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Cover for Machine Learning in Organic Chemistry
Published:
10 March 2023
by MDPI
in MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
congress CHEMBIO.MOL-09: Org. Chem., Med. Chem., Mol. Biol., & Pharm. Industry Congress, Paris, France-Fargo, USA, 2023.
https://doi.org/10.3390/mol2net-09-14142
(registering DOI)
Abstract:
Synthesis of organic molecules is one of the most essential tasks in organic chemistry. The standard methodology started by a chemist solving a problem centered on experience, heuristics, and rules of thumb. Generally, experimentalists often work backward, starting with the molecule desired design and then analyzing the retrosynthesis in which readily available reagents and sequences of reactions could be used to produce it. All this his process is time-consuming and source- consuming, it can result in non-optimized solutions or even failure in finding reaction pathways because of human errors. In this sense, AI/ML (Artificial Intelligence/Machine Learning) is gaining more and more attention in organic chemistry because it can speed up this process. In this mini-Review provided a guide map to review the digitalization and computerization of organic chemistry principles.
Keywords: Organic Chemistry; Machine Learning; Artificial Intelligence; Data-driven Research
Comments on this paper
Ajit Singh
27 December 2023
The paper titled "Cover for Machine Learning in Organic Chemistry" presents a compelling exploration into the integration of Artificial Intelligence (AI) and Machine Learning (ML) in the field of organic chemistry. The abstract aptly highlights the challenges faced by traditional methodologies, emphasizing the time and resource-intensive nature of synthesizing organic molecules.
The paper effectively communicates the shift from manual, experience-based approaches to a more efficient and automated process driven by AI and ML. By focusing on the retrosynthetic analysis, the authors address a crucial aspect of organic chemistry, where the design of molecules often requires intricate planning. The acknowledgment of potential human errors in the traditional approach adds weight to the argument for adopting AI/ML solutions.
ChatGPT 3.5
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Write a review for a research paper having a title as Cover for Machine Learning in Organic Chemistry
Abstract: Synthesis of organic molecules is one of the most essential tasks in organic chemistry. The standard methodology started by a chemist solving a problem centered on experience, heuristics, and rules of thumb. Generally, experimentalists often work backward, starting with the molecule desired design and then analyzing the retrosynthesis in which readily available reagents and sequences of reactions could be used to produce it. All this his process is time-consuming and source- consuming, it can result in non-optimized solutions or even failure in finding reaction pathways because of human errors. In this sense, AI/ML (Artificial Intelligence/Machine Learning) is gaining more and more attention in organic chemistry because it can speed up this process. In this mini-Review provided a guide map to review the digitalization and computerization of organic chemistry principles.
ChatGPT
Title: Cover for Machine Learning in Organic Chemistry
Review:
The paper titled "Cover for Machine Learning in Organic Chemistry" presents a compelling exploration into the integration of Artificial Intelligence (AI) and Machine Learning (ML) in the field of organic chemistry. The abstract aptly highlights the challenges faced by traditional methodologies, emphasizing the time and resource-intensive nature of synthesizing organic molecules.
The paper effectively communicates the shift from manual, experience-based approaches to a more efficient and automated process driven by AI and ML. By focusing on the retrosynthetic analysis, the authors address a crucial aspect of organic chemistry, where the design of molecules often requires intricate planning. The acknowledgment of potential human errors in the traditional approach adds weight to the argument for adopting AI/ML solutions.
The mini-Review serves as a valuable guide, providing a roadmap for the digitalization and computerization of organic chemistry principles. The emphasis on optimization and avoiding non-optimized solutions aligns with the broader trend in scientific research towards leveraging technology for precision and efficiency.
One commendable aspect of the paper is its clear articulation of the benefits that AI/ML can bring to organic chemistry, specifically in accelerating the synthesis process. However, it would be beneficial if the paper delves deeper into specific case studies or examples to illustrate the practical applications of AI/ML in overcoming challenges highlighted in the abstract.
In conclusion, "Cover for Machine Learning in Organic Chemistry" makes a significant contribution to the discourse on the application of AI/ML in the synthesis of organic molecules. It provides a concise yet informative overview and sets the stage for further exploration into the transformative potential of cutting-edge technologies in the realm of organic chemistry.
The paper effectively communicates the shift from manual, experience-based approaches to a more efficient and automated process driven by AI and ML. By focusing on the retrosynthetic analysis, the authors address a crucial aspect of organic chemistry, where the design of molecules often requires intricate planning. The acknowledgment of potential human errors in the traditional approach adds weight to the argument for adopting AI/ML solutions.
ChatGPT 3.5
User
Write a review for a research paper having a title as Cover for Machine Learning in Organic Chemistry
Abstract: Synthesis of organic molecules is one of the most essential tasks in organic chemistry. The standard methodology started by a chemist solving a problem centered on experience, heuristics, and rules of thumb. Generally, experimentalists often work backward, starting with the molecule desired design and then analyzing the retrosynthesis in which readily available reagents and sequences of reactions could be used to produce it. All this his process is time-consuming and source- consuming, it can result in non-optimized solutions or even failure in finding reaction pathways because of human errors. In this sense, AI/ML (Artificial Intelligence/Machine Learning) is gaining more and more attention in organic chemistry because it can speed up this process. In this mini-Review provided a guide map to review the digitalization and computerization of organic chemistry principles.
ChatGPT
Title: Cover for Machine Learning in Organic Chemistry
Review:
The paper titled "Cover for Machine Learning in Organic Chemistry" presents a compelling exploration into the integration of Artificial Intelligence (AI) and Machine Learning (ML) in the field of organic chemistry. The abstract aptly highlights the challenges faced by traditional methodologies, emphasizing the time and resource-intensive nature of synthesizing organic molecules.
The paper effectively communicates the shift from manual, experience-based approaches to a more efficient and automated process driven by AI and ML. By focusing on the retrosynthetic analysis, the authors address a crucial aspect of organic chemistry, where the design of molecules often requires intricate planning. The acknowledgment of potential human errors in the traditional approach adds weight to the argument for adopting AI/ML solutions.
The mini-Review serves as a valuable guide, providing a roadmap for the digitalization and computerization of organic chemistry principles. The emphasis on optimization and avoiding non-optimized solutions aligns with the broader trend in scientific research towards leveraging technology for precision and efficiency.
One commendable aspect of the paper is its clear articulation of the benefits that AI/ML can bring to organic chemistry, specifically in accelerating the synthesis process. However, it would be beneficial if the paper delves deeper into specific case studies or examples to illustrate the practical applications of AI/ML in overcoming challenges highlighted in the abstract.
In conclusion, "Cover for Machine Learning in Organic Chemistry" makes a significant contribution to the discourse on the application of AI/ML in the synthesis of organic molecules. It provides a concise yet informative overview and sets the stage for further exploration into the transformative potential of cutting-edge technologies in the realm of organic chemistry.
