Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish

Tissue-to-blood
partition coefficients (Ptb) are key parameters
for assessing toxicokinetics of xenobiotics
in organisms, yet their experimental data were lacking. Experimental
methods for measuring Ptb values are inefficient,
underscoring the urgent need for prediction models. However, most
existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was
limited. The current study developed a multimodal model capable of
processing and integrating textual (categorical features) and numerical
information (molecular descriptors/fingerprints) to simultaneously
predict Ptb values across various species,
tissues, blood matrices, and measurement methods. Artificial neural
network algorithms with embedding layers were used for the multimodal
modeling. The corresponding unimodal models were developed for comparison.
Results showed that the multimodal model outperformed unimodal models.
To enhance the reliability of the model, a method considering categorical
features, weighted molecular similarity density, and weighted inconsistency
in molecular activities of structure–activity landscapes was
used to characterize the AD. The model constrained by the AD exhibited
better prediction accuracy for the validation set, with the determination
coefficient, root mean-square error, and mean absolute error being
0.843, 0.276, and 0.213 log units, respectively. The multimodal model
coupled with the AD characterization can serve as an efficient tool
for internal exposure assessment of chemicals.