Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction. (21st December 2020)

• Record Type: Journal Article
• Authors: Singh, Ajay Vikram; Rosenkranz, Daniel; Ansari, Mohammad Hasan Dad; Singh, Rishabh; Kanase, Anurag; Singh, Shubham Pratap; Johnston, Blair; Tentschert, Jutta; Laux, Peter; Luch, Andreas
• Is Part Of: Advanced intelligent systems. Volume 2:Number 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-21
• Subjects: Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.202070125
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : Computational Nanotoxicology Machine learning tools are making great strides in advancing computational nanotoxicology via in‐silico modeling and ab‐initio simulations to understand the nano‐bio interactions from environmental and health safety perspectives. In article number 2000084, Ajay Vikram Singh and co‐workers describe the potential, reality, challenges, and future advances that artifi cial intelligence (AI) and machine learning (ML) present in advanced material design and toxicity predictions.