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}.
@article{arxiv.2208.06665,
title = {Cloud-Based Real-Time Molecular Screening Platform with MolFormer},
author = {Brian Belgodere and Vijil Chenthamarakshan and Payel Das and Pierre Dognin and Toby Kurien and Igor Melnyk and Youssef Mroueh and Inkit Padhi and Mattia Rigotti and Jarret Ross and Yair Schiff and Richard A. Young},
journal= {arXiv preprint arXiv:2208.06665},
year = {2022}
}