Drug discovery is fairly different from pharmacometrics. Drug discovery is about finding what chemicals would likely produce the right effects by mining models and simulation for how they would bind to proteins and how that effects the protein's behavior. This generally uses molecular simulations, things like molecular dynamics or DFT to compute properties of the molecules themselves.

Pharmacometrics is focused on precision dosing: given a drug in a clinical trial, how should you be personalizing the dosing in order to have high efficacy with low toxicity? This is different depending on many factors (weight, metabolic factors, gender, etc.) and are a mix systems physiology types of models of metabolic and cell signaling (quantitative systems pharmacology and physiologically-based pharmacokinetics) and compartmental models.

They are both useful, just at different stages of the drug development pipeline. Drug discovery modeling and simulation is done at the very early stages before the clinical trial to predict what drugs to test and what the specificity of the targeting is (i.e. will it have off-target effects and cause side effects?). On the other hand, pharmacological modeling and simulation is done during the clinical to try and adaptively change the dosing, understand effects on the population, and predict whether the new off-target effects cause a system-wide toxic effects (i.e. just because drug X accidentally blocks the binding of Y to Z doesn't necessarily mean that most people will have a side effect, but you can predict whether certain sub-populations might be more prone to side effects and how likely that is to cause a clinical trial to fail). Given the cost of clinical trials is in the billions, any mathematics that can predict whether it will fail or simply avoid a clinical trial by proving safety through statistical means is something that's in high demand.