  An important aspect in the development of small molecules as drugs or
agro-chemicals is their systemic availability after intravenous and oral
administration. The prediction of the systemic availability from the chemical
structure of a potential candidate is highly desirable, as it allows to focus
the drug or agrochemical development on compounds with a favorable kinetic
profile. However, such pre-dictions are challenging as the availability is the
result of the complex interplay between molecular properties, biology and
physiology and training data is rare. In this work we improve the hybrid model
developed earlier [1]. We reduce the median fold change error for the total
oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to
1.62. This is achieved by training on a larger data set, improving the neural
network architecture as well as the parametrization of mechanistic model.
Further, we extend our approach to predict additional endpoints and to handle
different covariates, like sex and dosage form. In contrast to a pure machine
learning model, our model is able to predict new end points on which it has not
been trained. We demonstrate this feature by predicting the exposure over the
first 24h, while the model has only been trained on the total exposure.
