Quantitative structure–property relationship modelling can be a valuable alternative method to replace or reduce experimental testing. In particular, some endpoints such as octanol–water (K
OW) and organic carbon–water (K
coefficients of polychlorinated biphenyls (PCBs) are easier to predict and various models have been already developed. In this paper, two different methods, which are multiple linear regression based on the descriptors generated using Dragon software and hologram quantitative structure–activity
relationships, were employed to predict suspended particulate matter (SPM) derived log K
OC and generator column, shake flask and slow stirring method derived log K
OW values of 209 PCBs. The predictive ability of the derived models was validated using a test
set. The performances of all these models were compared with EPI Suite™ software. The results indicated that the proposed models were robust and satisfactory, and could provide feasible and promising tools for the rapid assessment of the SPM derived log K
OC and generator
column, shake flask and slow stirring method derived log K
OW values of PCBs.
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