Bulletin of the American Physical Society
New England Section Fall 2023 Meeting
Friday–Saturday, October 20–21, 2023; University of Rhode Island, Kingston, Rhode Island
Session H01: Poster Session
9:40 AM,
Saturday, October 21, 2023
University of Rhode Island
Room: Engineering Fascitelli Center Hallway
Abstract: H01.00014 : Classification of Biodegradable Chemicals Based on Their Structures and Physicochemical Properties Using Computational Simulations
Presenter:
Richard Kyung
(CRG-NJ)
Authors:
Richard Kyung
(CRG-NJ)
Eunbin Cho
(Providence Christian Academy)
In this paper, experimental values of 1055 chemicals were collected for the prediction. Forty-one molecular descriptors and one experimental class were used as attribute information. Explanatory Data Analysis (EDA) and data visualization were performed to identify the type and structure of the data and get the descriptive statistics. The studied biodegradation model involves selecting a training set of compounds with known biodegradability and identifying relevant molecular descriptors, such as electronic, topological, and steric properties. These descriptors with machine learning are then used to develop a mathematical model that can predict the biodegradability of new compounds.
It showed that the accuracy and reliability of QSAR models depend on the quality and completeness of the training data and the relevance of the molecular descriptors used in the model.
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