Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction

Materials at the nanoscale exhibit specific physicochemical interactions with their environment. Therefore, evaluating their toxic potential is a primary requirement for regulatory purposes and for the safer development of nanomedicines. In this review, to aid the understanding of nano–bio interactions from environmental and health and safety perspectives, the potential, reality, challenges, and future advances that artificial intelligence (AI) and machine learning (ML) present are described. Herein, AI and ML algorithms that assist in the reporting of the minimum information required for biomaterial characterization and aid in the development and establishment of standard operating procedures are focused. ML tools and ab initio simulations adopted to improve the reproducibility of data for robust quantitative comparisons and to facilitate in silico modeling and meta‐analyses leading to a substantial contribution to safe‐by‐design development in nanotoxicology/nanomedicine are mainly focused. In addition, future opportunities and challenges in the application of ML in nanoinformatics, which is particularly well‐suited for the clinical translation of nanotherapeutics, are highlighted. This comprehensive review is believed that it will promote an unprecedented involvement of AI research in improvements in the field of nanotoxicology and nanomedicine.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction ; volume:2 ; number:12 ; year:2020 ; extent:19 ; Advanced intelligent systems ; 2, Heft 12 (2020) (gesamt 19)

Contributors, Places and Time

• Creator: Singh, Ajay Vikram ; Rosenkranz, Daniel ; Ansari, Mohammad Hasan Dad ; Singh, Rishabh ; Kanase, Anurag ; Singh, Shubham Pratap ; Johnston, Blair ; Tentschert, Jutta ; Laux, Peter ; Luch, Andreas

Further information

• DOI: 10.1002/aisy.202000084
• URN: urn:nbn:de:101:1-2022063007525366001736
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:20 AM CEST