A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert-derived structure-activity alert spaces-Reference-Cited by-同舟云学术

A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert-derived structure-activity alert spaces

Published:2017-02-28 Issue:8 Volume:37 Page:985-995
ISSN:0260-437X
Container-title:Journal of Applied Toxicology
language:en
Short-container-title:J. Appl. Toxicol.

Author:

Canipa Steven J.¹,Chilton Martyn L.¹,Hemingway Rachel¹,Macmillan Donna S.¹^ORCID,Myden Alun¹,Plante Jeffrey P.¹,Tennant Rachael E.¹,Vessey Jonathan D.¹,Steger-Hartmann Thomas²,Gould Janet³,Hillegass Jedd³,Etter Sylvain⁴,Smith Benjamin P. C.⁵,White Angela⁶,Sterchele Paul⁷,De Smedt Ann⁸,O'Brien Devin⁹,Parakhia Rahul⁹

Affiliation:

1. Lhasa Limited; Granary Wharf House 2 Canal Wharf, Leeds LS11 5PS UK

2. Bayer Pharma; Muellerstrasse 178 Wedding, Berlin D-13342 Germany

3. Bristol-Myers Squibb; 1 Squibb Drive New Brunswick NJ 08903 USA

4. Firmenich S.A.; Rue de la Bergère 7 Meyrin 2 CH-1217 Switzerland

5. Firmenich Inc.; POB 5880 Princeton NJ 08543 USA

6. GlaxosmithKline; Park Road Ware SG12 0DP UK

7. International Flavors & Fragrances Inc.; 800 Rose Lane Union Beach NJ 07735 USA

8. Janssen Research and Development; Turnhoutseweg 30 Beerse B-2340 Belgium

9. RIFM; 50 Tice Boulevard Woodcliff Lake NJ 07677 USA

Publisher

Wiley