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How artificial intelligence is changing drug discovery
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“AI is, for example, the major driver behind the current trend toward personalized medicine, with treatments being tailored to each patient's specific biological profile (Fleming, 2018;Lichfield, 2018).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Fleming, 2018
Experimental — accuracy not yet measuredIn the citing paper
“Many more examples are available [3,32,33,34,35,36,37], showing that AI-based methods can accelerate the drug discovery process and enable the development of more effective medications.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [37]
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Machine intelligence in cardiovascular medicine
Miller, D. D. (2020) · Cardiology in Review · doi:10.1097/crd.0000000000000294 · Cited by 58
“50 Taking advantage of ultrafast parallel processing computing for the query and extract-transform-load of large proprietary and public datasets, drug R&D operations can rapidly model the spatial relationships and dynamic coupling of very large numbers of candidate compounds (ie, small molecule…”
BackgroundIntegrated AI and Innovation Management: The Beginning of a Beautiful Friendship
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Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design
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Application of big data and artificial intelligence in epidemic surveillance and containment
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Organ-on-a-chip technology: turning its potential for clinical benefit into reality
Haddrick, M., & Simpson, P. B. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.03.011 · Cited by 59
Wee, J., & Xia, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab136 · Cited by 57
Artificial intelligence guided conformational mining of intrinsically disordered proteins
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Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2
Prasad, K., & Kumar, V. (2021) · Current Research in Pharmacology and Drug Discovery · doi:10.1016/j.crphar.2021.100042 · Cited by 58
“Over the past decade, AI-based models have revolutionized drug discovery in general (Fleming, 2018; Lavecchia, 2019).”
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“Traditional drug development methods are laborious and expensive, taking over a decade and billions of dollars to bring a single drug to market 1 .”
Rahman, M. M., Khatun, F., Uzzaman, A., Sami, S. I., Bhuiyan, M. A.-A., & Kiong, T. S. (2021) · International Journal of Health Services · doi:10.1177/00207314211017469 · Cited by 59
Current Trends, Overlooked Issues, and Unmet Challenges in Virtual Screening
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COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods
Aruleba, R. T., Adekiya, T. A., Ayawei, N., Obaido, G., Aruleba, K., Mienye, I. D., Aruleba, I., & Ogbuokiri, B. (2022) · Bioengineering · doi:10.3390/bioengineering9040153 · Cited by 60
Artificial intelligence in dermatology and healthcare: An overview
Pai, V. V., & Pai, R. B. (2021) · Indian Journal of Dermatology Venereology and Leprology · doi:10.25259/ijdvl_518_19 · Cited by 57
“…development, such as identification and validation of drug targets, designing of new drugs, drug repurposing, improving the R and D efficiency etc. 51-53 AI can predict with high accuracy the likelihood of failure of a drug clinical trial, to determine the cancer cell sensitivity to therapeutics,…”
Pandiyan, S., & Wang, L. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106140 · Cited by 63
McLennan, S., Meyer, A., Schreyer, K., & Buyx, A. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000114 · Cited by 50
“With the ability to learn from large sets of clinical data, medical AI applications have the potential to support a range of activities, including diagnosis [4], clinical decision making [5], personalized medicine [6], therapy [7], clinical research [8], drug development [9], and administrative processes [10].”
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“The recent evidence on cell-by-cell variability-dependent drug sensitivity in an analysis of thousands of cells from multiple cancer types [9] and the necessity of machine learning to analyze transcriptional landscapes of two million cells [2] suggest that high-precision dissection of drug resistance at single-cell re…”
BackgroundZhao, D., Wang, W., Tang, T., Zhang, Y., & Yu, C. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.05.029 · Cited by 52
“Given the ever-increasing breadth of data that current clinical research encompasses, including health records, diagnostic and treatment data, and extensive genomic databases, a major advantage of AI lies in its ability to efficiently analyze big data [12].”
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Artificial Intelligence: The Future for Organic Chemistry?
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Sağlık Hizmetleri ve Yönetiminde Yapay Zekâ
AKALIN, B., & Veranyurt, Ü. (2021) · Acta Infologica · doi:10.26650/acin.750857 · Cited by 42
The Ethics of AI in Health Care: A Mapping Review
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020) · Philosophical studies series · doi:10.1007/978-3-030-81907-1_18 · Cited by 145
Molecular property prediction based on graph structure learning
Zhao, B., Xu, W., Guan, J., & Zhou, S. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae304 · Cited by 40
“This is particularly important considering that the average cost of developing a new drug is currently estimated to be approximately $2.8 billion [4, 30] and the development period lasts a dozen of years, let alone the high risk of clinical failure [20].”
BackgroundInfluentialA roadmap to AI-driven in silico process development: bioprocessing 4.0 in practice
von Stosch, M., Portela, R. M. C., & Varsakelis, C. (2021) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2021.100692 · Cited by 39
Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes
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“A great deal of innovative recent work has explored machine-learning methods—particularly deep learning methods— for predicting ligand properties (54).”
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“4 Steps included in IBFRS the search for novel drugs and result in the creation of fresh medical approaches [79].”
In Silico Tools and Software to Predict ADMET of New Drug Candidates
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“60 Molecular biologists and computer scientists are working together to acquire new insights into tumor biology due to advances made possible by AI.”
Omics Data and Data Representations for Deep Learning-Based Predictive Modeling
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“The employment of computational drug discovery by using big data and deep neural networks to detect the complex patterns in drug-related databases, generate hypotheses and reduce the search space could bring a new era in pharmaceutics [10,11].”
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A population health perspective on artificial intelligence
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Proving the Mode of Action of Phytotoxic Phytochemicals
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Delivery of Nanoconstructs in Cancer Therapy: Challenges and Therapeutic Opportunities
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Unbiased data analytic strategies to improve biomarker discovery in precision medicine
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Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive ReviewPreprint
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“oper AI approaches are used, we will enter an age in which clinical trial errors are reduced to the lowest rate possible and the drug discovery phase can be done quicker, easier, and more effectively [126]. As a result, using ML methods, information can be extracted from six types of evidence: proteomics, microarrays,…”
MethodZilocchi, M., Broderick, K., Phanse, S., Aly, K. A., & Babu, M. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.09.008 · Cited by 32
“While it is hard to predict where AI is going to take us in the next decade, it is important to note that AI has not yet led to commercialized drugs, thus remaining a promising area that lacks proof-of-concept product outcomes [125].”
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Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications
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Privacy-preserving federated learning based on partial low-quality data
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Artificial intelligence in medicine: past, present, and future
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“In this regard, AI is crucial in optimizing drug discovery pipelines and improving drug development outcomes, with estimated costs of US $2.6 billion [59].”
InfluentialAutomated De Novo Drug Design: Are We Nearly There Yet?
Schneider, G., & Clark, D. E. (2019) · Angewandte Chemie · doi:10.1002/ange.201814681 · Cited by 37
Rational Design of Organelle-Targeted Fluorescent Probes: Insights from Artificial Intelligence
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Blaming Humans and Machines: What Shapes People’s Reactions to Algorithmic Harm
Lima, G., Grgić-Hlača, N., & Cha, M. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580953 · Cited by 35
“Medicine has received considerable attention from AI research, with algorithms being used to discover new drugs [40], identify patients at risk [83], and many other functions.”
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Deng, X., Chen, K., Pang, K., Liu, X., Gao, M., Ren, J., Yang, G., Wu, G., Zhang, C., Ni, X., et al. (2023) · Chinese Chemical Letters · doi:10.1016/j.cclet.2023.108861 · Cited by 29
Towards in silico Process Modeling for Vaccines
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Artificial Intelligence: Applications in Pharmacovigilance Signal Management
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“ML is not a new concept in cancer studies; it has been used to study various cancer types such as cancer lesion classification,(34) image modalities,(35) gastric carcinoma,(36) lung cancer,(37) classification of skin lesions,(23) cancer sequencing data,(38) predicting outcomes in colorectal cancer,(39) and drug discov…”
MethodDrug repurposing in silico screening platforms
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China and the U.S. produce more impactful AI research when collaborating together
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“In healthcare, machine learning algorithms support the global response to pandemic outbreaks [7], transforming the medical image analysis field [8, 9], and making drug discovery faster, cheaper, and more effective [10].”
Introduction to Machine Learning in Obstetrics and Gynecology
Shazly, S. A., Trabuco, E. C., Ngufor, C. G., & Famuyide, A. O. (2022) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000004706 · Cited by 32
“17,21 The company leveraged machine learning algorithms to better understand normal cells ’ and cancer cells ’ responses to varying concentrations of oxygen and sugar to develop BPM31510, a medication that is currently undergoing phase II trials for pancreatic cancer treatment.”
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“These tools can assist healthcare workers in fully understanding human biology providing improved abilities to treat, prevent, and cure human diseases (Fleming, 2018 ).”
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“In addition, the vast amount of technical knowledge accumulated in patents has created a basis for automating parts of the design process or the discovery of new materials and drugs [134,135].”
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“But then again, nine out of ten drugs fail Phase II clinical trials and eventually receive final regulatory body approval [58, 59].”
Best practices for artificial intelligence in life sciences research
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“Prior research shows that investment into AI to support internal organizational tasks can drive cost efficiencies in diverse domains such as talent-acquisition [99], drug development [40], demand forecasting [103], inventory management [36], auditing [54], and employee performance management [18].”
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“Currently, over 90% of drug candidates fail between phase I clinical trials and regulatory approval, resulting in substantial loss of financial investment and resources (Fleming, 2018).”
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“There is considerable hype around the use of artificial intelligence and machine learning to find novel drug candidates and to optimize early-stage drug discovery (Fleming, 2018).”
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“In general, discovering a drug takes on average 15 years and approximately US $ 2.8 billion [3].”
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“…clinician-free AI imaging diagnostic tools’ (Ratner 2018); ‘Computer-calculated compounds: researchers are developing artificial intelligence to discover drugs’ (Fleming 2018); ‘AI designs organic syntheses’ (Lowe 2018); ‘Machine learning for molecular and material science’ (Butler et al. 2018);…”
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A community effort in SARS‐CoV‐2 drug discovery
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“Such methods should also suggest new promising ligands [21–23].”
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Artificial intelligence in international business: IB theory under augmented decision-making
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"Artificial Intelligence in Veterinary Care: A Review of Applications for Animal Health
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“The effectiveness of treatment is increased and the likelihood of pharmaceutical side effects is decreased with this personalized approach [17].”
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Drug Discovery and Drug Identification using AI
Biswas, R., Basu, A., Nandy, A., et al. (2020) · doi:10.1109/indo-taiwanican48429.2020.9181309 · Cited by 15
“With recent advances [2] in AI and further work that has been done in this field has given us scope to do more experimentation.”
BackgroundArtificial Intelligence in Organizations: Implications for Information Systems Research
Benbya, H., Pachidi, S., & Järvenpää, S. L. (2021) · Own your potential (DEAKIN) · Cited by 15
Designing medical artificial intelligence for in- and out-groups
Li, W., Zhou, X., & Yang, Q. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106929 · Cited by 16
Manu, D., Sheng, Y., Yang, J., Deng, J., Geng, T., Li, A., Ding, C., Jiang, W., & Yang, L. (2021) · International Conference on Computer Aided Design · doi:10.1109/iccad51958.2021.9643440 · Cited by 17
Discovering novel Cathepsin L inhibitors from natural products using artificial intelligence
Li, Q., Zhou, S.-R., Kim, H., Wang, H., Zhu, J., & Yang, J. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.06.009 · Cited by 17
“The entire process can take decades and cost billions of dollars, with a failure rate exceeding 90 % [72].”
BackgroundAI in Health Science: A Perspective
Mishra, R., Chaudhary, K., & Mishra, I. (2022) · Current Pharmaceutical Biotechnology · doi:10.2174/1389201023666220929145220 · Cited by 14
Machine learning applications to Covid-19: a state-of-the-art survey
Kamalov, F., Cherukuri, A. K., & Thabtah, F. (2022) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset53988.2022.9734959 · Cited by 16
Lu, Y., Chen, J., Fan, N., Song, W., Sheng, H., Yang, Y., & Wang, J. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02400-3 · Cited by 23
Metadata
- Authors
- Fleming, N.
- Corresponding author
- Fleming, Nic
- Venue
- Nature
- Published
- 2018-05-30
- Volume
- 557
- Issue
- 7707
- Pages
- S55-S57
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2806834952
- Cited by
- 546
- References
- 0
- Citations by year
- 2026: 41 · 2025: 49 · 2024: 81 · 2023: 95 · 2022: 97 · 2021: 81 · 2020: 56 · 2019: 38 · 2018: 7
- FWCI
- 34.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, drug discovery, machine learning, pharmaceuticals, computer-aided drug design
- MeSH terms
- Machine Learning, Algorithms, Amyotrophic Lateral Sclerosis, Animals, Artificial Intelligence, Automation, Drug Industry, Humans, Neoplasms, Pattern Recognition, Automated, Robotics, Efficiency, Organizational, Mice, Drug Discovery, Precision Medicine
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