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Journal articles on the topic 'AI in Discovery'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Laddha, C. S., A. V. Shelke, Y. V. Vaidya, A. A. Sheikh, and K. R. Biyani. "A Review on Artificial Intellegence in Drug Discovery & Pharmaceutical Industry." Asian Journal of Pharmaceutical Research and Development 11, no. 3 (2023): 45–51. http://dx.doi.org/10.22270/ajprd.v11i3.1252.

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Introduction: The use of artificial intelligence (AI) in drug discovery and the pharma industry has been rapidly expanding in recent years. AI algorithms can analyze vast amounts of data, identify patterns, and make predictions that can accelerate drug discovery and improve patient outcomes. Methods: AI is being used in various stages of the drug discovery process, from target identification and lead optimization to clinical trials and post-market surveillance. Machine learning algorithms, neural networks, and natural language processing are among the AI techniques used in drug discovery

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Tsunoyama, Kazuhisa. "AI and drug discovery." Proceedings for Annual Meeting of The Japanese Pharmacological Society 92 (2019): 3—CS4–1. http://dx.doi.org/10.1254/jpssuppl.92.0_3-cs4-1.

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Boomisha, S. D.* Jemmy Christy H. "AI in Drug Discovery." International Journal of Pharmaceutical Sciences 3, no. 2 (2025): 1800–1810. https://doi.org/10.5281/zenodo.14907864.

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Artificial intelligence (AI) is the technology and science of creating intelligent machines by using algorithms which the machine adheres to in order to mimic human cognitive functions like learning and problem solving. Artificial intelligence (AI) is the term used to describe computer programs that simulate the mechanisms that support the intellect of humans, including as engagement, deep learning, reasoning, adaptation, and sensory comprehension. It aims to mimic human cognitive functions. This article examines the prospective applications of AI in drug discovery, emphasizing significant dev

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Rama, Brahma Reddy D.* Malleswari K. Chetan M. Adarsh Babu B. Bhuvan Chandra Durga Eswar J. "A Review On Role Of Artificial Intelligence In Drug Discovery." International Journal of Pharmaceutical Sciences 2, no. 8 (2024): 2913–22. https://doi.org/10.5281/zenodo.13293031.

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Artificial intelligence accelerates the drug discovery and development process and reduces the cost, with enormous amounts of successful applications from language modeling to improvement in the pharmaceutical sector. The deep-learning approach has been used throughout the drug discovery steps as the drug-related data increase. In this mini-review, I gave a general description of AI and its application in drug discovery and development. Computer-aided drug discovery and ligand-based quantitative structure activity and property (QSAR/ QSPR) and De Novo drug design, integration with single cell

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Abhishek, Sahu* Prem Samundre Dr. Jitendra Banweer. "AI in Drug Discovery and Development." International Journal of Pharmaceutical Sciences 3, no. 5 (2025): 2510–15. https://doi.org/10.5281/zenodo.15426727.

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Artificial intelligence (AI) is revolutionizing the landscape of drug discovery and development by accelerating timelines, reducing costs, and improving the precision of therapeutic design. From target identification to lead optimization and clinical trial design, AI-driven approaches—such as machine learning, deep learning, and natural language processing—are enhancing the efficiency and predictive power of each stage in the pharmaceutical pipeline. This review explores the current applications of AI across the drug development lifecycle, with a focus on virtual screening, de novo

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Varun, Ahuja. "Artificial Intelligence (AI) in Drug Discovery and Medicine." Journal of Clinical Cases & Reports 2, no. 3 (2019): 76–80. http://dx.doi.org/10.46619/joccr.2019.2-1043.

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Artificial intelligence (AI) is a branch of computer science that deals with the development of algorithms that seek to simulate human intelligence. The phrase “artificial intelligence” was likely coined during a conference at Dartmouth College in 1956. The earliest work of medical AI dates back to the early 1970s. Over years, AI has found implications in various fields. In this article, we summarize its applications in drug discovery and medicine.

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LeCun, Yann. "Artificial Intelligence in Scientific Research: Transforming Data Analysis and Discovery." International Journal of Innovative Computer Science and IT Research 1, no. 01 (2025): 1–9. https://doi.org/10.63665/ijicsitr.v1i01.01.

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Artificial Intelligence (AI) has become a transformative tool in scientific research, reshaping traditional methodologies by enabling advanced data analysis, hypothesis testing, and predictive modeling. The integration of machine learning (ML), deep learning (DL), and natural language processing (NLP) has significantly accelerated discoveries in medicine, physics, chemistry, environmental science, and other disciplines. AI-driven technologies allow researchers to process large datasets, identify complex patterns, and generate predictive insights with unprecedented accuracy and speed. These inn

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Thomas, Dan. "Revolutionising Drug Discovery." ITNOW 65, no. 2 (2023): 62–63. http://dx.doi.org/10.1093/combul/bwad068.

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Beverley, Dr Charles. "Artificial Intelligence's Influence on HIV/AIDS Cure Discovery." Journal of Quality in Health Care & Economics 7, no. 1 (2024): 1–3. http://dx.doi.org/10.23880/jqhe-16000364.

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This comprehensive review explores the transformative role of artificial intelligence (AI) in advancing research towards finding a cure for HIV/AIDS. By analyzing a diverse array of peer- reviewed articles, the review investigates how AI is revolutionizing various aspects of HIV/AIDS research, including drug discovery, treatment optimization, vaccine development, understanding HIV pathogenesis, public health interventions, and ethical considerations. Furthermore, the review discusses current challenges, future research directions, and practical implications of integrating AI into HIV/AIDS rese

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Bai, Junwen, Yexiang Xue, Johan Bjorck, et al. "Phase Mapper: Accelerating Materials Discovery with AI." AI Magazine 39, no. 1 (2018): 15–26. http://dx.doi.org/10.1609/aimag.v39i1.2785.

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From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of th

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Hassabis, Demis. "Accelerating Scientific Discovery with AI." Keio Journal of Medicine 73, no. 4 (2024): 47. https://doi.org/10.2302/kjm.abstract_73_4-1.

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12

May, Mike. "AI Takes On Drug Discovery." Genetic Engineering & Biotechnology News 43, no. 6 (2023): 40–43. http://dx.doi.org/10.1089/gen.43.06.15.

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Puja L.Choundhe, Puja L. Choundhe, Payal A. Dhoke Payal A.Dhoke, Rutuja B. Deshpande Rutuja B. Deshpande, Praktan S. Dahekar Praktan S.Dahekar, and Tushar C. Dangare Tushar C. Dangare. "The Recent Advances in the Approach of Artificial Intelligencetowards Medicine Discovery." International Journal of Pharmaceutical Research and Applications 10, no. 3 (2025): 34–42. https://doi.org/10.35629/4494-10033442.

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Artificial intelligence (AI) has recently emerged as a transformative force in the field of medicine, playing a pivotal role in advancing drug discovery. AI offers groundbreaking opportunities for improving accuracy, efficiency, and innovation in this domain. With its integration into drug development processes, AI has the potential to revolutionize how new treatments are discovered and designed. Over recent years, the use of AI techniques in drug discovery has significantly expanded. Tools like combinatorial QSAR and QSPR models, virtual screening, and de novo drug design are gaining traction

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M., N. O. Sadiku, R. Nelatury S., and Musa S.M. "Artificial Intelligence in Industry." Journal of Scientific and Engineering Research 8, no. 2 (2021): 45–52. https://doi.org/10.5281/zenodo.10574946.

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<strong>Abstract </strong>Artificial intelligence (AI) refers to computer systems that mimic human cognitive functions. AI-driven systems can discover trends, reveal inefficiencies, and predict future outcomes.  These characteristics enable informed decision-making and AI to be potentially beneficial for many industries. Industrial AI deals with the application of AI technologies to address industrial issues such customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis, and insight discovery.  This paper provides different applications

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Ferguson*, David Joshua. "AI-Driven Retrosynthesis Framework for Drug Discovery: The Use of LLMs." Journal of Biomedical Research & Environmental Sciences 6, no. 5 (2024): 556–62. https://doi.org/10.37871/jbres2110.

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The process of retrosynthetic analysis, introduced by Corey, systematically deconstructs complex molecules into simpler precursors, providing a logical pathway for chemical synthesis. Here, we propose an innovative AI-driven retrosynthesis framework for drug discovery leveraging Large Language Models (LLMs) and advanced computational tools. This "retro drug discovery" platform integrates AlphaFold2-generated protein structures, MolGPT-driven scaffold generation, and a tailored ChatGPT model orchestrating Structure-Activity Relationship (SAR) analyses, virtual screening, and iterative optimizat

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Ankit Ujjwal. "The Integration of Artificial Intelligence in Drug Discovery and Development : Novel Approach." International Journal of Scientific Research in Science and Technology 11, no. 6 (2024): 228–37. http://dx.doi.org/10.32628/ijsrst24116175.

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The drug discovery and development process is complex, time-consuming, and costly. Artificial Intelligence (AI) has emerged as a transformative technology to improve efficiency, accuracy, and innovation in pharmaceutical research. This study explores the applications, benefits, and challenges of integrating AI in drug discovery and development. the role of AI in drug discovery, its transformative impact on pharmaceutical research, and the potential benefits and challenges. Briefly mention the major AI techniques used in different phases of drug discovery and development. The integration of Art

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Kanza, Samantha, Colin Leonard Bird, Mahesan Niranjan, William McNeill, and Jeremy Graham Frey. "The AI for Scientific Discovery Network+." Patterns 2, no. 1 (2021): 100162. http://dx.doi.org/10.1016/j.patter.2020.100162.

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18

Cyranoski, David. "AI drug discovery booms in China." Nature Biotechnology 39, no. 8 (2021): 900–902. http://dx.doi.org/10.1038/s41587-021-01016-0.

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19

Davies, Kevin. "Key Drivers of AI Drug Discovery." Genetic Engineering & Biotechnology News 44, no. 12 (2024): 18–20. https://doi.org/10.1089/gen.44.12.07.

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Gao, Shanghua, Ada Fang, Yepeng Huang, et al. "Empowering biomedical discovery with AI agents." Cell 187, no. 22 (2024): 6125–51. http://dx.doi.org/10.1016/j.cell.2024.09.022.

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21

Marcus, Alan. "Making AI Work in Drug Discovery." Genetic Engineering & Biotechnology News 44, no. 9 (2024): 27. http://dx.doi.org/10.1089/gen.44.09.11.

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Shen, Wei-Min. "Functional transformations in AI discovery systems." Artificial Intelligence 41, no. 3 (1990): 257–72. http://dx.doi.org/10.1016/0004-3702(90)90045-2.

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23

Okuno, Yasushi. "AI platform to accelerate drug discovery." Drug Metabolism and Pharmacokinetics 61 (June 2025): 101077. https://doi.org/10.1016/j.dmpk.2025.101077.

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24

Nilesh, Savale*. "Artificial Inteligence Used in Drug Discovery." International Journal of Pharmaceutical Sciences 3, no. 3 (2025): 138–47. https://doi.org/10.5281/zenodo.14961715.

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The traditional drug discovery process is time consuming, costly and often yields limited success. Recent advancements in artificial intelligence (AI) have transformed the landscape of drug discovery, enabling faster, more accurate, and cost-effective identification of potential therapeutic candidates. AI powered approaches, such as machine learning (ML) and deep learning (DL), are being employed to analyze vast amounts of biological and chemical data, predict molecular interactions, and optimize lead compounds. This abstract review the current state of AI in drug discovery, highlighting its a

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Nasim, Md, Xinghang Zhang, Anter El-Azab, and Yexiang Xue. "End-to-End Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23005–11. http://dx.doi.org/10.1609/aaai.v38i21.30342.

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The availability of tera-byte scale experiment data calls for AI driven approaches which automatically discover scientific models from data. Nonetheless, significant challenges present in AI-driven scientific discovery: (i) The annotation of large scale datasets requires fundamental re-thinking in developing scalable crowdsourcing tools. (ii) The learning of scientific models from data calls for innovations beyond black-box neural nets. (iii) Novel visualization & diagnosis tools are needed for the collaboration of experimental and theoretical physicists, and computer scientists. We presen

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Brusova, Olga, Margarita Corzo, and Michael J. Pyrcz. "Introduction to this special section: Machine learning and AI." Leading Edge 39, no. 10 (2020): 700. http://dx.doi.org/10.1190/tle39100700.1.

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There is an ongoing digital revolution motivated by the data-driven scientific discovery paradigm. Geophysics has a mixed level of readiness, with some of these technologies already applied, research still emerging, and new opportunities to be discovered.

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Decheng Huang, Mingxuan Yang, Xin Wen, Siwei Xia, and Bo Yuan. "AI-Driven drug discovery: Accelerating the development of novel therapeutics in biopharmaceuticals." International Medical Science Research Journal 4, no. 8 (2024): 882–99. http://dx.doi.org/10.51594/imsrj.v4i8.1458.

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Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing the biopharmaceutical industry's approach to developing novel therapeutics. This paper provides a comprehensive overview of AI-driven drug discovery, focusing on its applications in accelerating the development of innovative treatments. We examine the fundamental AI technologies employed in drug discovery, including machine learning algorithms, deep learning architectures, and natural language processing techniques. The paper analyzes the integration of AI across various stages of the drug dis

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Xu, Wei, Jiajun Shen, Han Chen, et al. "P‐104: Graph‐Based AI Workflow for OLED Materials Discovery." SID Symposium Digest of Technical Papers 54, no. 1 (2023): 1571–74. http://dx.doi.org/10.1002/sdtp.16893.

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Artificial Intelligence (AI) is becoming an emerging technique in scientific research including novel materials discovery. In this work, we present a novel graph‐based AI workflow for discovering Organic Light‐Emitting Diode (OLED) materials. This workflow contains two graph‐based AI models: a molecular structure generative model and a molecular property predictive model. The target materials here are Red‐Prime (RP) materials, which are widely used to pair with the red light emitters in OLED devices. Based on the desired properties required by our OLED devices, we apply the AI‐based workflow t

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Reddy, Chandan K., and Parshin Shojaee. "Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28601–9. https://doi.org/10.1609/aaai.v39i27.35084.

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Scientific discovery is a complex cognitive process that has driven human knowledge and technological progress for centuries. While artificial intelligence (AI) has made significant advances in automating aspects of scientific reasoning, simulation, and experimentation, we still lack integrated AI systems capable of performing autonomous long-term scientific research and discovery. This paper examines the current state of AI for scientific discovery, highlighting recent progress in large language models and other AI techniques applied to scientific tasks. We then outline key challenges and pro

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Huang, Decheng, Mingxuan Yang, Xin Wen, Siwei Xia, and Bo Yuan. "AI-Driven Drug Discovery: Accelerating the Development of Novel Therapeutics in Biopharmaceuticals." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 3, no. 3 (2024): 206–24. http://dx.doi.org/10.60087/jklst.vol3.n3.p.206-224.

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Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing the biopharmaceutical industry's approach to developing novel therapeutics. This paper provides a comprehensive overview of AI-driven drug discovery, focusing on its applications in accelerating the development of innovative treatments. We examine the fundamental AI technologies employed in drug discovery, including machine learning algorithms, deep learning architectures, and natural language processing techniques. The paper analyzes the integration of AI across various stages of the drug dis

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Alzamer, Haneen, Russlan Jaafreh, Jung-Gu Kim, and Kotiba Hamad. "Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery." Crystals 15, no. 2 (2025): 114. https://doi.org/10.3390/cryst15020114.

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Recent advancements in artificial intelligence (AI), particularly in algorithms and computing power, have led to the widespread adoption of AI techniques in various scientific and engineering disciplines. Among these, materials science has seen a significant transformation due to the availability of vast datasets, through which AI techniques, such as machine learning (ML) and deep learning (DL), can solve complex problems. One area where AI is proving to be highly impactful is in the design of high-performance Li-ion batteries (LIBs). The ability to accelerate the discovery of new materials wi

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Mane, Sanvidha A., Ravindra L. Bakal, and Pooja R. Hatwar. "Artificial Intelligence in Pharmaceutical Research." Journal of Drug Delivery and Therapeutics 15, no. 6 (2025): 260–67. https://doi.org/10.22270/jddt.v15i6.7234.

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Artificial intelligence (AI) is transforming the pharmaceutical industry by accelerating medication development and discovery. AI technologies, including machine learning and deep learning, are being applied in various areas, such as drug design, target discovery, preclinical research, and personalized medicine. AI can analyze vast amounts of data, identify patterns, and make predictions, thereby improving the efficiency and effectiveness of the drug development process. This review highlights the applications of AI in pharmaceutical research, including drug discovery, target identification, a

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Rajabi Dezfooli, Helia, Adib Dashtizadeh, Arman Esmaeily, and Shiva Baradaranaghili. "THE VITAL ROLE OF ARTIFICIAL INTELLIGENCE IN ACCELERATING THE DISCOVERY AND DEVELOPMENT OF ANTIBIOTICS." International Journal of Advanced Research 12, no. 10 (2024): 1223–33. http://dx.doi.org/10.21474/ijar01/19747.

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Background: Artificial intelligence (AI) has the potential to revolutionize antibiotic discovery. By automating and accelerating various stages of the drug discovery process, AI can help address the urgent need for new antibiotics to combat rising antimicrobial resistance.AI can be used to analyze vast amounts of data, AI algorithms can process and analyze large datasets to identify patterns and trends that may be relevant to antibiotic discovery. Predict molecular properties, design novel antibiotics and optimize drug development. By leveraging AI, researchers can expedite the discovery of no

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Somnath, S. Davkhar* Devadhe Sanika S. Khedkar Namdev Bhagwan. "A Review Of Artificial Intelligence In Drug Discovery And Development." International Journal of Pharmaceutical Sciences 2, no. 3 (2024): 7–22. https://doi.org/10.5281/zenodo.10774652.

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From initial molecule discovery to bringing a new drug to market, it takes at least 10 years to complete the process of making a drug, costing approximately 2.5billion dollars. Over a past decade, Artificial Intelligence (AI) have brought ease to the process of developing a drug and helping companies save time and money.AI tools are revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry. AI has recently started to gear up its application in various pharmaceutical sectors viz., drug discovery, drug repu

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Kundu, Subhasis. "AI-Driven Copilot: Revolutionizing Scientific Discovery and Innovation through Self-Learning Systems." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 07 (2023): 1–7. https://doi.org/10.55041/ijsrem24893.

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This paper investigates the transformative impact of AI-powered copilots on scientific research and innovation. It explores how self-learning systems possess the potential to revolutionize hypothesis generation, experimental design, and data analysis across various scientific disciplines. The study examines the integration of autonomous AI agents into research workflows, emphasizing their ability to accelerate discovery processes and enhance the capabilities of human researchers. This paper explores the challenges and ethical implications of using AI copilots in scientific research. It present

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NVVS Vinayak Lingolu, Deena Kumari M, and Pavan Kumar VLSS. "Evaluating the Impact of AI and ML on Modern Drug Discovery." Journal of Pharma Insights and Research 2, no. 4 (2024): 067–72. http://dx.doi.org/10.69613/8tckqp18.

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Artificial intelligence has several effective applications, ranging from language modelling to pharmaceutical sector enhancement, and it speeds up and lowers the cost of medication research and development. As the amount of drug-related data increases, the deep-learning method has been applied at every stage of the drug development process. A broad overview of artificial intelligence (AI) and its use in medication research and discovery is discussed in this review. Drug metabolism, excretion, and recent advancements in colorectal cancer and tooth loss are discussed, along with the integration

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Kambhampati, Ravi Theja. "AI Telco Research: Advancements in Telecommunications Scientific Discovery." International Journal for Research in Applied Science and Engineering Technology 12, no. 9 (2024): 1514–19. http://dx.doi.org/10.22214/ijraset.2024.64339.

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This article explores the transformative impact of Artificial Intelligence (AI) on telecommunications research and development. It examines how the integration of AI technologies is revolutionizing various aspects of the industry, including network optimization, spectrum management, and scientific discovery. The article highlights the synergy between human expertise and AI capabilities in enhancing research processes, improving data analysis, and driving innovation. Key areas of focus include AI-powered data analysis, network behavior simulation, signal processing enhancements, and the evolvin

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Uttam, Kumar* Priyal jain Dr. Jitendra banweer. "Artificial Intelligence Based Drug Designing." International Journal of Pharmaceutical Sciences 3, no. 5 (2025): 1535–52. https://doi.org/10.5281/zenodo.15380466.

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The combination of Artificial Intelligence (AI) and pharmaceutical science is creating exciting changes in the way new medicines are discovered and developed. Significant developments in artificial intelligence and machine learning offer a game-changing prospect for pharmaceutical dosage form testing, formulation, and medication discovery. AI can help lower development costs. In addition to predicting the pharmacokinetics and toxicity of potential drugs, machine learning techniques aid in the design of experiment. By prioritizing and optimizing lead compounds, this capability lessens the need

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Sharma, Anshul. "AI Powered Identification of Drug Targets and Pathways for Diagnosis and Treatment Planning: A Review." Nanomedicine & Nanotechnology Open Access 8, no. 2 (2023): 1–6. http://dx.doi.org/10.23880/nnoa-16000232.

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AI has become an integral part of drug discovery, particularly in the identification of drug targets and pathways for diagnosis and treatment planning. By using machine learning algorithms to analyze large datasets, AI can identify potential drug targets and predict drug efficacy, potentially streamlining the drug development process and improving patient outcomes. In this article, we have discussed the emerging role of AI in the discovery of drug targets and pathways for diagnosis and treatment planning. We have explored how AI is being used to identify potential drug targets by analyzing lar

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Ali Mahjoub, Mohammad, and Zahra Sheikholislam. "Artificial Intelligence in Drug Discovery and Delivery: Advancements and Applications." Journal of Biomedical Research & Environmental Sciences 4, no. 7 (2023): 1140–42. http://dx.doi.org/10.37871/jbres1778.

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Artificial Intelligence (AI) has made significant progress in drug discovery and drug delivery and has become an active area of research. The use of AI in drug delivery has gained significant attention, with the development of new technologies and algorithms that enable more efficient drug delivery. The history of AI in drug discovery can be traced back to the 1960s, and since then, AI has been used in various stages of drug discovery, including target identification, lead optimization, and drug design. AI can aid in different stages of drug development, including drug discovery, formulation,

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Shubham, Gurule* Pratik Bhabad Anuja Darade Sahil Gawade Rutuja Avhad Sakshi Bhoye. "Artificial intelligence in drug discovery and development." International Journal of Scientific Research and Technology 2, no. 3 (2025): 426–33. https://doi.org/10.5281/zenodo.15078083.

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Artificial Intelligence (AI) has emerged as a transformative force in drug discovery and development, addressing challenges such as high costs, lengthy timelines, and frequent failures in pharmaceutical research. AI-driven approaches, including machine learning, deep learning, and bioinformatics, are revolutionizing various stages of the drug development process—from target identification and lead optimization to clinical trials and regulatory approval. AI enhances drug bioactivity prediction, facilitates personalized medicine, and streamlines clinical trial recruitment, improving effici

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42

Visan, Anita Ioana, and Irina Negut. "Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery." Life 14, no. 2 (2024): 233. http://dx.doi.org/10.3390/life14020233.

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Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening

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Ghag, Vaishnavi P. "Revolutionizing Drug Discovery and Development with AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem51077.

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Artificial Intelligence (AI) is revolutionizing the pharmaceutical landscape by transforming the traditionally laborious, expensive, and time-consuming drug discovery and development process. By harnessing the power of machine learning (ML), deep learning (DL), and advanced data analytics, AI systems enable rapid screening of compounds, prediction of drug-target interactions, and optimization of molecular structures. These technologies significantly reduce development timelines and costs while increasing the probability of success in clinical trials. AI-driven platforms facilitate personalized

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Cheepurubilli, Tharun. "AI in Healthcare: Medical Diagnostics & Drug Discovery." Journal of Research and Innovation in Technology, Commerce and Management Vol. 2, Issue 6 (2025): 2630–38. https://doi.org/10.5281/zenodo.15591237.

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This paper examines the transformative impact of artificial intelligence (AI) in healthcare, with specific focus on medical diagnostics and drug discovery. The integration of AI technologies has revolutionized medical imaging analysis, disease diagnosis, personalized medicine approaches, and predictive analytics in patient care. Through comprehensive analysis of current implementations, this research highlights how machine learning algorithms, deep neural networks, and natural language processing have enhanced diagnostic accuracy, accelerated drug development timelines, and improved patient ou

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Tanmay, Kohad*. "Artificial Intelligence in Drug Discovery and Pharmacology." International Journal of Pharmaceutical Sciences 3, no. 5 (2025): 2011–19. https://doi.org/10.5281/zenodo.15391096.

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Artificial Intelligence (AI) has emerged as a transformative force in drug discovery and pharmacology, offering unprecedented capabilities to accelerate and optimize the traditionally lengthy, expensive, and complex drug development pipeline. By leveraging machine learning (ML), deep learning (DL), and natural language processing (NLP), AI enables rapid identification of drug targets, virtual screening of lead compounds, drug repurposing, and early prediction of pharmacokinetic and pharmacodynamic properties. These techniques significantly reduce the time and cost associated with bringing new

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Lou, Bowen, and Lynn Wu. "AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-Scale Examination of Bio-Pharma Firms." MIS Quarterly 45, no. 3 (2021): 1451–82. http://dx.doi.org/10.25300/misq/2021/16565.

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Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm’s ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm’s discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less hel

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Córdova, France, Valerie Browning, Walter Copan, Evgeni Gousev, and Jesse Thaler. "Physics, AI, and the future of discovery." Physics Today 77, no. 11 (2024): 30–37. http://dx.doi.org/10.1063/pt.rlqh.ngld.

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Nwakamma Ninduwezuor-Ehiobu, Olawe Alaba Tula, Chibuike Daraojimba, et al. "TRACING THE EVOLUTION OF AI AND MACHINE LEARNING APPLICATIONS IN ADVANCING MATERIALS DISCOVERY AND PRODUCTION PROCESSES." Engineering Science & Technology Journal 4, no. 3 (2023): 66–83. http://dx.doi.org/10.51594/estj.v4i3.552.

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This research paper examines the transformative role of artificial intelligence (AI) and machine learning (ML) in advancing materials discovery and production processes. The paper explores the historical evolution of AI and ML techniques, their application in materials science, challenges and limitations, emerging technologies, and ethical considerations. Key findings highlight how AI and ML accelerate materials discovery, optimize production processes, and enhance quality control. Emerging technologies such as generative models, reinforcement learning, and AI integration with experimental tec

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Bagane, Mayur, and Dr Rajesh Jorgewad. "From AI Labs to Clinics: A Review of 21st-Century Drug Candidates Powered by Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 1419–28. http://dx.doi.org/10.22214/ijraset.2024.58188.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on drug discovery. Traditional drug discovery, slow and serendipitous, struggled to meet urgent medical needs. Artificial intelligence (AI) emerges as a transformative force, harnessing vast scientific data to predict drug properties and efficacy with remarkable precision. From identifying novel targets to designing custom molecules, AI streamlines selection, reduces costs and opens doors to previously unexplored therapeutic avenues. Breakthrough candidates like Atacicept, GTX-007, and AB-928 showcase the

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Alanazi, Ahmad Asri Awad, ‏Abdulrahman Ibrahim Abdullah Al Fahad, Abdullah Saleh Abdullah Almorshed, et al. "Artificial intelligence in drug discovery: Current applications and future directions." International journal of health sciences 6, S10 (2022): 2011–40. http://dx.doi.org/10.53730/ijhs.v6ns10.15290.

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Background: The drug discovery process is complex, time-consuming, and costly, traditionally relying on trial-and-error approaches. The integration of artificial intelligence (AI) and machine learning (ML) has emerged as a transformative solution, enhancing efficiency and precision in identifying potential drug candidates. Aim: This review aims to explore the current applications of AI in drug discovery, highlight the AI tools utilized in the process, and discuss the associated challenges. Methods: A comprehensive literature review was conducted, focusing on peer-reviewed articles, clinical st

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