A Furuhama, A Kitazawa, J Yao, C E Matos Dos Santos, J Rathman, C Yang, J V Ribeiro, K Cross, G Myatt, G Raitano, E Benfenati, N Jeliazkova, R Saiakhov, S Chakravarti, R S Foster, C Bossa, C Laura Battistelli, R Benigni, T Sawada, H Wasada, T Hashimoto, M Wu, R Barzilay, P R Daga, R D Clark, J Mestres, A Montero, E Gregori-Puigjané, P Petkov, H Ivanova, O Mekenyan, S Matthews, D Guan, J Spicer, R Lui, Y Uesawa, K Kurosaki, Y Matsuzaka, S Sasaki, M T D Cronin, S J Belfield, J W Firman, N Spînu, M Qiu, J M Keca, G Gini, T Li, W Tong, H Hong, Z Liu, Y Igarashi, H Yamada, K-I Sugiyama, M Honma
Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating...
2023: SAR and QSAR in Environmental Research