An important aspect in the development of small molecules as drugs or agrochemicals 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 predictions 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 (Schneckener in J Chem Inf Model 59:4893–4905, 2019). 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 24 h, while the model has only been trained on the total exposure.

小分子作为药物或农用化学品开发的一个重要方面是它们在静脉内和口服给药后的全身可用性。从潜在候选物的化学结构预测系统可用性是非常可取的，因为它允许将药物或农用化学品的开发集中在具有有利动力学特征的化合物上。然而，此类预测具有挑战性，因为可用性是分子特性、生物学和生理学之间复杂相互作用的结果，并且训练数据很少。在这项工作中，我们改进了早期开发的混合模型（Schneckener in J Chem Inf Model 59:4893–4905, 2019）。我们将总口服暴露量的中值倍数变化误差从 2.85 减少到 2.35，静脉给药的中值倍数变化误差从 1.95 减少到 1.62。这是通过在更大的数据集上进行训练、改进神经网络架构以及机械模型的参数化来实现的。此外，我们扩展了我们的方法来预测其他终点并处理不同的协变量，例如性别和剂型。与纯机器学习模型相比，我们的模型能够预测未经训练的新端点。我们通过预测前 24 小时内的暴露量来展示此功能，而模型仅针对总暴露量进行了训练。