Physical formula enhanced multi-task learning for pharmacokinetics prediction

arXiv:2404.10354v1 Announce Type: cross
Abstract: 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 …

abstract artificial artificial intelligence arxiv challenge cs.ce cs.lg data discovery drug discovery drugs intelligence lab major multi-task learning prediction q-bio.qm quality quality data role safety technology type work