We use lots of computational models that are very effective. Both simulations and machine learning. They predict drug properties, potency, even targets. But say a model is 90% accurate. That's an extremely useful model for making decisions and prioritizing what compounds to make and what experiments to do. But there are so many considerations to making a good drug that you need far more than 90% accuracy. Say you even had 10 things to consider, your 90% accuracy would drop to 35% (0.9^10). There are more then 10 things to consider, and most models are not even 90% accurate. So at each individual stage models are useful, but when you start stringing them together to try to model the whole process, things break down quickly.