The developments of the past years demonstrate the potential of data-driven machine learning applications in the field of molecular informatics in an impressive manner ]. An obvious requirement to benefit from this development is the availability of open structured experimental data . The integration of open data infrastructures will enable AI to be used in nearly every field of chemistry. The application of deep learning methodologies and the sharing of code and data in the field of chemistry are still in their early stages and require more community standards to be developed. Many of the models are still being trained from scratch using in-house servers and GPUs, which is a time-consuming and restrictive process. The rapid growth of the field will be enabled by the sharing of already-trained models and curated data with the public. When sharing code or data, high quality and data standards must be maintained . Using the public cloud infrastructures will readily allow researchers to take advantage of the latest developments in hardware and software, which will lead to faster growth and a reduction in energy consumption. There are several initiatives working continuously to implement open data, open-source, and open science in their individual research area . Fueled by the availability of more and more open research data, AI-powered molecular informatics will be a key driver of the digital transformation of chemistry in the coming years.