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EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit
Published 2025“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”
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The results of ICA performed using PyNoetic.
Published 2025“…For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. …”
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List of Abbreviations
Published 2025“…For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. …”
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S1 Graphical abstract -
Published 2025“…Currently, multiple EHT platforms are used by both industry and academia for different applications, such as drug discovery, disease modelling, and fundamental research. …”
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Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making
Published 2025“…In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. …”
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Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.
Published 2025“…The analysis was conducted in a Jupyter Notebook environment, using Python and libraries such as Scikit-learn and Pandas. …”
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Mechanomics Code - JVT
Published 2025“…At the beginning of the code, there is a help section that explains how to use it.<br></li><li>Python (written by Syed Shafat Ali and tested by Yan Ge): analogous functions of the MATLAB folder. …”
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Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100)
Published 2025“…The corrected bias functions were then applied to adjust the years (2020–2100) of daily rainfall data using the "ibicus" package, an open-source Python tool for bias adjustment and climate model evaluation. …”
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Landscape17
Published 2025“…We validated the convergence, grid, and spin settings against published data from rMD17, using the appropriate functional and basis set: PBE/def2-SVP. …”
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Code
Published 2025“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”
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Core data
Published 2025“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”
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Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation
Published 2025“…We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. …”