QuantumTox: Utilizing quantum chemistry with ensemble learning for molecular toxicity prediction

Wang, Xun; Wang, Lulu; Wang, Shuang; Ren, Yongqi; Chen, Wenqi; Li, Xue; Han, Peifu; Song, Tao

doi:10.1016/j.compbiomed.2023.106744

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Pandas: a useful library for data management and analysis [11]. It provides data structures such as Data Frame to simplify process and advance data structure.…”

Section: Data Analysis and Visualization Tools (Pandas Numpy Matplotlib)mentioning

confidence: 99%

Promoting Brain Health Through Machine Learning to Predict Stroke Risk

Kumar

2024

IJSREM

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Stroke is a serious disease that can lead to serious consequences if not diagnosed and treated in time. The project focuses on using machine learning algorithms to analyze different types of data, including medical history, lifestyle and physical characteristics, to create powerful predictive models. Methods include preprocessing and data cleaning, product removal, and options to improve model accuracy. Various machine learning algorithms such as decision trees, support vector machines, and neural networks will be applied and compared to determine which is best for batting prediction [1]. The model will be trained with historical data and its performance will be evaluated with metrics such as demand, specificity and accuracy. The potential impact of this study includes early identification of individuals at high risk of stroke and provision of timely intervention and prevention. The development of effective predictive models can help reduce the burden of stroke-related morbidity and mortality. By combining architectural models with health applications, the program demonstrates the collaboration of today's technology in solving important health problems. Key Words: Brain Strokes, Early Detection, Predictive Model, Machine Learning Techniques, Medical Histories, Lifestyle Factors, Physiological Parameters, Preprocessing, Feature Extraction, Feature Selection, ML Algorithms, Healthcare Applications, Engineering Principles, Interdisciplinary Approach.

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“…Pandas: a useful library for data management and analysis [11]. It provides data structures such as Data Frame to simplify process and advance data structure.…”

Section: Data Analysis and Visualization Tools (Pandas Numpy Matplotlib)mentioning

confidence: 99%

Promoting Brain Health Through Machine Learning to Predict Stroke Risk

Kumar

2024

IJSREM

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“…However, it should also be noted that the ensemble models can be more complex and harder to interpret than individual models. Some examples to guide the application of ensemble learning in chemical toxicity prediction can be found in previous studies. − In addition, most ML models still rely on experimentally measured features (incapable of predicting the properties of materials not yet synthesized, or for virtual screening) and traditional small-molecule-specific theoretical descriptors (Figure D). Given the weaknesses in current nanodescriptors, there is an urgent need to develop more advanced nanospecific descriptors in the future.…”

Section: Unraveling Quantitative Nanostructure–toxicity Relationships...mentioning

confidence: 99%

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

et al. 2023

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Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)–toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

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