  Artificial Intelligence (AI)-driven material design has been attracting great
attentions as a groundbreaking technology across a wide spectrum of industries.
Molecular design is particularly important owing to its broad application
domains and boundless creativity attributed to progresses in generative models.
The recent maturity of molecular generative models has stimulated expectations
for practical use among potential users, who are not necessarily familiar with
coding or scripting, such as experimental engineers and students in chemical
domains. However, most of the existing molecular generative models are Python
libraries on GitHub, that are accessible for only IT-savvy users. To fill this
gap, we newly developed a graphical user interface (GUI)-based web application
of molecular generative models, Molecule Generation Experience, that is open to
the general public. This is the first web application of molecular generative
models enabling users to work with built-in datasets to carry out molecular
design. In this paper, we describe the background technology extended from our
previous work. Our new online evaluation and structural filtering algorithms
significantly improved the generation speed by 30 to 1,000 times with a wider
structural variety, satisfying chemical stability and synthetic reality. We
also describe in detail our Kubernetes-based scalable cloud architecture and
user-oriented GUI that are necessary components to achieve a public service.
Finally, we present actual use cases in industrial research to design new
photoacid generators (PAGs) as well as release cases in educational events.
