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 poten-tial candidate is highly desirable, as it allows to focus
the drug or agrochemicaldevelopment on compounds with a favorable kinetic
profile. However, such pre-dictions are challenging as the availability is the
result of the complex interplaybetween molecular properties, biology and
physiology and training data is rare.In this work we improve the hybrid model
developed earlier [34]. We reducethe median fold change error for the total
oral exposure from 2.85 to 2.35 andfor intravenous administration from 1.95 to
1.62. This is achieved by trainingon a larger data set, improving the neural
network architecture as well as theparametrization of mechanistic model.
Further, we extend our approach to predictadditional 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 newend points on which it has not
been trained. We demonstrate this feature by1predicting the exposure over the
first 24h, while the model has only been trainedon the total exposure.

通过采用扩展的混合模型并在更大的数据集上进行训练，我们成功改善了之前开发的模型，将总体口服暴露的中位倍数误差从 2.85 降低到 2.35，静脉给药从 1.95 降低到 1.62，实现了对药代动力学不利显性统计配置的化合物进行筛选和开发，同时提供了预测其他指标（如性别和剂量形式）的能力。