ChatGPT Generated Content and Similarity Index in Chemistry

Kirtania, Deep Kumar

doi:10.1021/acs.jcim.3c01110

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…They collected 920 pieces of data for model establishment by humans, which was a substantial task . With the emergence of large language models (LLMs), such as ChatGPT, we wondered whether ChatGPT could assist humans in collecting data from a vast number of papers. In addition, could we improve the prediction accuracy by optimizing ML models?…”

Section: Introductioncontrasting

confidence: 66%

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities

Sun,

Hu,

Yang

et al. 2024

J. Chem. Inf. Model.

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The design of nanozymes with superior catalytic activities is a prerequisite for broadening their biomedical applications. Previous studies have exerted significant effort in theoretical calculation and experimental trials for enhancing the catalytic activity of nanozyme. Machine learning (ML) provides a forward-looking aid in predicting nanozyme catalytic activity. However, this requires a significant amount of human effort for data collection. In addition, the prediction accuracy urgently needs to be improved. Herein, we demonstrate that ChatGPT can collaborate with humans to efficiently collect data. We establish four qualitative models (random forest (RF), decision tree (DT), adaboost random forest (adaboost-RF), and adaboost decision tree (adaboost-DT)) for predicting nanozyme catalytic types, such as peroxidase, oxidase, catalase, superoxide dismutase, and glutathione peroxidase. Furthermore, we use five quantitative models (random forest (RF), decision tree (DT), Support Vector Regression (SVR), gradient boosting regression (GBR), and fully connected deep neuron network (DNN)) to predict nanozyme catalytic activities. We find that GBR model demonstrates superior prediction performance for nanozyme catalytic activities (R 2 = 0.6476 for Km and R 2 = 0.95 for Kcat). Moreover, an open-access web resource, AI-ZYMES, with a ChatGPT-based nanozyme copilot is developed for predicting nanozyme catalytic types and activities and guiding the synthesis of nanozyme. The accuracy of the nanozyme copilot's responses reaches more than 90% through the retrieval augmented generation. This study provides a new potential application for ChatGPT in the field of nanozymes.

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Section: Introductioncontrasting

confidence: 66%

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities

Sun,

Hu,

Yang

et al. 2024

J. Chem. Inf. Model.

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show abstract

“…The outsourcing of such efforts negates the effectiveness of the assessment and is antithetical to learning goals of the classes. Efforts are underway in using tools like similarity analyses (e.g., Turnitin) on open response exam questions, lab reports, and essays to detect and deter such usage. These methods analyze similarities in text as well as other parameters like sentence length, word/phrase use, etc.…”

Section: Introductionmentioning

confidence: 99%

Identifying Generative Artificial Intelligence Chatbot Use on Multiple-Choice, General Chemistry Exams Using Rasch Analysis

Sorenson,

Hanson

2024

J. Chem. Educ.

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Generative artificial intelligence (AI) technology is expected to have a profound impact on chemical education. While there are certainly positive uses, some of which are being actively implemented even now, there is a reasonable concern about its use in cheating. Efforts are underway to detect generative AI usage on open-ended questions, lab reports, and essays, but its detection on multiple choice exams is largely unexplored. Here we propose the use of Rasch analysis to identify the unique behavioral pattern of ChatGPT on General Chemistry II, multiple choice exams. While raw statistics (e.g., average, ability, outfit) were insufficient to readily identify ChatGPT instances, a strategy of fixing the ability scale on high success questions and then refitting the outcomes dramatically enhanced its outlier behavior in terms of Z-standardized out-fit statistic and ability displacement. Setting the detection threshold to a true positive rate (TPR) of 1.0, a false positive rate (FPR) of <0.1 was obtained across a majority of the 20 exams investigated here. Furthermore, the receiver operating characteristic curve (i.e., FPR vs TPR) exhibited outstanding areas under the curve of >0.9 for nearly all exams. While limitations of this method are described and the analysis is by no means exhaustive, these outcomes suggest that the unique behavior patterns of generative AI chat bots can be identified using Rasch modeling and fit statistics.

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“…A series of articles in this collection focused on the prediction of the physicochemical characteristics of chemical compounds, and these characteristics included: temperature-dependent viscosity, solvation Gibbs energies, p K a, metal coordination geometry, binding energy, and electronic property. − Another set of papers focused on molecular generation and design by introducing software/tool, , developing transformer-based new algorithms, and optimizing molecule via molecular scaffold decoration . The remaining tested the performance of ChatGPT in chemical generation and similarity indexing …”

mentioning

confidence: 99%

Editorial: Machine Learning in Bio-cheminformatics

Merz,

Wei,

Zhu

2024

J. Chem. Inf. Model.

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ChatGPT Generated Content and Similarity Index in Chemistry

Cited by 7 publications

References 14 publications

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities

Identifying Generative Artificial Intelligence Chatbot Use on Multiple-Choice, General Chemistry Exams Using Rasch Analysis

Editorial: Machine Learning in Bio-cheminformatics

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