# Cloud-Based Real-Time Molecular Screening Platform with MolFormer

arXiv: https://arxiv.org/abs/2208.06665 · HTML: https://mlsanity.com/paper/2208.06665/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 13 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Optimizing Molecules using Efficient Queries from Property Evaluations](https://mlsanity.com/paper/2011.01921/) (2020)

## Built on by (most cited first, 1 of 1)

- [Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties](https://mlsanity.com/paper/2407.02770/) (2024)

## Abstract (arXiv)

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 urlwww.ibm.biz/moleculardemo.