CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space
Abstract Chemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recen...
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| Format: | Article |
| Language: | English |
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BMC
2024-05-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-024-00840-1 |
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| author | Christina Humer Rachel Nicholls Henry Heberle Moritz Heckmann Michael Pühringer Thomas Wolf Maximilian Lübbesmeyer Julian Heinrich Julius Hillenbrand Giulio Volpin Marc Streit |
| author_facet | Christina Humer Rachel Nicholls Henry Heberle Moritz Heckmann Michael Pühringer Thomas Wolf Maximilian Lübbesmeyer Julian Heinrich Julius Hillenbrand Giulio Volpin Marc Streit |
| author_sort | Christina Humer |
| collection | DOAJ |
| description | Abstract Chemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using artificial intelligence (AI) models to aid RO, another level of complexity has been added. Helping to assess the quality of a model’s prediction and understand its decision is critical to supporting human-AI collaboration and trust calibration. To address this, we propose CIME4R—an open-source interactive web application for analyzing RO data and AI predictions. CIME4R supports users in ( i ) comprehending a reaction parameter space, ( ii ) investigating how an RO process developed over iterations, ( iii ) identifying critical factors of a reaction, and ( iv ) understanding model predictions. This facilitates making informed decisions during the RO process and helps users to review a completed RO process, especially in AI-guided RO. CIME4R aids decision-making through the interaction between humans and AI by combining the strengths of expert experience and high computational precision. We developed and tested CIME4R with domain experts and verified its usefulness in three case studies. Using CIME4R the experts were able to produce valuable insights from past RO campaigns and to make informed decisions on which experiments to perform next. We believe that CIME4R is the beginning of an open-source community project with the potential to improve the workflow of scientists working in the reaction optimization domain. Scientific contribution To the best of our knowledge, CIME4R is the first open-source interactive web application tailored to the peculiar analysis requirements of reaction optimization (RO) campaigns. Due to the growing use of AI in RO, we developed CIME4R with a special focus on facilitating human-AI collaboration and understanding of AI models. We developed and evaluated CIME4R in collaboration with domain experts to verify its practical usefulness. |
| format | Article |
| id | doaj-art-7de8772a2d2947fca96eae02a399343a |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-7de8772a2d2947fca96eae02a399343a2025-01-26T12:50:02ZengBMCJournal of Cheminformatics1758-29462024-05-0116111910.1186/s13321-024-00840-1CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter spaceChristina Humer0Rachel Nicholls1Henry Heberle2Moritz Heckmann3Michael Pühringer4Thomas Wolf5Maximilian Lübbesmeyer6Julian Heinrich7Julius Hillenbrand8Giulio Volpin9Marc Streit10Johannes Kepler University LinzDivision Crop Science, Bayer AGDivision Crop Science, Bayer AGdatavisyn GmbHdatavisyn GmbHDivision Crop Science, Bayer AGDivision Crop Science, Bayer AGDivision Crop Science, Bayer AGDivision Pharmaceuticals, Bayer AGDivision Crop Science, Bayer AGJohannes Kepler University LinzAbstract Chemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using artificial intelligence (AI) models to aid RO, another level of complexity has been added. Helping to assess the quality of a model’s prediction and understand its decision is critical to supporting human-AI collaboration and trust calibration. To address this, we propose CIME4R—an open-source interactive web application for analyzing RO data and AI predictions. CIME4R supports users in ( i ) comprehending a reaction parameter space, ( ii ) investigating how an RO process developed over iterations, ( iii ) identifying critical factors of a reaction, and ( iv ) understanding model predictions. This facilitates making informed decisions during the RO process and helps users to review a completed RO process, especially in AI-guided RO. CIME4R aids decision-making through the interaction between humans and AI by combining the strengths of expert experience and high computational precision. We developed and tested CIME4R with domain experts and verified its usefulness in three case studies. Using CIME4R the experts were able to produce valuable insights from past RO campaigns and to make informed decisions on which experiments to perform next. We believe that CIME4R is the beginning of an open-source community project with the potential to improve the workflow of scientists working in the reaction optimization domain. Scientific contribution To the best of our knowledge, CIME4R is the first open-source interactive web application tailored to the peculiar analysis requirements of reaction optimization (RO) campaigns. Due to the growing use of AI in RO, we developed CIME4R with a special focus on facilitating human-AI collaboration and understanding of AI models. We developed and evaluated CIME4R in collaboration with domain experts to verify its practical usefulness.https://doi.org/10.1186/s13321-024-00840-1Reaction optimizationExplainable AIArtificial intelligenceBayesian optimizationInterpretable |
| spellingShingle | Christina Humer Rachel Nicholls Henry Heberle Moritz Heckmann Michael Pühringer Thomas Wolf Maximilian Lübbesmeyer Julian Heinrich Julius Hillenbrand Giulio Volpin Marc Streit CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space Journal of Cheminformatics Reaction optimization Explainable AI Artificial intelligence Bayesian optimization Interpretable |
| title | CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space |
| title_full | CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space |
| title_fullStr | CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space |
| title_full_unstemmed | CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space |
| title_short | CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space |
| title_sort | cime4r exploring iterative ai guided chemical reaction optimization campaigns in their parameter space |
| topic | Reaction optimization Explainable AI Artificial intelligence Bayesian optimization Interpretable |
| url | https://doi.org/10.1186/s13321-024-00840-1 |
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