Reliable and robust human dose prediction plays a pivotal role in drug discovery. The prediction of human dose requires proper modeling of preclinical intravenous (IV) pharmacokinetic (PK) data, which is usually achieved either through noncompartmental analysis (NCA) or compartmental analysis. While NCA is straightforward, it loses valuable information about the shape of the PK curves. In contrast, compartmental analysis offers a more comprehensive interpretation but poses challenges in scaling up for high-throughput applications in discovery. To address this challenge, we developed computational frameworks, termed compartmental PK (CPK) and automated dose prediction (ADP), to enable automated compartmental model-based IV PK data modeling, translation, and simulation for human dose prediction in compound triaging and optimization. With CPK and ADP, we analyzed compounds with data collected at the MRL between 2013 and 2023 to quantitatively characterize the impact of different PK modeling and simulation methods on human dose prediction. Our study revealed that despite minimal impact on estimating animal PK parameters, different methods significantly impacted predicted human dose, exposure, and Cmax, driven more by different simulation assumptions than by the PK modeling itself. CPK-ADP therefore enables us to efficiently perform complex human dose predictions on a large scale while integrating the latest and best information available on absorption, distribution, and clearance to support decision-making in discovery.