RT Journal Article
SR Electronic
T1 Review of Natural Language Processing in Pharmacology
JF Pharmacological Reviews
JO Pharmacol Rev
FD American Society for Pharmacology and Experimental Therapeutics
SP PHARMREV-AR-2022-000715
DO 10.1124/pharmrev.122.000715
A1 Dimitar Trajanov
A1 Vangel Trajkovski
A1 Makedonka Dimitrieva
A1 Jovana Dobreva
A1 Milos Jovanovik
A1 Matej Klemen
A1 Aleš Žagar
A1 Marko Robnik-Šikonja
YR 2023
UL http://pharmrev.aspetjournals.org/content/early/2023/03/24/pharmrev.122.000715.abstract
AB Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora. The main objective of this work is to survey the recent use of NLP in the field of pharmacology. As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology. It has been used extensively, from intelligent searches through thousands of medical documents to finding traces of adversarial drug interactions in social media. We split our coverage into five categories to survey modern NLP methodology, commonly addressed tasks, relevant textual data, knowledge bases, and useful programming libraries. We split each of the five categories into appropriate subcategories, describe their main properties and ideas, and summarize them in a tabular form. The resulting survey presents a comprehensive overview of the area, useful to practitioners and interested observers. Significance Statement The main objective of this work is to survey the recent use of NLP in the field of pharmacology, in order to provide a comprehensive overview of the current state in the area after the rapid developments which occurred in the last few years. We believe the resulting survey to be useful to practitioners and interested observers in the domain.