[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123291-en":3,"doc-seo-123291-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123291,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Exploring the Synergistic Potential of Artificial Intelligence and Machine Learning in Chemistry - Review and Applications","Machine learning (ML) and artificial intelligence (AI) increasingly reshape chemical research by transforming standard workflows for data analysis and molecular design, alongside reliable property forecasting. This review surveys key applications of AI and ML in drug discovery, chemical synthesis, material science, and computational chemistry. It highlights the use of AI algorithms in quantum-chemistry simulations and faster reaction-rate prediction, as well as AI-driven robotic synthesis and improved structure identification. The discussion covers benefits, limitations, and integration risks, emphasizing interdisciplinary collaboration and data sharing.","Exploring the Synergistic Potential of Artificial Intelligence and  \nMachine Learning in Chemistry  \nShyamal Mondal *  \nDepartment of Chemistry, New Alipore College, L-Block, New Alipore, Kolkata, West Bengal, India-  \n700053  \nDate Received: 24-10-2024 Date Accepted: 25-12-2024  \nAbstract  \nMachine learning (ML) and artificial intelligence (AI) have become specialists in different areas of chemistry. These technologies help to change the standard approaches to data analysis and molecular design along with the properties forecast. This review describes the interesting applications and increasing potential of AI and ML , specifically in drug discovery, chemical synthesis, material science, and computational chemistry. Computationally, the focus was on the application of AI algorithms to quantum chemistry simulations to predict properties of elements within a molecule, or possible reactions of molecules at a rate that would not have been possible manually. Moreover, AI-driven robotic synthesis platforms and experimental techniques have become less labor-intensive. The methods used for the identification of new chemical structures have improved in terms of speed. The benefits and the limitations of integrating AI, as well as the opportunities, are discussed in detail. In this review, it is also reiterated that there are risks that come with the integration of ML in chemistry and how interdisciplinary collaboration and data sharing are crucial to advancing in this field. In a single summary, this review demonstrates how the use of AI and ML can and will expand the horizons of chemical science and discovery.  \nKeywords: Artificial Intelligence, Machine Learning, Drug Discovery, Chemical Synthesis, Materials Science, Computational Chemistry.  \nIntroduction  \nArtificial Intelligence and Machine Learning are rapidly developing tools that have become the core of numerous fundamental and technical disciplines, among which chemistry is one of the most important and promising disciplines (Cun et al., 2015) . The combination of these two approaches has stimulated abilities for unprecedented innovation. These methods can also be applied in data mining, molecular simulations, and property estimation, significantly influencing drug design, materials science, and chemical manufacturing (Goh et al., 2017) . Thus, this introduction aims to familiarize the reader with the importance of AI and ML in revolutionizing chemistry, as well as their underlying concepts, methods, and applications (Schneider et al., 2018) .  \nThe history of AI can be traced back to the mid-twentieth century, with prominent figures such as Alan Turing and John McCarthy (Butler et al., 2018) . However, significant progress in the field was not achieved until recent decades, driven by advancements in computational power, algorithms, and data availability (Coley et al., 2019) . At the same time, the growth rate of the field of Machine Learning, an AI subfield accelerated. Concurrently, the rapid growth of machine learning, a subfield of AI, has been fueled by improvements in statistical modeling, optimization techniques, and neural networks (Segler et al., 2017) .  \nComputational methods have been employed in chemistry since the advent of computers in the mid-twentieth century (Kuhn et al., 2013) . Initially, these methods were primarily used to solve quantum mechanical problems to determine molecular structures and properties. Of late, the field of computational chemistry has broadened in line with the emergence of access to high performance computing and the implementation of Artificial Intelligence and Machine Learning (Lo et al., 2018) . Today AI and ML are part of numerous aspects of chemical research, providing unparalleled opportunities for big data analysis as well as computer-aided molecular design and modeling (Coleyet al., 2019) .The primary applications of AI and ML in the field of chemistry are summarized in Table 1 (Schneider et al., 2018; Noé et al., 2020; Butle","cbCaipoDL7LYRo1E","https://ap.wps.com/l/cbCaipoDL7LYRo1E","pdf",628970,1,18,"English","en",105,"# Introduction\n## AI and ML in Chemistry\n## Evolution of AI and ML\n## Computational Chemistry and Current Applications\n## Applications Overview (Table 1)","[{\"question\":\"How do AI and ML contribute to chemistry research, according to the document?\",\"answer\":\"They support data mining, molecular simulations, and property estimation, enabling better drug design, materials science progress, and improved chemical manufacturing. They also expand chemical research through big-data analysis and computer-aided molecular modeling.\"},{\"question\":\"Which main areas does the review focus on for AI and ML in chemistry?\",\"answer\":\"The review emphasizes four aspects: drug discovery, synthetic chemistry, material science, and computational chemistry, summarizing how AI and ML are used in each area.\"},{\"question\":\"What benefits and limitations of integrating AI/ML into chemistry are discussed?\",\"answer\":\"The review describes advantages such as faster identification of new structures, more efficient data-driven prediction, and less labor-intensive robotic synthesis. It also discusses limitations and risks, stressing the importance of interdisciplinary collaboration and data sharing.\"}]","Exploring the Synergistic Potential of Artificial Intelligence and Machine Learning in Chemistry - Review and Applications | PDF",1785815776,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"exploring-the-synergistic-potential-of-artificial-intelligence-and-machine-learning-in-chemistry-review-and-applications","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/exploring-the-synergistic-potential-of-artificial-intelligence-and-machine-learning-in-chemistry-review-and-applications/123291/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How do AI and ML contribute to chemistry research, according to the document?","Question",{"text":76,"@type":77},"They support data mining, molecular simulations, and property estimation, enabling better drug design, materials science progress, and improved chemical manufacturing. They also expand chemical research through big-data analysis and computer-aided molecular modeling.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which main areas does the review focus on for AI and ML in chemistry?",{"text":81,"@type":77},"The review emphasizes four aspects: drug discovery, synthetic chemistry, material science, and computational chemistry, summarizing how AI and ML are used in each area.",{"name":83,"@type":74,"acceptedAnswer":84},"What benefits and limitations of integrating AI/ML into chemistry are discussed?",{"text":85,"@type":77},"The review describes advantages such as faster identification of new structures, more efficient data-driven prediction, and less labor-intensive robotic synthesis. 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