Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics
Open accessAbstract
Abstract Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as game-changing technologies, AI and machine learning algorithms have slowly but surely begun to revolutionize pharmaceutical industry and drug development over the past 5 years. In this expert review, we describe the most frequently used machine learning algorithms in drug development pipelines and the -omics databases well poised to support machine learning and drug discovery. Subsequently, we analyze the emerging new computational approaches to drug discovery and the in silico pipelines for drug repositioning and the synergies among -omics system sciences, AI and machine learning. As with system sciences, AI and machine learning embody a system scale and Big Data driven vision for drug discovery and development. We conclude with a future outlook on the ways in which machine learning approaches can be implemented to buttress and expedite drug discovery and precision medicine. As AI and machine learning are rapidly entering pharmaceutical industry and the art and science of drug development, we need to critically examine the attendant prospects and challenges to benefit patients and public health.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI-based methods can accelerate the drug discovery process and enable the development of more effective medications.
“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 [35]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863 · see citing works
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551 · see citing works
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228 · see citing works
Sequence Variations in <i>PCSK9,</i> Low LDL, and Protection against Coronary Heart Disease
Cohen, J. C., Boerwinkle, E., Mosley, T. H., & Hobbs, H. H. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa054013 · Cited by 3,181 · see citing works
A genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993 · see citing works
The NCI60 human tumour cell line anticancer drug screen
Shoemaker, R. H. (2006) · Nature reviews. Cancer · doi:10.1038/nrc1951 · Cited by 2,644 · see citing works
Bias in error estimation when using cross-validation for model selection
Varma, S., & Simon, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-91 · Cited by 2,485 · cited 1× · see citing works
“Then, the procedure is repeated as many times as the data points (Baek et al., 2009; Varma and Simon, 2006).”
Machine learning applications in genetics and genomics
Libbrecht, M. W., & Noble, W. S. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3920 · Cited by 2,142 · see citing works
Assessing the accuracy of prediction algorithms for classification: an overview
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.5.412 · Cited by 2,111 · see citing works
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · see citing works
Hoadley, K. A., Yau, C. S. F., Wolf, D. M., Cherniack, A. D., Tamborero, D., Ng, S., Leiserson, M. D. M., Niu, B., McLellan, M. D., Uzunangelov, V., et al. (2014) · Cell · doi:10.1016/j.cell.2014.06.049 · Cited by 1,500 · see citing works
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works
Cohen, J., Pertsemlidis, A., Kotowski, I. K., Graham, R., Garcia, C. K., & Hobbs, H. H. (2005) · Nature Genetics · doi:10.1038/ng1509 · Cited by 1,381 · see citing works
Byers, L. A., Diao, L., Wang, J., Saintigny, P., Girard, L., Peyton, M., Shen, L., Fan, Y., Giri, U., Tumula, P. K., et al. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-12-1558 · Cited by 1,059 · see citing works
Artificial intelligence in drug development: Present status and future prospects
Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018 · see citing works
Geeleher, P., Cox, N. J., & Huang, R. S. (2014) · Genome biology · doi:10.1186/gb-2014-15-3-r47 · Cited by 889 · see citing works
A community effort to assess and improve drug sensitivity prediction algorithms
Community, N. D., Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2877 · Cited by 761 · see citing works
Review of Drug Repositioning Approaches and Resources
Xue, H., Li, J., Xie, H., & Wang, Y. (2018) · International Journal of Biological Sciences · doi:10.7150/ijbs.24612 · Cited by 613 · see citing works
Menden, M. P., Iorio, F., Garnett, M. J., McDermott, U., Benes, C. H., Ballester, P. J., & Sáez-Rodríguez, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061318 · Cited by 543 · see citing works
Nicolau, M. M., Levine, A. J., & Carlsson, G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1102826108 · Cited by 516 · see citing works
Loss-of-function mutations in SLC30A8 protect against type 2 diabetes
Go-T2D Consortium, Flannick, J., T2D-GENES Consortium, Thorleifsson, G., Beer, N. L., Jacobs, S. B. R., Grarup, N., Burtt, N. P., Mahajan, A., Fuchsberger, C., et al. (2014) · Nature Genetics · doi:10.1038/ng.2915 · Cited by 492 · see citing works
Pharmacogenomic agreement between two cancer cell line data sets
The Cancer Cell Line Encyclopedia Consortium, & The Genomics of Drug Sensitivity in Cancer Consortium (2015) · Nature · doi:10.1038/nature15736 · Cited by 418 · see citing works
Gönen, M. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts360 · Cited by 383 · see citing works
Drug repositioning: a machine-learning approach through data integration
Napolitano, F., Zhao, Y., Moreira, V. M., Tagliaferri, R., Kere, J., D’Amato, M., & Greco, D. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-30 · Cited by 349 · see citing works
A review of network-based approaches to drug repositioning
Lotfi Shahreza, M., Ghadiri, N., Mousavi, S. R., Varshosaz, J., & Green, J. R. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx017 · Cited by 330 · see citing works
A community computational challenge to predict the activity of pairs of compounds
Community, N.-D., Bansal, M., Yang, J., Karan, C., Menden, M. P., Costello, J. C., Tang, H., Xiao, G., Li, Y., Allen, J., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3052 · Cited by 321 · see citing works
Zhu, T., Cao, S., Su, P.-C., Patel, R., Shah, D., Chokshi, H. B., Szukala, R., Johnson, M. E., & Hevener, K. E. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm301916b · Cited by 310 · see citing works
A review of computational drug repurposing
Park, K. (2019) · Translational and Clinical Pharmacology · doi:10.12793/tcp.2019.27.2.59 · Cited by 303 · see citing works
Global Proteome Analysis of the NCI-60 Cell Line Panel
Gholami, A. M., Hahne, H., Wu, Z., Auer, F. J., Meng, C., Wilhelm, M., & Kuster, B. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.07.018 · Cited by 302 · see citing works
Modeling precision treatment of breast cancer
Daemen, A., Griffith, O. L., Heiser, L. M., Wang, N. J., Enache, O. M., Sanborn, Z., Pepin, F., Durinck, S., Korkola, J. E., Griffith, M., et al. (2013) · Genome biology · doi:10.1186/gb-2013-14-10-r110 · Cited by 287 · see citing works
Manifold alignment using Procrustes analysis
Wang, C., & Mahadevan, S. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390297 · Cited by 265 · see citing works
Weeber, M., Klein, H., den Berg, L. T. W. D. J., & Vos, R. (2001) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.1104 · Cited by 265 · see citing works
Frismantas, V., Dobay, M. P., Rinaldi, A. M., Tchinda, J., Dunn, S. H., Kunz, J. B., Richter‐Pechańska, P., Marovca, B., Pail, O., Jenni, S., et al. (2017) · Blood · doi:10.1182/blood-2016-09-738070 · Cited by 260 · see citing works
Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach
Emig, D., Ivliev, A. E., Pustovalova, O., Lancashire, L., Bureeva, S. V., Nikolsky, Y., & Bessarabova, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060618 · Cited by 242 · see citing works
The Next Era: Deep Learning in Pharmaceutical Research
Ekins, S. (2016) · Pharmaceutical Research · doi:10.1007/s11095-016-2029-7 · Cited by 237 · see citing works
Biomarkers as drug development tools: discovery, validation, qualification and use
Kraus, V. B. (2018) · Nature Reviews Rheumatology · doi:10.1038/s41584-018-0005-9 · Cited by 231 · see citing works
Mamoshina, P., Volosnikova, M., Ozerov, I. V., Lane, E., Skibina, E., Cortese, F., & Zhavoronkov, A. A. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00242 · Cited by 227 · see citing works
Ding, M. Q., Chen, L., Cooper, G. F., Young, J. D., & Lu, X. (2017) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-17-0378 · Cited by 191 · see citing works
A multi-scale convolutional neural network for phenotyping high-content cellular images
Godinez, W. J., Hossain, I., Lazic, S. E., Davies, J. W., & Zhang, X. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx069 · Cited by 181 · see citing works
Predicting in vitro drug sensitivity using Random Forests
Riddick, G., Song, H., Ahn, S. J., Walling, J., Borges-Rivera, D., Zhang, W., & Fine, H. A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq628 · Cited by 170 · see citing works
Ten things we have to do to achieve precision medicine
Kohane, I. S. (2015) · Science · doi:10.1126/science.aab1328 · Cited by 162 · see citing works
Network-based drug repositioning
Wu, Z., Wang, Y., & Chen, L. (2013) · Molecular BioSystems · doi:10.1039/c3mb25382a · Cited by 161 · see citing works
A Study on the Application and Use of Artificial Intelligence to Support Drug Development
Lamberti, M. J., Wilkinson, M., Donzanti, B. A., Wohlhieter, G. E., Parikh, S., Wilkins, R. G., & Getz, K. (2019) · Clinical Therapeutics · doi:10.1016/j.clinthera.2019.05.018 · Cited by 160 · see citing works
Kinase Pathway Dependence in Primary Human Leukemias Determined by Rapid Inhibitor Screening
Tyner, J. W., Yang, W. F., Bankhead, A., Fan, G., Fletcher, L. B., Bryant, J. A., Glover, J., Chang, B. H., Spurgeon, S. E. F., Fleming, W., et al. (2012) · Cancer Research · doi:10.1158/0008-5472.can-12-1906 · Cited by 150 · see citing works
Geeleher, P., Zhang, Z., Wang, F., Gruener, R. F., Nath, A., Morrison, G., Bhutra, S., Grossman, R. L., & Huang, R. S. (2017) · Genome Research · doi:10.1101/gr.221077.117 · Cited by 137 · see citing works
Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)
Berrar, D., & Flach, P. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbr008 · Cited by 136 · see citing works
In silico prediction of novel therapeutic targets using gene–disease association data
Ferrero, E., Dunham, I., & Sanséau, P. (2017) · Journal of Translational Medicine · doi:10.1186/s12967-017-1285-6 · Cited by 132 · see citing works
Vougas, K. N., Sakellaropoulos, T., Kotsinas, A., Foukas, G.-R. P., Ntargaras, A., Koinis, F., Polyzos, A., Myrianthopoulos, V., Zhou, H., Narang, S., et al. (2019) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2019.107395 · Cited by 126 · see citing works
Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel
Cortés‐Ciriano, I., van Westen, G. J. P., Bouvier, G., Nilges, M., Overington, J. P., Bender, A., & Malliavin, T. E. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv529 · Cited by 121 · see citing works
Ammad-ud-din, M., Khan, S. A., Malani, D., Murumägi, A., Kallioniemi, O., Aittokallio, T. A., & Samuel Kaski, A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw433 · Cited by 118 · see citing works
Interaction prediction in structure-based virtual screening using deep learning
Gonczarek, A., Tomczak, J. M., Zaręba, S., Kaczmar-Michalska, J., Dąbrowski, P., & Walczak, M. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.007 · Cited by 103 · see citing works
Andersson, E. I., Pützer, S., Yadav, B., Dufva, O., Khan, S., He, L., Sellner, L., Schrader, A., Crispatzu, G., Oleś, M., et al. (2017) · Leukemia · doi:10.1038/leu.2017.252 · Cited by 94 · see citing works
An Ensemble Based Top Performing Approach for NCI-DREAM Drug Sensitivity Prediction Challenge
Wan, Q., & Pal, R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101183 · Cited by 85 · see citing works
Chen, B., Sirota, M., Fan-Minogue, H., Hadley, D., & Butte, A. J. (2015) · BMC Medical Genomics · doi:10.1186/1755-8794-8-s2-s5 · Cited by 77 · see citing works
Aben, N., Vis, D. J., Michaut, M., & Wessels, L. F. A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw449 · Cited by 75 · see citing works
Ammad-ud-din, M., Khan, S. A., Wennerberg, K., & Aittokallio, T. A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx266 · Cited by 73 · see citing works
Heterogeneity Aware Random Forest for Drug Sensitivity Prediction
Rahman, R., Matlock, K., Ghosh, S., & Pal, R. (2017) · Scientific Reports · doi:10.1038/s41598-017-11665-4 · Cited by 71 · see citing works
Ali, M., Khan, S. A., Wennerberg, K., & Aittokallio, T. A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx766 · Cited by 62 · see citing works
Gene expression profile alone is inadequate in predicting complete response in multiple myeloma
Amin, S. B., Yip, W.-K., Minvielle, S., Broyl, A., Li, Y., Hanlon, B. M., Swanson, D. M., Shah, P. K., Moreau, P., van der Holt, B., et al. (2014) · Leukemia · doi:10.1038/leu.2014.140 · Cited by 61 · see citing works
Falgreen, S., Dybkær, K., Young, K. H., Xu-Monette, Z. Y., El‐Galaly, T. C., Laursen, M. B., Bødker, J. S., Kjeldsen, M. K., Schmitz, A., Nyegaard, M., et al. (2015) · BMC Cancer · doi:10.1186/s12885-015-1237-6 · Cited by 59 · see citing works
Development of biomarker classifiers from high-dimensional data
Baek, S., Tsai, C., & Chen, J. J. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp016 · Cited by 56 · see citing works
Ли, Б., Shin, H., Gulbekyan, G., Pustovalova, O., Nikolsky, Y., Hope, A. J., Bessarabova, M., Schu, M. C., Kolpakova-Hart, E., Merberg, D., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130700 · Cited by 53 · see citing works
Structure-Based Functional Design of Drugs: From Target to Lead Compound
Anderson, A. C. (2011) · Methods in molecular biology · doi:10.1007/978-1-60327-216-2_23 · Cited by 53 · see citing works
Computational identification of multi-omic correlates of anticancer therapeutic response
Stetson, L. C., Pearl, T., Chen, Y., & Barnholtz-Sloan, J. S. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-s7-s2 · Cited by 49 · see citing works
Pathway and network-based strategies to translate genetic discoveries into effective therapies
Greene, C. S., & Voight, B. F. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw160 · Cited by 49 · see citing works
Linking drug target and pathway activation for effective therapy using multi-task learning
Yang, M., Simm, J., Lam, C. C., Zakeri, P., van Westen, G. J. P., Moreau, Y., & Sáez-Rodríguez, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-25947-y · Cited by 46 · see citing works
Turki, T., Wei, Z., & Wang, J. T. L. (2018) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720018400140 · Cited by 39 · see citing works
DIGRE: Drug‐Induced Genomic Residual Effect Model for Successful Prediction of Multidrug Effects
Yang, J., Tang, J. P., Li, Y., Zhong, R., Wang, T.-M., Wong, S. T. C., Xiao, G., & Xie, Y. (2015) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.1 · Cited by 37 · see citing works
A tool for discovering drug sensitivity and gene expression associations in cancer cells
Qin, Y. W., Conley, A. P., Grimm, E. A., & Roszik, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176763 · Cited by 34 · see citing works
Robust Prediction of Anti-Cancer Drug Sensitivity and Sensitivity-Specific Biomarker
Park, H., Shimamura, T., Miyano, S., & Imoto, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108990 · Cited by 22 · see citing works
A p-Median approach for predicting drug response in tumour cells
Fersini, E., Messina, E., & Archetti, F. (2014) · BMC Bioinformatics · doi:10.1186/s12859-014-0353-7 · Cited by 20 · see citing works
Tran, T. P., Ong, E., Hodges, A. P., Paternostro, G., & Piermarocchi, C. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-74 · Cited by 19 · see citing works
Garvey, C. K. (2018) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2017.0218 · Cited by 10 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Zhao K, and So H. (2017). A machine learning approach to drug repositioning based on drug expression profiles: Applications to schizophrenia and depression/anxiety disorders.arXiv
Cited by
Sorted by citations
Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695 · see citing works
The role of AI in drug discovery: challenges, opportunities, and strategies
Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690 · see citing works
Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development
Sarkar, C., Das, B., Rawat, V. S., Wahlang, J. B., Nongpiur, A., Tiewsoh, I., Lyngdoh, N. M., Das, D., Bidarolli, M., & Sony, H. T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032026 · Cited by 347 · see citing works
Artificial intelligence, machine learning, and drug repurposing in cancer
Tanoli, Z., Vähä-Koskela, M. J. V., & Aittokallio, T. A. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1883585 · Cited by 224 · see citing works
Selvaraj, C., Chandra, I., & Singh, S. K. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10326-z · Cited by 206 · see citing works
Tribulations and future opportunities for artificial intelligence in precision medicine
Carini, C., & Seyhan, A. A. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05067-0 · Cited by 175 · see citing works
Huanbutta, K., Burapapadh, K., Kraisit, P., Sriamornsak, P., Ganokratanaa, T., Suwanpitak, K., & Sangnim, T. (2024) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2024.106938 · Cited by 185 · see citing works
The emerging roles of artificial intelligence in cancer drug development and precision therapy
Liang, G., Fan, W., Luo, H., & Zhu, X. M. (2020) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2020.110255 · Cited by 149 · see citing works
He, S., Leanse, L. G., & Feng, Y. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.113922 · Cited by 141 · see citing works
Application of Artificial Intelligence in Pathology: Trends and Challenges
Kim, I., Kang, K., Song, Y., & Kim, T. J. (2022) · Diagnostics · doi:10.3390/diagnostics12112794 · Cited by 126 · see citing works
Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace
Singh, N., Chaput, L., & Villoutreix, B. O. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa034 · Cited by 123 · see citing works
Nayarisseri, A., Khandelwal, R., Tanwar, P., Madhavi, M., Sharma, D., Thakur, G., Speck‐Planche, A., & Singh, S. K. (2021) · Current Drug Targets · doi:10.2174/1389450122999210104205732 · Cited by 105 · see citing works
Keutzer, L., You, H., Farnoud, A., Nyberg, J., Wicha, S. G., Maher‐Edwards, G., Vlasakakis, G., Moghaddam, G. K., Svensson, E. M., Menden, M. P., et al. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14081530 · Cited by 98 · see citing works
Aldughayfiq, B., & Sampalli, S. (2020) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2020.0085 · Cited by 91 · see citing works
Stenzinger, A., Alber, M., Allgäuer, M., Jurmeister, P. S., Bockmayr, M. L., Budczies, J., Lennerz, J. K., Eschrich, J., Kazdal, D. N., Schirmacher, P., et al. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2021.02.011 · Cited by 91 · see citing works
Alqarni, A. M., & Zeidler, M. P. (2020) · Biochemical Society Transactions · doi:10.1042/bst20190803 · Cited by 89 · see citing works
Emerging applications of machine learning in genomic medicine and healthcare
Chafai, N., Bonizzi, L., Botti, S., & Badaoui, B. (2023) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2023.2259466 · Cited by 81 · see citing works
Strategies of Artificial intelligence tools in the domain of nanomedicine
Habeeb, M., You, H. W., Mutheeswaran, U., Kanna, R. K., Hariyadi, & Mishra, S. (2023) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2023.105157 · Cited by 73 · see citing works
Antimicrobial Properties of Antidepressants and Antipsychotics—Possibilities and Implications
Caldara, M., & Marmiroli, N. (2021) · Pharmaceuticals · doi:10.3390/ph14090915 · Cited by 54 · see citing works
Targeting the STAT3 oncogenic pathway: Cancer immunotherapy and drug repurposing
Dong, Y., Chen, J., Chen, Y., & Liu, S. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.115513 · Cited by 54 · see citing works
Drug repurposing—an emerging strategy in cancer therapeutics
Turabi, K. S., Deshmukh, A. J., Paul, S., Swami, D., Siddiqui, S., Kumar, U., Naikar, S., Devarajan, S., Basu, S., Paul, M. K., et al. (2022) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-022-02263-x · Cited by 53 · see citing works
The applications and advances of artificial intelligence in drug regulation: A global perspective
Lixia, F., Jia, G., Liu, Z., Pang, X., & Cui, Y. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.11.006 · Cited by 50 · see citing works
In silico Potential of Approved Antimalarial Drugs for Repurposing Against COVID-19
Sachdeva, C., Wadhwa, A., Kumari, A., Hussain, F., Jha, P., & Kaushik, N. K. (2020) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2020.0071 · Cited by 47 · see citing works
Husnain, A., Rasool, S., Saeed, A., & Hussain, H. K. (2023) · International Journal of Multidisciplinary Sciences and Arts · doi:10.47709/ijmdsa.v2i2.2897 · Cited by 48 · see citing works
Multi-omics data integration approaches for precision oncology
Aguila, R. C., Alonso-Pupo, N., & Hernández‐Rodríguez, E. W. (2022) · Molecular Omics · doi:10.1039/d1mo00411e · Cited by 48 · see citing works
Advancing health care via artificial intelligence: From concept to clinic
Sandeep Ganesh, G., Kolusu, A. S., Prasad, K., Samudrala, P. K., & Nemmani, K. V. S. (2022) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2022.175320 · Cited by 43 · see citing works
Artificial intelligence and the future of life sciences
Leite, M. L., Costa, L. S. D. L., Cunha, V. A., Kreniski, V., Filho, M. D. O. B., da Cunha, N. B., & Costa, F. F. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.07.002 · Cited by 46 · see citing works
Pu, C., Cui, H. F., Yu, H., Cheng, X., Zhang, M., Qin, L., Ning, Z., Zhang, W., Chen, S., Qian, Y., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59874-0 · Cited by 40 · see citing works
Adeola, H. A., Bano, A., Vats, R., Vashishtha, A., Verma, D., Kaushik, D. K., Mittal, V., Rahman, M. H., Najda, A., Albadrani, G. M., et al. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2021.111809 · Cited by 32 · see citing works
RetractedRao, M. S., McDuffie, E., & Sachs, C. W. (2023) · Toxics · doi:10.3390/toxics11100875 · Cited by 31 · see citing works
The biomedical applications of artificial intelligence: an overview of decades of research
Naskar, S., Sharma, S., Kuotsu, K., Halder, S., Pal, G., Saha, S., Mondal, S., Biswas, U. K., Jana, M., & Bhattacharjee, S. (2025) · Journal of drug targeting (Print) · doi:10.1080/1061186x.2024.2448711 · Cited by 18 · see citing works
Maharjan, R., Lee, J., Lee, K., Han, H., Kim, K. H., & Jeong, S. H. (2023) · Journal of Pharmaceutical Investigation · doi:10.1007/s40005-023-00637-8 · Cited by 31 · see citing works
Özdemir, V., Arğa, K. Y., Aziz, R. K., Bayram, M., Conley, S. N., Dandara, C., Endrényi, L., Fisher, E., Garvey, C. K., Hekim, N., et al. (2020) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0220 · Cited by 29 · see citing works
Mulat, M., Banicod, R. J. S., Tabassum, N., Javaid, A., Kim, T., Kim, Y., & Khan, F. (2025) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2025.107232 · Cited by 30 · see citing works
Sampene, A. K., & Nyirenda, F. (2024) · Future Journal of Pharmaceutical Sciences · doi:10.1186/s43094-024-00632-2 · Cited by 26 · see citing works
Srivastava, N., Verma, S., Singh, A., Shukla, P., Singh, Y., Oza, A. D., Kaur, T., Chowdhury, S., Kapoor, M., & Yadav, A. N. (2024) · DARU Journal of Pharmaceutical Sciences · doi:10.1007/s40199-024-00548-5 · Cited by 27 · see citing works
Advances in machine learning for optimizing pharmaceutical drug discovery
Ogbonna, U. E., Ofoezie, E. F., Babalola, O. O., Ottu, P. O., Ogbonna, C. A., Olisakwe, S. C., George, E. T., Babarinde, S., Omaba, O. J., Chukwuemeka, C. G., et al. (2025) · Current Proteomics · doi:10.1016/j.curpro.2025.100015 · Cited by 24 · see citing works
GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds
Ahmed, M., Hasani, H. J., Kalyaanamoorthy, S., & Barakat, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-88939-5 · Cited by 20 · see citing works
Quazi, S. (2021) · Preprints.org · doi:10.20944/preprints202105.0346.v1 · Cited by 19 · see citing works
Triorganotin complexes in cancer chemotherapy: Mechanistic insights and future perspectives
Anasamy, T., Chee, C. F., Wong, Y. F., Heh, C. H., Kiew, L. V., Lee, H. B., & Chung, L. Y. (2020) · Applied Organometallic Chemistry · doi:10.1002/aoc.6089 · Cited by 18 · see citing works
Manian, V. B., Orozco-Sandoval, J., & Díaz-Martínez, V. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.732370 · Cited by 18 · see citing works
Majumder, S., & Panigrahi, G. K. (2024) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.10.001 · Cited by 17 · see citing works
Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs
Carraro, C., Montgomery, J. V., Klimmt, J., Paquet, D., Schultze, J. L., & Beyer, M. (2024) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2024.1414886 · Cited by 15 · see citing works
Drug Repurposing at the Interface of Melanoma Immunotherapy and Autoimmune Disease
Spitschak, A., Gupta, S. K., Singh, K. P., Logotheti, S., & Pützer, B. M. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics15010083 · Cited by 17 · see citing works
Bassey, G. E., Daniel, E., Okesina, K. B., & Odetayo, A. F. (2025) · Drug Design Development and Therapy · doi:10.2147/dddt.s538269 · Cited by 20 · see citing works
Siddiqui, M. F., Mouna, A., Nicolas, G. N., Rahat, S. A. A., Nurmamatovna, M. A., Emmanuel, N., & Tashmatova, N. M. (2022) · Studies in computational intelligence · doi:10.1007/978-981-16-9221-5_14 · Cited by 16 · see citing works
Advances in AI-based strategies and tools to facilitate natural product and drug development
Basnet, B. B., Zhou, Z., Wei, B., & Wang, H. (2025) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2025.2478094 · Cited by 17 · see citing works
Gurwitz, D. (2020) · Drug Development Research · doi:10.1002/ddr.21689 · Cited by 16 · see citing works
Drug Repurposing in Neurological Disorders: Implications for Neurotherapy in Traumatic Brain Injury
Shakkour, Z., Habashy, K. J., Berro, M., Takkoush, S., Abdelhady, S., Koleilat, N., Eid, A. H., Zibara, K., Obeid, M., Shear, D., et al. (2020) · The Neuroscientist · doi:10.1177/1073858420961078 · Cited by 16 · see citing works
Artificial Intelligence as Accelerator for Genomic Medicine and Planetary Health
Gülfidan, G., Beklen, H., & Arğa, K. Y. (2021) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2021.0170 · Cited by 17 · see citing works
Karwasra, R., Khanna, K., Singh, S., Ahmad, S., & Verma, S. M. (2022) · Studies in computational intelligence · doi:10.1007/978-981-16-9221-5_21 · Cited by 16 · see citing works
İnce, A. T., Silahtaroğlu, G., Seven, G., Koçhan, K., Yıldız, K., & Şentürk, H. (2022) · Pancreatology · doi:10.1016/j.pan.2022.12.005 · Cited by 15 · see citing works
Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data
Kumar, H., Panigrahi, M., Panwar, A., Rajawat, D., Nayak, S. S., Saravanan, K. A., Kaisa, K., Parida, S., Bhushan, B., & Dutt, T. (2022) · Journal of Computational Biology · doi:10.1089/cmb.2021.0447 · Cited by 15 · see citing works
An Update on the Global Treatment of Invasive Fungal Infections
Munzen, M. E., Garcia, A. D. G., & Martinez, L. R. (2023) · Future Microbiology · doi:10.2217/fmb-2022-0269 · Cited by 15 · see citing works
Alqahtani, A. (2022) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2022/6201067 · Cited by 13 · see citing works
RetractedArtificial Intelligence in the Paradigm Shift of Pharmaceutical Sciences: A Review
Tade, R. S., Jain, S. N., Satyavijay, J. T., Shah, P. N., Bari, T. D., Patil, T. M., & Shah, R. P. (2023) · Nano Biomedicine and Engineering · doi:10.26599/nbe.2023.9290043 · Cited by 19 · see citing works
Tarek, M. T., El-Gogary, R. I., & Kamel, A. O. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.125385 · Cited by 12 · see citing works
Introduction to Drug Repurposing: Exploring New Applications for Existing Drugs
Ahmad, Z., Rauf, A., Naz, S., & Hemeg, H. A. (2024) · Pharmaceutical science. · doi:10.5772/intechopen.113207 · Cited by 11 · see citing works
A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology
Sahu, A., Rathee, S., Saraf, S., & Jain, S. (2024) · Current Drug Targets · doi:10.2174/0113894501281290231221053939 · Cited by 9 · see citing works
Neuropharmacology and neurotherapeutics
Oyebanjo, O. T., Adetuyi, B. O., Adeoye, A. D., Adetuyi, O. A., Oni, P. G., & Ogunlana, O. O. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-16013-4.00019-1 · Cited by 9 · see citing works
Artificial Intelligence in Drug Discovery and Development: Transforming Pharmaceutical Innovation
Ali, M. S., Alqahtani, T., Shmrany, H. A., Gupta, G., Goh, K. W., Sahebkar, A., & Kesharwani, P. (2026) · Drug Development Research · doi:10.1002/ddr.70281 · Cited by 9 · see citing works
Physical-chemical repurposing of drugs. History of its formation in Russia
Ураков, А. Л., & Shabanov, P. D. (2023) · Reviews on Clinical Pharmacology and Drug Therapy · doi:10.17816/rcf567782 · Cited by 9 · see citing works
Carraro, C., Bonaguro, L., Schulte-Schrepping, J., Horne, A., Oestreich, M., Warnat‐Herresthal, S., Helbing, T., Franco, M. D., Händler, K., Mukherjee, S., et al. (2022) · eLife · doi:10.7554/elife.78012 · Cited by 9 · see citing works
Caldara, M., & Marmiroli, N. (2020) · Microorganisms · doi:10.3390/microorganisms8050742 · Cited by 8 · see citing works
Editorial: Model-informed drug development and evidence-based translational pharmacology
Zhao, J., Zhu, X., Tan, S., Chen, C., Kaddoumi, A., Guo, X., Lin, Y.-W., & Cheung, S. Y. A. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1086551 · Cited by 8 · see citing works
A Systematic Review of AI-Driven Innovations in Drug Development, Precision Medicine, and Healthcare
Uriti, S. V. (2025) · Journal of Pharma Insights and Research. · doi:10.69613/nvj8x122 · Cited by 7 · see citing works
Evaluation of Natural Peptides to Prevent and Reduce the Novel SARS-CoV-2 Infection
Paredes-Ramos, M., Piñeiro, E. C., Pérez‐Sánchez, H., & Vilariño, J. M. L. (2022) · Journal of Food Quality · doi:10.1155/2022/2102937 · Cited by 7 · see citing works
Biomarkers in drug development
Schuck, R. N., Delfino, J. G., Leptak, C., & Wagner, J. A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819869-8.00029-x · Cited by 7 · see citing works
Artificial Intelligence for Assessing Side Effects
Panchbhai, A. (2022) · Wiley eBooks · doi:10.1002/9781394167258.ch12 · Cited by 7 · see citing works
Artificial Intelligence perspectives in advancing Persian Herbal Medicine: A systematic review
Soltani, S., Khodaie, L., & Surana, V. (2025) · Advances in Integrative Medicine · doi:10.1016/j.aimed.2025.03.001 · Cited by 6 · see citing works
Dual-route embedding-aware graph neural networks for drug repositioning
Zhao, Y., Chen, Y., Du, J., Wen, J., Sun, Q., Wang, R., & Chen, C. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf555 · Cited by 6 · see citing works
Insight into Quantum Computing and Deep Learning Approach for Drug Design
Paliwal, D., Gudhanti, S. N. K. R., Yadav, D., & Raj, P. (2023) · Letters in Drug Design & Discovery · doi:10.2174/1570180820666230427151812 · Cited by 6 · see citing works
Machine Learning Applications in Drug Discovery
Ugurlu, S. Y., McDonald, D., & He, S. (2025) · French-Ukrainian Journal of Chemistry · doi:10.17721/fujcv13i1p1-66 · Cited by 6 · see citing works
Krishnamurthy, A., & Goel, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91172-6.00010-8 · Cited by 6 · see citing works
Transforming Human Resources With AI
Massoud, M. F., Maaliky, B., Fawal, A. E., Mawlawi, A. H., & Yahkni, F. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1046-5.ch011 · Cited by 6 · see citing works
Yasui, K., & Hamamoto, K. (2023) · AIP Advances · doi:10.1063/5.0141905 · Cited by 5 · see citing works
Deep learning-based discovery of compounds for blood pressure lowering effects
Li, R., Wu, T., Xu, X., Duan, X., & Wang, Y. (2025) · Scientific Reports · doi:10.1038/s41598-024-83924-0 · Cited by 4 · see citing works
Rentschler, M., Hohmann, S., Heuermann, P., Valenti, L., & Miehe, R. (2025) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2025.100484 · Cited by 4 · see citing works
Uso de la inteligencia artificial en la investigación para el reposicionamiento de fármacos
Olascoaga, S., Königsberg-Fainstein, M., Pérez‐Villanueva, J., & López‐Díazguerrero, N. E. (2022) · TIP Revista Especializada en Ciencias Químico-Biológicas · doi:10.22201/fesz.23958723e.2022.450 · Cited by 4 · see citing works
Drug repurposing prediction for COVID-19 using probabilistic networks and crowdsourced curation
Skelton, J., Alsobhe, A., Anastasi, E., Atallah, C., Je, B., Brown, B., Didon, D., Gater, P., James, K., Lennon, D. D., et al. (2020) · Northumbria Research Link (Northumbria University) · doi:10.48550/arxiv.2005.11088 · Cited by 4 · see citing works
Artificial intelligence in natural products research
Yuan, X., Yang, X., Pan, Q., Luo, C. Y., Luan, X., & Zhang, H. (2025) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(25)60902-2 · Cited by 3 · see citing works
ARSLANTAŞ, S. (2024) · Journal of Health Organization and Management · doi:10.1108/jhom-06-2024-0241 · Cited by 4 · see citing works
Molefi, T., Marima, R. M., Demetriou, D., Basera, A., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_13 · Cited by 4 · see citing works
Aplicação de algoritmos de machine learning na área farmacêutica: uma revisão
de Campos, T. C., & de Vasconcelos, T. C. L. (2021) · Research Society and Development · doi:10.33448/rsd-v10i15.22862 · Cited by 2 · see citing works
Ali, M., Wani, S. U. D., Khan, A. A., Dey, T., & Salahuddin, M. (2023) · Research Square · doi:10.21203/rs.3.rs-2724591/v1 · Cited by 3 · see citing works
A study on XAI-based drug identification system
Ramakrishnan, M., Nagamanickam, B., Ravikumar, D., Muruganandam, S., & Selvakumar, V. (2025) · AIP conference proceedings · doi:10.1063/5.0269093 · Cited by 2 · see citing works
Role of Artificial Intelligence and Machine Learning in Drug Discovery and Drug Repurposing
Quazi, S., & Fatima, Z. (2023) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-6684-6523-3.ch009 · Cited by 2 · see citing works
Deep learning in drug discovery
Bhati, M., Virmani, T., Kumar, G., Sharma, A., & Chitranshi, N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19413-9.00013-8 · Cited by 2 · see citing works
Arivanantham, S., Sekar, P., Sundhararajan, N., Nagalingam, Y., & Raju, S. K. (2023) · World Journal of Biology Pharmacy and Health Sciences · doi:10.30574/wjbphs.2023.16.2.0467 · Cited by 2 · see citing works
Production and development of novel drug targets through AI
Mahmoud, G. A., Riad, O. K. M., & Badawy, M. S. E. M. (2024) · Methods in microbiology · doi:10.1016/bs.mim.2024.05.002 · Cited by 2 · see citing works
Genomic Approaches for Drug Repositioning
Farouk, I. A., Low, Z. Y., Puniyamurti, A., Zabidi, N. Z., Amin, M. K. B. A., & Lal, S. K. (2022) · Springer eBooks · doi:10.1007/978-981-16-9232-1_5 · Cited by 2 · see citing works
Artificial intelligence in the treatment of cancer: Changing patterns, constraints, and prospects
Ali, M., Wani, S. U. D., Dey, T., & Mehdi, S. (2024) · Health and Technology · doi:10.1007/s12553-024-00825-y · Cited by 2 · see citing works
El-Gawad, S. A., Mikhael, Y., & Arafa, R. K. (2024) · Interdisciplinary cancer research · doi:10.1007/16833_2024_395 · Cited by 1 · see citing works
Artificial Intelligence and Machine Learning Approaches in Radiology for Medical Imaging Diagnostics
Gomase, V. S., & Ghatule, A. P. (2025) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965407792250811093550 · Cited by 1 · see citing works
Shin, G., & Lee, H. (2025) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-10-2024-0746 · Cited by 1 · see citing works
Guo, X., Zhao, Y., Rong, X., Niu, C., Hu, S., Liu, Q., & You, F. (2025) · Advanced Science · doi:10.1002/advs.202413775 · Cited by 1 · see citing works
Role of Computational Science and Artificial Intelligence in Understanding Climate Change
Thakur, A., Kumar, A., Singh, K., & Raina, P. (2025) · Springer eBooks · doi:10.1007/978-981-97-9859-9_83 · Cited by 1 · see citing works
Al-Driven Regulatory Affairs: Current Applications and Future Directions in Drug Development
Patel, T. B., Prajapati, A., Narkhede, S., Luhar, S., & Thakor, M. (2025) · Research Journal of Science and Technology · doi:10.52711/2349-2988.2025.00042 · Cited by 1 · see citing works
AI technologies for smart pharmacology
Vișan, A. I., & Neguț, I. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00003-8 · Cited by 1 · see citing works
Zhou, Q., Sun, L., Ou, Y., Liu, H., Gao, J., Dai, R., Li, Y., Cheng, G., Tao, Y., Pan, Y., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag107 · Cited by 1 · see citing works
- Authors
- Koromina, M., Pandi, M.-T., & Patrinos, G. P.
- Affiliations
- University of Patras (GR)
- Corresponding author
- Patrinos, George P.
- Venue
- OMICS A Journal of Integrative Biology
- Publisher
- SAGE Publications
- Published
- 2019-11
- Volume
- 23
- Issue
- 11
- Pages
- 539-548
- ISSN
- 1536-2310 (ISSN-L)
- 1557-8100
- OpenAlex
- W2981458781
- Cited by
- 140
- References
- 74
- Citations by year
- 2026: 14 · 2025: 28 · 2024: 25 · 2023: 23 · 2022: 21 · 2021: 16 · 2020: 13
- FWCI
- 8.49
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- OMICS A Journal of Integrative Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- drug repurposing, drug development, artificial intelligence, machine learning, omics databases, computational drug discovery, precision medicine, big data, pharmaceutical industry
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Models, Theoretical, Reproducibility of Results, Computational Biology, Genomics, Drug Discovery, Drug Repositioning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review