[entee]

This is true. Getting datasets with the necessary quality and scale for molecular ML is hard and uncommon. Experimental design is also a huge value add, especially given the enormous search space (estimates suggest there are more possible drug-like structures than there are stars in the universe). The challenge is figuring out how to do computational work in a tight marriage with the lab work to support and rapidly explore the hypotheses generated by the computational predictions. Getting compute and lab to mesh productively is hard. Teams and projects have to be designed to do so from the start to derive maximum benefit.

Also shameless plug: I started a company to do just that, anchored to generating custom million-to-billion point datasets and using ML to interpret and design new experiments at scale.