Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine-Reference-Cited by-同舟云学术

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Published:2020-07-06 Issue:17 Volume:9 Page:1901862
ISSN:2192-2640
Container-title:Advanced Healthcare Materials
language:en
Short-container-title:Adv. Healthcare Mater.

Author:

Singh Ajay Vikram¹^ORCID,Ansari Mohammad Hasan Dad²³,Rosenkranz Daniel¹,Maharjan Romi Singh¹,Kriegel Fabian L.¹,Gandhi Kaustubh⁴,Kanase Anurag⁵,Singh Rishabh⁶,Laux Peter¹,Luch Andreas¹

Affiliation:

1. Department of Chemical and Product Safety German Federal Institute for Risk Assessment (BfR) Max‐Dohrn‐Strasse 8‐10 Berlin 10589 Germany

2. The BioRobotics Institute Scuola Superiore Sant'Anna Via Rinaldo Piaggio 34 Pontedera 56025 Italy

3. Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Via Rinaldo Piaggio 34 Pontedera 56025 Italy

4. Bosch Sensortec GmbH Gerhard‐Kindler‐Straße 9 Reutlingen 72770 Germany

5. Department of Bioengineering Northeastern University Boston MA 02215 USA

6. Rajarshi Shahu College of Engineering Pune Maharashtra 411033 India

Funder

BfR SFP

Publisher

Wiley

Subject

Pharmaceutical Science,Biomedical Engineering,Biomaterials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adhm.201901862

Reference174 articles.

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3. Nanotoxicity prediction using computational modelling - review and future directions

4. A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

5. Open Source Chemoinformatics Software including KNIME Analytics

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