Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.
Kale, Bharat, Clyde, Austin, Sun, Maoyuan, Ramanathan, Arvind, Stevens, Rick, & Papka, Michael E. (2023). ChemoGraph: Interactive Visual Exploration of the Chemical Space. https://doi.org/10.1111/cgf.14807
Kale, Bharat, Clyde, Austin, Sun, Maoyuan, et al., "ChemoGraph: Interactive Visual Exploration of the Chemical Space," (2023), https://doi.org/10.1111/cgf.14807
@conference{osti_1996240,
author = {Kale, Bharat and Clyde, Austin and Sun, Maoyuan and Ramanathan, Arvind and Stevens, Rick and Papka, Michael E.},
title = {ChemoGraph: Interactive Visual Exploration of the Chemical Space},
annote = {Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.},
doi = {10.1111/cgf.14807},
url = {https://www.osti.gov/biblio/1996240},
place = {United States},
organization = {Argonne National Laboratory (ANL), Argonne, IL (United States)},
year = {2023},
month = {06}}
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
National Institutes of Health (NIH) - National Institute of Allergy and Infectious Diseases (NIAID); U.S. Department of Treasury - Coronavirus Aid, Relief, and Economic Security (CARES) Act; USDOE Exascale Computing Project (ECP); National Science Foundation (NSF)
DOE Contract Number:
AC02-06CH11357
OSTI ID:
1996240
Resource Relation:
Conference: 25th Eurographics Conference on Visualization, 06/12/23 - 06/16/23, Leipzig, DE
Proceedings of the 2015 ACM International Workshop on International Workshop on Security and Privacy Analytics - IWSPA '15https://doi.org/10.1145/2713579.2713583