Data Science in Chemistry

Gressling, Thorsten

doi:10.1515/9783110629453

Cited by 15 publications

(7 citation statements)

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“…This approach harnesses the power of computers to model reactions, design new molecules, and explore the vast chemical space more efficiently than traditional experimental methods alone. Digital chemistry encompasses several key areas, including computational chemistry, [22] cheminformatics, [23][24] [25] molecular modeling, [26] virtual screening, [27] machine learning, [28] artificial intelligence applications, [29] quantum computing, and dedicated software and devices such as electronic lab notebooks and robotics, respectively. [30] It aims to accelerate discovery and innovation in various fields, such as drug development, material science, and sustainable chemistry, by providing insights that are difficult, if not impossible, to obtain through conventional laboratory experiments.…”

Section: Definitionmentioning

confidence: 99%

“…Coupling this with digital materials science, for example, with nano and micro 3D prints of materials that require digital blueprints, [105] further expands the potential for designing novel materials with tailored properties, revolutionizing fields from sustainable energy to advanced manufacturing through predictive modeling and high-throughput computational experiments. Branching out digitalization -computer-aided chemistry [106] and synthesis planning (CASP), [107] pattern recognition in chemistry, [108] virtual chemistry labs, [81b] digital sustainable chemistry, [109] digital synthetic electrochemistry, [110] digital twins [23,111] curiosity-driven discovery, [112] iterative chemical synthesizers, [113] data-driven discovery, [114] digitization of synthesis and chemical programming, [115] Natural Language Processing (NLP), [116] Chemical graph theory, [117] computerbased automatic indexing, [118] Neural Networks in Chemistry and Drug Design, [10], [119] Chemometrics, [120] and chemical 'Oracles' [121] are other terms relevant to digital chemistry.…”

Section: Embedding Digital Chemistry In Traditional Chemistry and Sci...mentioning

confidence: 99%

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Digital Chemistry: Navigating the Confluence of Computation and Experimentation – Definition, Status Quo, and Future Perspective

Bräse

2024

Preprint

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Digital chemistry represents a transformative approach integrating computational methods, digital data, and automation within the chemical sciences. It is defined by the use of digital tools and algorithms to simulate, predict, accelerate, and analyze chemical processes and properties, augmenting traditional experimental methods. The current status quo of digital chemistry is marked by rapid advancements in several key areas: high-throughput screening, miniaturization, machine learning models, quantum chemistry, and laboratory automation. These technologies have enabled unprecedented speeds in discovering and optimizing new molecules, materials, and reactions. Retrosynthesis and structure-active prediction tools have supported these endeavors. Furthermore, integrating large-language models and robotics in chemistry labs (self-driving labs) has begun to automate routine tasks and complex decision-making processes. Looking forward, the future of digital and digitalized chemistry is poised for significant growth, driven by the increasing accessibility of computational resources, the expansion of chemical databases, and the refinement of artificial intelligence algorithms. This evolution promises to accelerate innovation in drug discovery, material science, and sustainable manufacturing, ultimately leading to more efficient, cost-effective, and environmentally friendly chemical research and production. The challenge lies in advancing the technology itself, fostering interdisciplinary collaboration, and ensuring the ethical use of digital tools in chemical research.

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Section: Definitionmentioning

confidence: 99%

Section: Embedding Digital Chemistry In Traditional Chemistry and Sci...mentioning

confidence: 99%

Digital Chemistry: Navigating the Confluence of Computation and Experimentation – Definition, Status Quo, and Future Perspective

Bräse

2024

Preprint

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“…In this work, we present the open-source Python project MOCCA (Multivariate Online Contextual Chromatographic Analysis), which enables the direct processing and analysis of HPLC–DAD raw data in Python, the de facto standard programming/scripting language for data science projects in chemistry. − As a ready-to-use Python package, it is easily implemented into existing automated and nonautomated workflows. By making the Pythonic library of data analysis toolkits accessible, MOCCA enables its users to develop new and powerful data analysis features.…”

Section: Introductionmentioning

confidence: 99%

Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening

et al. 2023

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Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors' hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC−DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine of known signals, even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA's data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction demonstrating MOCCA's peak deconvolution feature; (iii) a closed-loop optimization study for the alkylation of 2-pyridone without human control during data analysis; (iv) a well plate screening of categorical reaction parameters for a novel palladium-catalyzed cyanation of aryl halides employing O-protected cyanohydrins. By publishing MOCCA as a Python package with this work, we envision an opensource community project for chromatographic data analysis with the potential of further advancing its scope and capabilities.

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“…32 In this work, we present the open-source Python project MOCCA (Multivariate Online Contextual Chromatographic Analysis), which enables the direct processing and analysis of HPLC-DAD raw data in Python, the de-facto standard programming/scripting language for data science projects in chemistry. [33][34][35][36] As a ready-to-use Python package, it is easily implemented into existing automated and non-automated workflows. By making the Pythonic library of data analysis toolkits accessible, MOCCA enables its users to develop new and powerful data analysis features.…”

Section: Introductionmentioning

confidence: 99%

Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening

Haas

Lübbesmeyer

Jin

et al. 2022

Preprint

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Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors’ hardware and software components limiting their potential in automated workflows and contradicting to FAIR data principles (findability, accessibility, interoperability, reuse), which enable chemometrics and data science applications. In this work, we present an open-source Python project called MOCCA (Multivariate Online Contextual Chromatographic Analysis) for the analysis of open-format HPLC–DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features including a peak deconvolution routine which allows for automated deconvolution of known signals even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA’s data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction demonstrating MOCCA’s peak deconvolution feature; (iii) a closed-loop optimization study for the alkylation of 2-pyridone highlighting MOCCA’s potential to obviate the need for human control during data analysis; (iv) a well plate screening of categorical reaction parameters for a novel palladium-catalyzed cyanation of aryl halides employing O-protected cyanohydrins where MOCCA tracks all known and unknown signals. These studies emphasize how MOCCA enables its users to make data-based decisions in synthesis workflows with different degrees of automation by providing actionable analytics. By publishing MOCCA as a Python package together with this work, we envision an open-source community project for chromatographic data analysis with the potential of further advancing its scope and capabilities

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Data Science in Chemistry

Cited by 15 publications

References 0 publications

Digital Chemistry: Navigating the Confluence of Computation and Experimentation – Definition, Status Quo, and Future Perspective

Digital Chemistry: Navigating the Confluence of Computation and Experimentation – Definition, Status Quo, and Future Perspective

Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening

Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening

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