  With the prospect of automating a number of chemical tasks with high
fidelity, chemical language processing models are emerging at a rapid speed.
Here, we present a cloud-based real-time platform that allows users to
virtually screen molecules of interest. For this purpose, molecular embeddings
inferred from a recently proposed large chemical language model, named
MolFormer, are leveraged. The platform currently supports three tasks: nearest
neighbor retrieval, chemical space visualization, and property prediction.
Based on the functionalities of this platform and results obtained, we believe
that such a platform can play a pivotal role in automating chemistry and
chemical engineering research, as well as assist in drug discovery and material
design tasks. A demo of our platform is provided at
\url{www.ibm.biz/molecular_demo}.
