Artificial intelligence (AI) technology has demonstrated remarkable potential
in drug dis-covery, where pharmacokinetics plays a crucial role in determining
the dosage, safety, and efficacy of new drugs. A major challenge for AI-driven
drug discovery (AIDD) is the scarcity of high-quality data, which often
requires extensive wet-lab work. A typical example of this is pharmacokinetic
experiments. In this work, we develop a physical formula enhanced mul-ti-task
learning (PEMAL) method that predicts four key parameters of pharmacokinetics
simultaneously. By incorporating physical formulas into the multi-task
framework, PEMAL facilitates effective knowledge sharing and target alignment
among the pharmacokinetic parameters, thereby enhancing the accuracy of
prediction. Our experiments reveal that PEMAL significantly lowers the data
demand, compared to typical Graph Neural Networks. Moreover, we demonstrate
that PEMAL enhances the robustness to noise, an advantage that conventional
Neural Networks do not possess. Another advantage of PEMAL is its high
flexibility, which can be potentially applied to other multi-task machine
learning scenarios. Overall, our work illustrates the benefits and potential of
using PEMAL in AIDD and other scenarios with data scarcity and noise.

我们开发了一种物理公式增强的多任务学习（PEMAL）方法，通过将物理公式纳入多任务框架中，增强了药物代谢动力学参数的预测准确性。我们的研究表明，PEMAL 相对于 Typical Graph Neural Networks 显著降低了数据需求，并提高了对噪声的鲁棒性，此外，PEMAL 的高灵活性也有潜在的应用价值。