arXiv:2603.05525v1 Announce Type: cross
Abstract: Deep learning, a subfield of machine learning, has gained importance in various application areas in recent years. Its growing popularity has led it to enter the natural sciences as well. This has created the need for molecular representations that are both machine-readable and understandable to scientists from different fields. Over the years, many chemical molecular representations have been constructed, and new ones continue to be developed as computer technology advances and knowledge of molecular complexity increases. This paper presents some of the most popular digital molecular representations inspired by natural language processing (NLP) and used in chemical informatics. In addition, the paper discusses some notable AI-based applications that use these representations. This paper aims to provide a guide to structural representations that are important for the application of AI in chemistry and materials science from the perspective of an NLP researcher. This review is a reference tool for researchers with little experience working with chemical representations who wish to work on projects at the interface of these fields.
Telemedicine Adoption for Managing Chronic and Rare Diseases in Indonesia During and Beyond the COVID-19 Era: Qualitative Study
Background: Telemedicine has emerged as a valuable tool for improving health care delivery, especially in low-resource and geographically isolated regions. In Indonesia, the COVID-19 pandemic