Despite this progress, more work is needed to faithfully predict the outcome of reactions, synthesis routes, synthesis execution instructions, and learn distributions over molecules. Major challenges also still exist the scoring. Setting up multi-objective scoring functions is still somewhat a dark art, and requires trial and error to get right. The accurate prediction of biological properties is still limited both with machine learning, as well as physics-based approaches, and requires further improvements. Also, the community should put more effort in providing unified benchmarking methods. Finally, on an organizational level, to harness automated molecular design, computational methods need to be incorporated further in the discovery process, which requires strong collaboration from different groups of medicinal and computational chemists, machine learning researchers, software engineers, and data scientists.