Transformative Applications of Artificial Intelligence in Modern Chemistry
Dwivedi, Aarti, Dwivedi, Vinay (2024). Transformative Applications of Artificial Intelligence in Modern Chemistry. oai:ojs2.pubs.rsyn.org:article/48
Dwivedi, Aarti, Dwivedi, Vinay. "Transformative Applications of Artificial Intelligence in Modern Chemistry.".
Dwivedi, Aarti, Dwivedi, Vinay. "Transformative Applications of Artificial Intelligence in Modern Chemistry.".
@article{dwivedi2024transformative,
title = {Transformative Applications of Artificial Intelligence in Modern Chemistry},
author = {Dwivedi, Aarti and Dwivedi, Vinay},
year = 2024,
journal = {},
url = oai:ojs2.pubs.rsyn.org:article/48,
language = en
}
TY - JOUR TI - Transformative Applications of Artificial Intelligence in Modern Chemistry AU - Dwivedi, Aarti AU - Dwivedi, Vinay PY - 2024 DA - 2024-10-10 UR - oai:ojs2.pubs.rsyn.org:article/48 AB - Artificial Intelligence (AI) is revolutionizing the field of chemistry, enabling breakthroughs in research and industrial applications through data-driven insights and computational power. One of the most impactful areas is drug discovery, where AI accelerates the identification of potential drug candidates by predicting molecular interactions, reducing the time and cost of traditional trial-and-error methods. In material design, AI aids in the development of novel materials, including catalysts and polymers, by predicting their properties and guiding synthesis. AI also plays a crucial role in molecular property prediction, where algorithms forecast attributes like solubility and reactivity based on molecular structures, streamlining the experimental process. Additionally, AI is utilized in chemical reaction prediction, allowing chemists to anticipate reaction outcomes and side products with high accuracy, which enhances synthesis efficiency. Automated synthesis planning is another key application, where AI suggests the most effective and economical reaction pathways for chemical production. In quantum chemistry, AI accelerates the calculation of molecular electronic structures, offering deeper insights into chemical behavior. The technology is also applied to environmental chemistry, where AI models predict pollutant behavior and optimize remediation efforts. Furthermore, AI facilitates spectroscopic data analysis by automating the interpretation of complex data, such as NMR and IR spectra, improving structural identification. In terms of safety, AI contributes to toxicology and safety assessments by predicting the toxicity of chemicals, minimizing the need for animal testing. Finally, AI enables the creation of personalized chemical solutions, tailoring products like pharmaceuticals and cosmetics to individual needs. These applications highlight AI's transformative potential in advancing both academic research and industrial chemistry. LA - en ER -
🤖 AI Key Takeaways & Research Insights
Automated AI Analysis💡 Core Finding
Artificial Intelligence (AI) is revolutionizing the field of chemistry, enabling breakthroughs in research and industrial applications through data-driven insights and computational power.
🔬 Methodology
One of the most impactful areas is drug discovery, where AI accelerates the identification of potential drug candidates by predicting molecular interactions, reducing the time and cost of traditional...
🎯 Domain Impact
Abstract
Artificial Intelligence (AI) is revolutionizing the field of chemistry, enabling breakthroughs in research and industrial applications through data-driven insights and computational power. One of the most impactful areas is drug discovery, where AI accelerates the identification of potential drug candidates by predicting molecular interactions, reducing the time and cost of traditional trial-and-error methods. In material design, AI aids in the development of novel materials, including catalysts and polymers, by predicting their properties and guiding synthesis. AI also plays a crucial role in molecular property prediction, where algorithms forecast attributes like solubility and reactivity based on molecular structures, streamlining the experimental process. Additionally, AI is utilized in chemical reaction prediction, allowing chemists to anticipate reaction outcomes and side products with high accuracy, which enhances synthesis efficiency. Automated synthesis planning is another key application, where AI suggests the most effective and economical reaction pathways for chemical production. In quantum chemistry, AI accelerates the calculation of molecular electronic structures, offering deeper insights into chemical behavior. The technology is also applied to environmental chemistry, where AI models predict pollutant behavior and optimize remediation efforts. Furthermore, AI facilitates spectroscopic data analysis by automating the interpretation of complex data, such as NMR and IR spectra, improving structural identification. In terms of safety, AI contributes to toxicology and safety assessments by predicting the toxicity of chemicals, minimizing the need for animal testing. Finally, AI enables the creation of personalized chemical solutions, tailoring products like pharmaceuticals and cosmetics to individual needs. These applications highlight AI's transformative potential in advancing both academic research and industrial chemistry.
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