Asking the right questions for mutagenicity prediction from BioMedical text - Sathwik Acharya; Nicolas K. Shinada; Naoki Koyama; Megumi Ikemori; Tomoki Nishioka; Seiji Hitaoka; Atsushi Hakura; Shoji Asakura; Yukiko Matsuoka; Sucheendra K. Palaniappan (npj Systems Biology and Applications, 2023)
Abstract Assessing the mutagenicity of chemicals is an essential task in the drug development process. Usually, databases and other structured sources for AMES mutagenicity exist, which have been carefully and laboriously curated from scientific publications. As knowledge accumulates over time, updating these databases is always an overhead and impractical. In this paper, we first propose the problem of predicting the mutagenicity of chemicals from textual information in scientific publications. More simply, given a chemical and evidence in the natural language form from publications where the mutagenicity of the chemical is described, the goal of the model/algorithm is to predict if it is potentially mutagenic or not. For this, we first construct a golden standard data set and then propose MutaPredBERT, a prediction model fine-tuned on B i o L i n k B E R T based on a question-answering formulation of the problem. We leverage transfer learning and use the help of large transformer-based models to achieve a Macro F1 score of >0.88 even with relatively small data for fine-tuning. Our work establishes the utility of large language models for the construction of structured sources of knowledge bases directly from scientific publications.