Related Experiment Video
Updated: Mar 24, 2026
05:47
In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
14.8K
Environmental impact level prediction for inorganic and organic chemicals using machine learning models and
Chao-Hsu Yang1, Kai-Cheng Hsu2, Pei-Te Chiueh1
1Graduate Institute of Environmental Engineering, College of Engineering, National Taiwan University, Taipei, Taiwan.
Journal of Environmental Management
|March 22, 2026
Summary
This study developed machine learning models to predict chemical environmental impacts for both organic and inorganic compounds. The models achieved promising accuracy, offering a practical tool for sustainable manufacturing and environmental decision-making.
Area of Science:
- Environmental Chemistry
- Computational Chemistry
- Sustainable Manufacturing
Background:
- Growing chemical production necessitates robust environmental impact assessment.
- Existing machine learning methods are limited to organic chemicals, leaving a gap for inorganic compounds.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting environmental impact levels of both organic and inorganic chemicals.
- To integrate molecular descriptors into a two-level classification framework for enhanced prediction accuracy.
Main Methods:
- Utilized Mordred molecular descriptors for feature extraction.
- Employed a two-level machine learning classification framework.
- Applied SHapley Additive exPlanations (SHAP) for feature importance and mechanistic insights.
Main Results:
- Models incorporating Mordred descriptors demonstrated superior performance.
- Achieved high accuracies in predicting global warming potential (0.867) and freshwater eutrophication (0.800) levels.
- Models showed strong generalizability across external organic and inorganic chemical datasets.
Conclusions:
- This study advances machine learning applications for predicting chemical environmental impact levels.
- The developed models provide a practical tool for environmental decision-making in sustainable manufacturing.
- Interpretability analysis offers mechanistic understanding of factors driving environmental impacts.
Related Concept Videos
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
321
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
321
01:27Effects of Chemicals: Overview
2.4K
Drugs, encompassing various chemical compounds from natural sources, lab synthesis, or genetic engineering, elicit different biological responses in living organisms. Some of these responses are desirable or therapeutic, while others are undesirable. The primary goal of administering a drug is to achieve a therapeutic effect, that is, to address a specific disease or health condition. Any concurrent effects outside of this therapeutic outcome are considered undesirable. These undesirable...
2.4K
01:18Steps in Outbreak Investigation
694
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
694
01:15Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions
63
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
63
01:21Mechanistic Models: Overview of Compartment Models
469
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
469
01:20Pharmacokinetic Models: Overview
2.5K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
2.5K