Unsupervised Learning of Molecular Embeddings for Enhanced Clustering
  and Emergent Properties for Chemical Compounds
Jaiveer Gill Ratul Chakraborty Reetham Gubba Amy Liu Shrey Jain Chirag Iyer Obaid Khwaja Saurav Kumar
The detailed analysis of molecular structures and properties holds great
potential for drug development discovery through machine learning. Developing
an emergent property in the model to understand molecules would broaden the
horizons for development with a new computational tool. We introduce various
methods to detect and cluster chemical compounds based on their SMILES data.
Our first method, analyzing the graphical structures of chemical compounds
using embedding data, employs vector search to meet our threshold value. The
results yielded pronounced, concentrated clusters, and the method produced
favorable results in querying and understanding the compounds. We also used
natural language description embeddings stored in a vector database with
GPT3.5, which outperforms the base model. Thus, we introduce a similarity
search and clustering algorithm to aid in searching for and interacting with
molecules, enhancing efficiency in chemical exploration and enabling future
development of emergent properties in molecular property prediction models.