causes problems in both clinical and research settings. It is the most frequent cause of acute liver failure in the majority of Western countries and is a major cause of attrition of novel drug candidates. Manual trawling of the literature is the main route of deriving information on DILI from research studies. This makes it an inefficient process prone to human error. Therefore, an automatized AI model capable of retrieving DILI-related articles from the huge ocean of literature could be invaluable for the drug discovery community. In this study, we built an artificial intelligence (AI) model combining the power of natural language processing (NLP) and machine learning (ML) to address this problem. This model uses NLP to filter out meaningless text (e.g., stop words) and uses customized functions to extract relevant keywords such as singleton, pair, and triplet. These keywords are processed by an apriori pattern mining algorithm to extract relevant patterns which are used to estimate initial weightings for a ML classifier. Along with pattern importance and frequency, an FDA-approved drug list mentioning DILI adds extra confidence in classification. The combined power of these methods builds a DILI classifier (DILI C ), with 94.91% cross-validation and 94.14% external validation accuracy. To make DILI C as accessible as possible, including to researchers without coding experience, an R Shiny app capable of classifying single or multiple entries for DILI is developed to enhance ease of user experience and made available at https://researchmind.co.uk/diliclassifier/. Additionally, a GitHub link (https://github.com/sanjaysinghrathi/DILI-Classifier) for app source code and ISMB extended video talk (https://www.youtube.com/watch?v=j305yIVi_f8) are available as supplementary materials.