Machine Learning and Artificial Intelligence in drug repurposing – challenges and perspectives

Artificial Intelligence (AI) and Machine Learning (ML) techniques play an increasingly crucial role in the field of drug repurposing.As the number of computational tools grows, it is essential to not only understand and carefully select the method itself, but also consider the input data used for building predictive models.

This review aims to take a dive into current computational methods that leverage AI and ML to drive and accelerate compound and drug target selection, in addition to address the existing challenges and provide perspectives.While there is no doubt that AI and ML-based tools are transforming traditional approaches, especially with recent advancements in graph-based methods, they present novel challenges that require the human eye and expert intervention. The growing complexity of OMICs data further emphasizes the importance of data standardization and quality.

Content

Author and article information

Journal

Title: DrugRxiv

Publisher: REPO4EU

Publication date (Electronic preprint): 12 March 2024

Affiliations

[1 ] Discovery and Translational Sciences (DTS), Clarivate Analytics, Barcelona (Spain);

[2 ] Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising (Germany);

[3 ] Discovery Sciences, Research and Early Development, BioPharmaceuticals R&D, AstraZeneca, Cambridge (UK);

[4 ] Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid (Spain), Centro de Tecnología Biomédica, Universidad Politécnica de Madrid (Spain);

[5 ] Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki (Finland), BioICAWtech, Helsinki (Finland);

Author notes

[* ]Email: sarah.bonnin@ 123456clarivate.com .

Author information

Ezequiel Anokian https://orcid.org/0000-0003-0694-1867

Judith Bernett https://orcid.org/0000-0001-5812-8013

Adrian Freeman https://orcid.org/0009-0007-0413-5657

Markus List https://orcid.org/0000-0002-0941-4168

Lucía Prieto Santamaría https://orcid.org/0000-0003-1545-3515

Ziaurrehman Tanoli https://orcid.org/0000-0003-2435-9862

Sarah Bonnin https://orcid.org/0000-0001-5159-2518

Article

DOI: 10.58647/DRUGARXIV.PR000007.v1

SO-VID: ee3bc77a-ec7a-4175-a871-4bc2dcf065f1

License:

This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 12 March 2024

References

Mullard Asher. 2018 FDA drug approvals. Nature Reviews Drug Discovery. Vol. 18(2):85–89. 2019. Springer Science and Business Media LLC. [Cross Ref]
Li Jiao, Lu Zhiyong. A new method for computational drug repositioning using drug pairwise similarity. 2012 IEEE International Conference on Bioinformatics and Biomedicine. 2012. IEEE. [Cross Ref]
Park Kyungsoo. A review of computational drug repurposing. Translational and Clinical Pharmacology. Vol. 27(2)2019. Korean Society for Clinical Pharmacology and Therapeutics. [Cross Ref]
Jáñez-Martino Francisco, Alaiz-Rodríguez Rocío, González-Castro Víctor, Fidalgo Eduardo, Alegre Enrique. A review of spam email detection: analysis of spammer strategies and the dataset shift problem. Artificial Intelligence Review. Vol. 56(2):1145–1173. 2023. Springer Science and Business Media LLC. [Cross Ref]
Axen Seth D., Huang Xi-Ping, Cáceres Elena L., Gendelev Leo, Roth Bryan L., Keiser Michael J.. A Simple Representation of Three-Dimensional Molecular Structure. Journal of Medicinal Chemistry. Vol. 60(17):7393–7409. 2017. American Chemical Society (ACS). [Cross Ref]
Brown Adam S., Patel Chirag J.. A standard database for drug repositioning. Scientific Data. Vol. 4(1)2017. Springer Science and Business Media LLC. [Cross Ref]
Firoozbakht Farzaneh, Yousefi Behnam, Schwikowski Benno. An overview of machine learning methods for monotherapy drug response prediction. Briefings in Bioinformatics. Vol. 23(1)2022. Oxford University Press (OUP). [Cross Ref]
Schuhmacher Alexander, Hinder Markus, von Stegmann und Stein Alexander, Hartl Dominik, Gassmann Oliver. Analysis of pharma R&D productivity – a new perspective needed. Drug Discovery Today. Vol. 28(10)2023. Elsevier BV. [Cross Ref]
Yuskovych-Zhukovska Valentyna, Poplavska Tetiana, Diachenko Oksana, Mishenina Tetiana, Topolnyk Yana, Gurevych Roman. Application of Artificial Intelligence in Education. Problems and Opportunities for Sustainable Development. BRAIN. Broad Research in Artificial Intelligence and Neuroscience. Vol. 13(1Sup1):339–356. 2022. Asociatia LUMEN. [Cross Ref]
Van de Sande Bram, Lee Joon Sang, Mutasa-Gottgens Euphemia, Naughton Bart, Bacon Wendi, Manning Jonathan, Wang Yong, Pollard Jack, Mendez Melissa, Hill Jon, Kumar Namit, Cao Xiaohong, Chen Xiao, Khaladkar Mugdha, Wen Ji, Leach Andrew, Ferran Edgardo. Applications of single-cell RNA sequencing in drug discovery and development. Nature Reviews Drug Discovery. Vol. 22(6):496–520. 2023. Springer Science and Business Media LLC. [Cross Ref]
Harrer Stefan, Shah Pratik, Antony Bhavna, Hu Jianying. Artificial Intelligence for Clinical Trial Design. Trends in Pharmacological Sciences. Vol. 40(8):577–591. 2019. Elsevier BV. [Cross Ref]
Paul Debleena, Sanap Gaurav, Shenoy Snehal, Kalyane Dnyaneshwar, Kalia Kiran, Tekade Rakesh K.. Artificial intelligence in drug discovery and development. Drug Discovery Today. Vol. 26(1):80–93. 2021. Elsevier BV. [Cross Ref]
Vora Lalitkumar K., Gholap Amol D., Jetha Keshava, Thakur Raghu Raj Singh, Solanki Hetvi K., Chavda Vivek P.. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics. Vol. 15(7)2023. MDPI AG. [Cross Ref]
Gupta Rohan, Srivastava Devesh, Sahu Mehar, Tiwari Swati, Ambasta Rashmi K., Kumar Pravir. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular Diversity. Vol. 25(3):1315–1360. 2021. Springer Science and Business Media LLC. [Cross Ref]
Luo Huimin, Li Min, Yang Mengyun, Wu Fang-Xiang, Li Yaohang, Wang Jianxin. Biomedical data and computational models for drug repositioning: a comprehensive review. Briefings in Bioinformatics. Vol. 22(2):1604–1619. 2021. Oxford University Press (OUP). [Cross Ref]
Sadybekov Anastasiia V., Katritch Vsevolod. Computational approaches streamlining drug discovery. Nature. Vol. 616(7958):673–685. 2023. Springer Science and Business Media LLC. [Cross Ref]
Dudley Joel T., Sirota Marina, Shenoy Mohan, Pai Reetesh K., Roedder Silke, Chiang Annie P., Morgan Alex A., Sarwal Minnie M., Pasricha Pankaj Jay, Butte Atul J.. Computational Repositioning of the Anticonvulsant Topiramate for Inflammatory Bowel Disease. Science Translational Medicine. Vol. 3(96)2011. American Association for the Advancement of Science (AAAS). [Cross Ref]
Yang Jingbo, Cai Yiyang, Zhao Kairui, Xie Hongbo, Chen Xiujie. Concepts and applications of chemical fingerprint for hit and lead screening. Drug Discovery Today. Vol. 27(11)2022. Elsevier BV. [Cross Ref]
Bayer Florian P., Gander Manuel, Kuster Bernhard, The Matthew. CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose–response curves. Nature Communications. Vol. 14(1)2023. Springer Science and Business Media LLC. [Cross Ref]
Fernández-de Gortari Eli, García-Jacas César R., Martinez-Mayorga Karina, Medina-Franco José L.. Database fingerprint (DFP): an approach to represent molecular databases. Journal of Cheminformatics. Vol. 9(1)2017. Springer Science and Business Media LLC. [Cross Ref]
Low Zheng Yao, Farouk Isra Ahmad, Lal Sunil Kumar. Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak. Viruses. Vol. 12(9)2020. MDPI AG. [Cross Ref]
Pushpakom Sudeep, Iorio Francesco, Eyers Patrick A., Escott K. Jane, Hopper Shirley, Wells Andrew, Doig Andrew, Guilliams Tim, Latimer Joanna, McNamee Christine, Norris Alan, Sanseau Philippe, Cavalla David, Pirmohamed Munir. Drug repurposing: progress, challenges and recommendations. Nature Reviews Drug Discovery. Vol. 18(1):41–58. 2019. Springer Science and Business Media LLC. [Cross Ref]
Wang Yinyin, Aldahdooh Jehad, Hu Yingying, Yang Hongbin, Vähä-Koskela Markus, Tang Jing, Tanoli Ziaurrehman. DrugRepo: a novel approach to repurposing drugs based on chemical and genomic features. Scientific Reports. Vol. 12(1)2022. Springer Science and Business Media LLC. [Cross Ref]
Hall Lowell H., Kier Lemont B.. Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information. Journal of Chemical Information and Computer Sciences. Vol. 35(6):1039–1045. 1995. American Chemical Society (ACS). [Cross Ref]
Tanoli Ziaurrehman, Seemab Umair, Scherer Andreas, Wennerberg Krister, Tang Jing, Vähä-Koskela Markus. Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Briefings in Bioinformatics. Vol. 22(2):1656–1678. 2021. Oxford University Press (OUP). [Cross Ref]
Yang Wanjuan, Soares Jorge, Greninger Patricia, Edelman Elena J., Lightfoot Howard, Forbes Simon, Bindal Nidhi, Beare Dave, Smith James A., Thompson I. Richard, Ramaswamy Sridhar, Futreal P. Andrew, Haber Daniel A., Stratton Michael R., Benes Cyril, McDermott Ultan, Garnett Mathew J.. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research. Vol. 41(D1)2012. Oxford University Press (OUP). [Cross Ref]
Zhang Bin, Zhang Lu, Chen Qiuying, Jin Zhe, Liu Shuyi, Zhang Shuixing. Harnessing artificial intelligence to improve clinical trial design. Communications Medicine. Vol. 3(1)2023. Springer Science and Business Media LLC. [Cross Ref]
Gao Hui, Korn Joshua M, Ferretti Stéphane, Monahan John E, Wang Youzhen, Singh Mallika, Zhang Chao, Schnell Christian, Yang Guizhi, Zhang Yun, Balbin O Alejandro, Barbe Stéphanie, Cai Hongbo, Casey Fergal, Chatterjee Susmita, Chiang Derek Y, Chuai Shannon, Cogan Shawn M, Collins Scott D, Dammassa Ernesta, Ebel Nicolas, Embry Millicent, Green John, Kauffmann Audrey, Kowal Colleen, Leary Rebecca J, Lehar Joseph, Liang Ying, Loo Alice, Lorenzana Edward, Robert McDonald E, McLaughlin Margaret E, Merkin Jason, Meyer Ronald, Naylor Tara L, Patawaran Montesa, Reddy Anupama, Röelli Claudia, Ruddy David A, Salangsang Fernando, Santacroce Francesca, Singh Angad P, Tang Yan, Tinetto Walter, Tobler Sonja, Velazquez Roberto, Venkatesan Kavitha, Von Arx Fabian, Wang Hui Qin, Wang Zongyao, Wiesmann Marion, Wyss Daniel, Xu Fiona, Bitter Hans, Atadja Peter, Lees Emma, Hofmann Francesco, Li En, Keen Nicholas, Cozens Robert, Jensen Michael Rugaard, Pryer Nancy K, Williams Juliet A, Sellers William R. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nature Medicine. Vol. 21(11):1318–1325. 2015. Springer Science and Business Media LLC. [Cross Ref]
Jumper John, Evans Richard, Pritzel Alexander, Green Tim, Figurnov Michael, Ronneberger Olaf, Tunyasuvunakool Kathryn, Bates Russ, Žídek Augustin, Potapenko Anna, Bridgland Alex, Meyer Clemens, Kohl Simon A. A., Ballard Andrew J., Cowie Andrew, Romera-Paredes Bernardino, Nikolov Stanislav, Jain Rishub, Adler Jonas, Back Trevor, Petersen Stig, Reiman David, Clancy Ellen, Zielinski Michal, Steinegger Martin, Pacholska Michalina, Berghammer Tamas, Bodenstein Sebastian, Silver David, Vinyals Oriol, Senior Andrew W., Kavukcuoglu Koray, Kohli Pushmeet, Hassabis Demis. Highly accurate protein structure prediction with AlphaFold. Nature. Vol. 596(7873):583–589. 2021. Springer Science and Business Media LLC. [Cross Ref]
Talaviya Tanha, Shah Dhara, Patel Nivedita, Yagnik Hiteshri, Shah Manan. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture. Vol. 4:58–73. 2020. Elsevier BV. [Cross Ref]
Li Yihui, Hostallero David Earl, Emad Amin. Interpretable deep learning architectures for improving drug response prediction performance: myth or reality? Bioinformatics. Vol. 39(6)2023. Oxford University Press (OUP). [Cross Ref]
Kapoor Sayash, Narayanan Arvind. Leakage and the reproducibility crisis in machine-learning-based science. Patterns. Vol. 4(9)2023. Elsevier BV. [Cross Ref]
Issa Naiem T., Stathias Vasileios, Schürer Stephan, Dakshanamurthy Sivanesan. Machine and deep learning approaches for cancer drug repurposing. Seminars in Cancer Biology. Vol. 68:132–142. 2021. Elsevier BV. [Cross Ref]
Adam George, Rampášek Ladislav, Safikhani Zhaleh, Smirnov Petr, Haibe-Kains Benjamin, Goldenberg Anna. Machine learning approaches to drug response prediction: challenges and recent progress. npj Precision Oncology. Vol. 4(1)2020. Springer Science and Business Media LLC. [Cross Ref]
Cong Yi, Endo Toshinori. Multi-Omics and Artificial Intelligence-Guided Drug Repositioning: Prospects, Challenges, and Lessons Learned from COVID-19. OMICS: A Journal of Integrative Biology. Vol. 26(7):361–371. 2022. Mary Ann Liebert Inc. [Cross Ref]
Sadegh Sepideh, Skelton James, Anastasi Elisa, Bernett Judith, Blumenthal David B., Galindez Gihanna, Salgado-Albarrán Marisol, Lazareva Olga, Flanagan Keith, Cockell Simon, Nogales Cristian, Casas Ana I., Schmidt Harald H. H. W., Baumbach Jan, Wipat Anil, Kacprowski Tim. Network medicine for disease module identification and drug repurposing with the NeDRex platform. Nature Communications. Vol. 12(1)2021. Springer Science and Business Media LLC. [Cross Ref]
Le Tuan, Winter Robin, Noé Frank, Clevert Djork-Arné. Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures. Chemical Science. Vol. 11(38):10378–10389. 2020. Royal Society of Chemistry (RSC). [Cross Ref]
Ghandi Mahmoud, Huang Franklin W., Jané-Valbuena Judit, Kryukov Gregory V., Lo Christopher C., McDonald E. Robert, Barretina Jordi, Gelfand Ellen T., Bielski Craig M., Li Haoxin, Hu Kevin, Andreev-Drakhlin Alexander Y., Kim Jaegil, Hess Julian M., Haas Brian J., Aguet François, Weir Barbara A., Rothberg Michael V., Paolella Brenton R., Lawrence Michael S., Akbani Rehan, Lu Yiling, Tiv Hong L., Gokhale Prafulla C., de Weck Antoine, Mansour Ali Amin, Oh Coyin, Shih Juliann, Hadi Kevin, Rosen Yanay, Bistline Jonathan, Venkatesan Kavitha, Reddy Anupama, Sonkin Dmitriy, Liu Manway, Lehar Joseph, Korn Joshua M., Porter Dale A., Jones Michael D., Golji Javad, Caponigro Giordano, Taylor Jordan E., Dunning Caitlin M., Creech Amanda L., Warren Allison C., McFarland James M., Zamanighomi Mahdi, Kauffmann Audrey, Stransky Nicolas, Imielinski Marcin, Maruvka Yosef E., Cherniack Andrew D., Tsherniak Aviad, Vazquez Francisca, Jaffe Jacob D., Lane Andrew A., Weinstock David M., Johannessen Cory M., Morrissey Michael P., Stegmeier Frank, Schlegel Robert, Hahn William C., Getz Gad, Mills Gordon B., Boehm Jesse S., Golub Todd R., Garraway Levi A., Sellers William R.. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. Vol. 569(7757):503–508. 2019. Springer Science and Business Media LLC. [Cross Ref]
March-Vila Eric, Pinzi Luca, Sturm Noé, Tinivella Annachiara, Engkvist Ola, Chen Hongming, Rastelli Giulio. On the Integration of In Silico Drug Design Methods for Drug Repurposing. Frontiers in Pharmacology. Vol. 8:2017. Frontiers Media SA. [Cross Ref]
You Wenjie, Yang Zijiang, Ji Guoli. PLS-based recursive feature elimination for high-dimensional small sample. Knowledge-Based Systems. Vol. 55:15–28. 2014. Elsevier BV. [Cross Ref]
Gottlieb Assaf, Stein Gideon Y, Ruppin Eytan, Sharan Roded. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular Systems Biology. Vol. 7(1)2011. Springer Science and Business Media LLC. [Cross Ref]
Wang Jianlin, Wang Wenxiu, Yan Chaokun, Luo Junwei, Zhang Ge. Predicting Drug-Disease Association Based on Ensemble Strategy. Frontiers in Genetics. Vol. 12:2021. Frontiers Media SA. [Cross Ref]
Durant Joseph L., Leland Burton A., Henry Douglas R., Nourse James G.. Reoptimization of MDL Keys for Use in Drug Discovery. Journal of Chemical Information and Computer Sciences. Vol. 42(6):1273–1280. 2002. American Chemical Society (ACS). [Cross Ref]
Ahangari Farida, Becker Christine, Foster Daniel G., Chioccioli Maurizio, Nelson Meghan, Beke Keriann, Wang Xing, Justet Aurelien, Adams Taylor, Readhead Benjamin, Meador Carly, Correll Kelly, Lili Loukia N., Roybal Helen M., Rose Kadi-Ann, Ding Shuizi, Barnthaler Thomas, Briones Natalie, DeIuliis Giuseppe, Schupp Jonas C., Li Qin, Omote Norihito, Aschner Yael, Sharma Lokesh, Kopf Katrina W., Magnusson Björn, Hicks Ryan, Backmark Anna, Dela Cruz Charles S., Rosas Ivan, Cousens Leslie P., Dudley Joel T., Kaminski Naftali, Downey Gregory P.. Saracatinib, a Selective Src Kinase Inhibitor, Blocks Fibrotic Responses in Preclinical Models of Pulmonary Fibrosis. American Journal of Respiratory and Critical Care Medicine. Vol. 206(12):1463–1479. 2022. American Thoracic Society. [Cross Ref]
Shameer Khader, Glicksberg Benjamin S, Hodos Rachel, Johnson Kipp W, Badgeley Marcus A, Readhead Ben, Tomlinson Max S, O’Connor Timothy, Miotto Riccardo, Kidd Brian A, Chen Rong, Ma’ayan Avi, Dudley Joel T. Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. Briefings in Bioinformatics. Vol. 19(4):656–678. 2018. Oxford University Press (OUP). [Cross Ref]
Peek C. J., Glasgow R. E., Stange K. C., Klesges L. M., Purcell E. P., Kessler R. S.. The 5 R's: An Emerging Bold Standard for Conducting Relevant Research in a Changing World. The Annals of Family Medicine. Vol. 12(5):447–455. 2014. Annals of Family Medicine. [Cross Ref]
Zdrazil Barbara, Felix Eloy, Hunter Fiona, Manners Emma J, Blackshaw James, Corbett Sybilla, de Veij Marleen, Ioannidis Harris, Lopez David Mendez, Mosquera Juan F, Magarinos Maria Paula, Bosc Nicolas, Arcila Ricardo, Kizilören Tevfik, Gaulton Anna, Bento A Patrícia, Adasme Melissa F, Monecke Peter, Landrum Gregory A, Leach Andrew R. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Research. Vol. 52(D1)2024. Oxford University Press (OUP). [Cross Ref]
Lamb Justin, Crawford Emily D., Peck David, Modell Joshua W., Blat Irene C., Wrobel Matthew J., Lerner Jim, Brunet Jean-Philippe, Subramanian Aravind, Ross Kenneth N., Reich Michael, Hieronymus Haley, Wei Guo, Armstrong Scott A., Haggarty Stephen J., Clemons Paul A., Wei Ru, Carr Steven A., Lander Eric S., Golub Todd R.. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science. Vol. 313(5795):1929–1935. 2006. American Association for the Advancement of Science (AAAS). [Cross Ref]
Morgan Steve, Grootendorst Paul, Lexchin Joel, Cunningham Colleen, Greyson Devon. The cost of drug development: A systematic review. Health Policy. Vol. 100(1):4–17. 2011. Elsevier BV. [Cross Ref]
Corsello Steven M, Bittker Joshua A, Liu Zihan, Gould Joshua, McCarren Patrick, Hirschman Jodi E, Johnston Stephen E, Vrcic Anita, Wong Bang, Khan Mariya, Asiedu Jacob, Narayan Rajiv, Mader Christopher C, Subramanian Aravind, Golub Todd R. The Drug Repurposing Hub: a next-generation drug library and information resource. Nature Medicine. Vol. 23(4):405–408. 2017. Springer Science and Business Media LLC. [Cross Ref]
Shoemaker Robert H.. The NCI60 human tumour cell line anticancer drug screen. Nature Reviews Cancer. Vol. 6(10):813–823. 2006. Springer Science and Business Media LLC. [Cross Ref]
Ochoa David, Hercules Andrew, Carmona Miguel, Suveges Daniel, Baker Jarrod, Malangone Cinzia, Lopez Irene, Miranda Alfredo, Cruz-Castillo Carlos, Fumis Luca, Bernal-Llinares Manuel, Tsukanov Kirill, Cornu Helena, Tsirigos Konstantinos, Razuvayevskaya Olesya, Buniello Annalisa, Schwartzentruber Jeremy, Karim Mohd, Ariano Bruno, Martinez Osorio Ricardo Esteban, Ferrer Javier, Ge Xiangyu, Machlitt-Northen Sandra, Gonzalez-Uriarte Asier, Saha Shyamasree, Tirunagari Santosh, Mehta Chintan, Roldán-Romero Juan María, Horswell Stuart, Young Sarah, Ghoussaini Maya, Hulcoop David G, Dunham Ian, McDonagh Ellen M. The next-generation Open Targets Platform: reimagined, redesigned, rebuilt. Nucleic Acids Research. Vol. 51(D1)2023. Oxford University Press (OUP). [Cross Ref]
Blanco-González Alexandre, Cabezón Alfonso, Seco-González Alejandro, Conde-Torres Daniel, Antelo-Riveiro Paula, Piñeiro Ángel, Garcia-Fandino Rebeca. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals. Vol. 16(6)2023. MDPI AG. [Cross Ref]
Park Kyungsoo. The use of real-world data in drug repurposing. Translational and Clinical Pharmacology. Vol. 29(3)2021. Korean Society for Clinical Pharmacology and Therapeutics. [Cross Ref]
Ayuso-Muñoz Adrián, Prieto-Santamaría Lucía, Ugarte-Carro Esther, Serrano Emilio, Rodríguez-González Alejandro. Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data. Artificial Intelligence in Medicine. Vol. 145:2023. Elsevier BV. [Cross Ref]

Comments

Comment on this article

[1] Mullard Asher. 2018 FDA drug approvals. Nature Reviews Drug Discovery. Vol. 18(2):85–89. 2019. Springer Science and Business Media LLC. [Cross Ref]

[2] Li Jiao, Lu Zhiyong. A new method for computational drug repositioning using drug pairwise similarity. 2012 IEEE International Conference on Bioinformatics and Biomedicine. 2012. IEEE. [Cross Ref]

[3] Park Kyungsoo. A review of computational drug repurposing. Translational and Clinical Pharmacology. Vol. 27(2)2019. Korean Society for Clinical Pharmacology and Therapeutics. [Cross Ref]

[4] Jáñez-Martino Francisco, Alaiz-Rodríguez Rocío, González-Castro Víctor, Fidalgo Eduardo, Alegre Enrique. A review of spam email detection: analysis of spammer strategies and the dataset shift problem. Artificial Intelligence Review. Vol. 56(2):1145–1173. 2023. Springer Science and Business Media LLC. [Cross Ref]

[5] Axen Seth D., Huang Xi-Ping, Cáceres Elena L., Gendelev Leo, Roth Bryan L., Keiser Michael J.. A Simple Representation of Three-Dimensional Molecular Structure. Journal of Medicinal Chemistry. Vol. 60(17):7393–7409. 2017. American Chemical Society (ACS). [Cross Ref]

[6] Brown Adam S., Patel Chirag J.. A standard database for drug repositioning. Scientific Data. Vol. 4(1)2017. Springer Science and Business Media LLC. [Cross Ref]

[7] Firoozbakht Farzaneh, Yousefi Behnam, Schwikowski Benno. An overview of machine learning methods for monotherapy drug response prediction. Briefings in Bioinformatics. Vol. 23(1)2022. Oxford University Press (OUP). [Cross Ref]

[8] Schuhmacher Alexander, Hinder Markus, von Stegmann und Stein Alexander, Hartl Dominik, Gassmann Oliver. Analysis of pharma R&D productivity – a new perspective needed. Drug Discovery Today. Vol. 28(10)2023. Elsevier BV. [Cross Ref]

[9] Yuskovych-Zhukovska Valentyna, Poplavska Tetiana, Diachenko Oksana, Mishenina Tetiana, Topolnyk Yana, Gurevych Roman. Application of Artificial Intelligence in Education. Problems and Opportunities for Sustainable Development. BRAIN. Broad Research in Artificial Intelligence and Neuroscience. Vol. 13(1Sup1):339–356. 2022. Asociatia LUMEN. [Cross Ref]

[10] Van de Sande Bram, Lee Joon Sang, Mutasa-Gottgens Euphemia, Naughton Bart, Bacon Wendi, Manning Jonathan, Wang Yong, Pollard Jack, Mendez Melissa, Hill Jon, Kumar Namit, Cao Xiaohong, Chen Xiao, Khaladkar Mugdha, Wen Ji, Leach Andrew, Ferran Edgardo. Applications of single-cell RNA sequencing in drug discovery and development. Nature Reviews Drug Discovery. Vol. 22(6):496–520. 2023. Springer Science and Business Media LLC. [Cross Ref]

[11] Harrer Stefan, Shah Pratik, Antony Bhavna, Hu Jianying. Artificial Intelligence for Clinical Trial Design. Trends in Pharmacological Sciences. Vol. 40(8):577–591. 2019. Elsevier BV. [Cross Ref]

[12] Paul Debleena, Sanap Gaurav, Shenoy Snehal, Kalyane Dnyaneshwar, Kalia Kiran, Tekade Rakesh K.. Artificial intelligence in drug discovery and development. Drug Discovery Today. Vol. 26(1):80–93. 2021. Elsevier BV. [Cross Ref]

[13] Vora Lalitkumar K., Gholap Amol D., Jetha Keshava, Thakur Raghu Raj Singh, Solanki Hetvi K., Chavda Vivek P.. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics. Vol. 15(7)2023. MDPI AG. [Cross Ref]

[14] Gupta Rohan, Srivastava Devesh, Sahu Mehar, Tiwari Swati, Ambasta Rashmi K., Kumar Pravir. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular Diversity. Vol. 25(3):1315–1360. 2021. Springer Science and Business Media LLC. [Cross Ref]

[15] Luo Huimin, Li Min, Yang Mengyun, Wu Fang-Xiang, Li Yaohang, Wang Jianxin. Biomedical data and computational models for drug repositioning: a comprehensive review. Briefings in Bioinformatics. Vol. 22(2):1604–1619. 2021. Oxford University Press (OUP). [Cross Ref]

[16] Sadybekov Anastasiia V., Katritch Vsevolod. Computational approaches streamlining drug discovery. Nature. Vol. 616(7958):673–685. 2023. Springer Science and Business Media LLC. [Cross Ref]

[17] Dudley Joel T., Sirota Marina, Shenoy Mohan, Pai Reetesh K., Roedder Silke, Chiang Annie P., Morgan Alex A., Sarwal Minnie M., Pasricha Pankaj Jay, Butte Atul J.. Computational Repositioning of the Anticonvulsant Topiramate for Inflammatory Bowel Disease. Science Translational Medicine. Vol. 3(96)2011. American Association for the Advancement of Science (AAAS). [Cross Ref]

[18] Yang Jingbo, Cai Yiyang, Zhao Kairui, Xie Hongbo, Chen Xiujie. Concepts and applications of chemical fingerprint for hit and lead screening. Drug Discovery Today. Vol. 27(11)2022. Elsevier BV. [Cross Ref]

[19] Bayer Florian P., Gander Manuel, Kuster Bernhard, The Matthew. CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose–response curves. Nature Communications. Vol. 14(1)2023. Springer Science and Business Media LLC. [Cross Ref]

[20] Fernández-de Gortari Eli, García-Jacas César R., Martinez-Mayorga Karina, Medina-Franco José L.. Database fingerprint (DFP): an approach to represent molecular databases. Journal of Cheminformatics. Vol. 9(1)2017. Springer Science and Business Media LLC. [Cross Ref]

[21] Low Zheng Yao, Farouk Isra Ahmad, Lal Sunil Kumar. Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak. Viruses. Vol. 12(9)2020. MDPI AG. [Cross Ref]

[22] Pushpakom Sudeep, Iorio Francesco, Eyers Patrick A., Escott K. Jane, Hopper Shirley, Wells Andrew, Doig Andrew, Guilliams Tim, Latimer Joanna, McNamee Christine, Norris Alan, Sanseau Philippe, Cavalla David, Pirmohamed Munir. Drug repurposing: progress, challenges and recommendations. Nature Reviews Drug Discovery. Vol. 18(1):41–58. 2019. Springer Science and Business Media LLC. [Cross Ref]

[23] Wang Yinyin, Aldahdooh Jehad, Hu Yingying, Yang Hongbin, Vähä-Koskela Markus, Tang Jing, Tanoli Ziaurrehman. DrugRepo: a novel approach to repurposing drugs based on chemical and genomic features. Scientific Reports. Vol. 12(1)2022. Springer Science and Business Media LLC. [Cross Ref]

[24] Hall Lowell H., Kier Lemont B.. Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information. Journal of Chemical Information and Computer Sciences. Vol. 35(6):1039–1045. 1995. American Chemical Society (ACS). [Cross Ref]

[25] Tanoli Ziaurrehman, Seemab Umair, Scherer Andreas, Wennerberg Krister, Tang Jing, Vähä-Koskela Markus. Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Briefings in Bioinformatics. Vol. 22(2):1656–1678. 2021. Oxford University Press (OUP). [Cross Ref]

[26] Yang Wanjuan, Soares Jorge, Greninger Patricia, Edelman Elena J., Lightfoot Howard, Forbes Simon, Bindal Nidhi, Beare Dave, Smith James A., Thompson I. Richard, Ramaswamy Sridhar, Futreal P. Andrew, Haber Daniel A., Stratton Michael R., Benes Cyril, McDermott Ultan, Garnett Mathew J.. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research. Vol. 41(D1)2012. Oxford University Press (OUP). [Cross Ref]

[27] Zhang Bin, Zhang Lu, Chen Qiuying, Jin Zhe, Liu Shuyi, Zhang Shuixing. Harnessing artificial intelligence to improve clinical trial design. Communications Medicine. Vol. 3(1)2023. Springer Science and Business Media LLC. [Cross Ref]

[28] Gao Hui, Korn Joshua M, Ferretti Stéphane, Monahan John E, Wang Youzhen, Singh Mallika, Zhang Chao, Schnell Christian, Yang Guizhi, Zhang Yun, Balbin O Alejandro, Barbe Stéphanie, Cai Hongbo, Casey Fergal, Chatterjee Susmita, Chiang Derek Y, Chuai Shannon, Cogan Shawn M, Collins Scott D, Dammassa Ernesta, Ebel Nicolas, Embry Millicent, Green John, Kauffmann Audrey, Kowal Colleen, Leary Rebecca J, Lehar Joseph, Liang Ying, Loo Alice, Lorenzana Edward, Robert McDonald E, McLaughlin Margaret E, Merkin Jason, Meyer Ronald, Naylor Tara L, Patawaran Montesa, Reddy Anupama, Röelli Claudia, Ruddy David A, Salangsang Fernando, Santacroce Francesca, Singh Angad P, Tang Yan, Tinetto Walter, Tobler Sonja, Velazquez Roberto, Venkatesan Kavitha, Von Arx Fabian, Wang Hui Qin, Wang Zongyao, Wiesmann Marion, Wyss Daniel, Xu Fiona, Bitter Hans, Atadja Peter, Lees Emma, Hofmann Francesco, Li En, Keen Nicholas, Cozens Robert, Jensen Michael Rugaard, Pryer Nancy K, Williams Juliet A, Sellers William R. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nature Medicine. Vol. 21(11):1318–1325. 2015. Springer Science and Business Media LLC. [Cross Ref]

[29] Jumper John, Evans Richard, Pritzel Alexander, Green Tim, Figurnov Michael, Ronneberger Olaf, Tunyasuvunakool Kathryn, Bates Russ, Žídek Augustin, Potapenko Anna, Bridgland Alex, Meyer Clemens, Kohl Simon A. A., Ballard Andrew J., Cowie Andrew, Romera-Paredes Bernardino, Nikolov Stanislav, Jain Rishub, Adler Jonas, Back Trevor, Petersen Stig, Reiman David, Clancy Ellen, Zielinski Michal, Steinegger Martin, Pacholska Michalina, Berghammer Tamas, Bodenstein Sebastian, Silver David, Vinyals Oriol, Senior Andrew W., Kavukcuoglu Koray, Kohli Pushmeet, Hassabis Demis. Highly accurate protein structure prediction with AlphaFold. Nature. Vol. 596(7873):583–589. 2021. Springer Science and Business Media LLC. [Cross Ref]

[30] Talaviya Tanha, Shah Dhara, Patel Nivedita, Yagnik Hiteshri, Shah Manan. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture. Vol. 4:58–73. 2020. Elsevier BV. [Cross Ref]

[31] Li Yihui, Hostallero David Earl, Emad Amin. Interpretable deep learning architectures for improving drug response prediction performance: myth or reality? Bioinformatics. Vol. 39(6)2023. Oxford University Press (OUP). [Cross Ref]

[32] Kapoor Sayash, Narayanan Arvind. Leakage and the reproducibility crisis in machine-learning-based science. Patterns. Vol. 4(9)2023. Elsevier BV. [Cross Ref]

[33] Issa Naiem T., Stathias Vasileios, Schürer Stephan, Dakshanamurthy Sivanesan. Machine and deep learning approaches for cancer drug repurposing. Seminars in Cancer Biology. Vol. 68:132–142. 2021. Elsevier BV. [Cross Ref]

[34] Adam George, Rampášek Ladislav, Safikhani Zhaleh, Smirnov Petr, Haibe-Kains Benjamin, Goldenberg Anna. Machine learning approaches to drug response prediction: challenges and recent progress. npj Precision Oncology. Vol. 4(1)2020. Springer Science and Business Media LLC. [Cross Ref]

[35] Cong Yi, Endo Toshinori. Multi-Omics and Artificial Intelligence-Guided Drug Repositioning: Prospects, Challenges, and Lessons Learned from COVID-19. OMICS: A Journal of Integrative Biology. Vol. 26(7):361–371. 2022. Mary Ann Liebert Inc. [Cross Ref]

[36] Sadegh Sepideh, Skelton James, Anastasi Elisa, Bernett Judith, Blumenthal David B., Galindez Gihanna, Salgado-Albarrán Marisol, Lazareva Olga, Flanagan Keith, Cockell Simon, Nogales Cristian, Casas Ana I., Schmidt Harald H. H. W., Baumbach Jan, Wipat Anil, Kacprowski Tim. Network medicine for disease module identification and drug repurposing with the NeDRex platform. Nature Communications. Vol. 12(1)2021. Springer Science and Business Media LLC. [Cross Ref]

[37] Le Tuan, Winter Robin, Noé Frank, Clevert Djork-Arné. Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures. Chemical Science. Vol. 11(38):10378–10389. 2020. Royal Society of Chemistry (RSC). [Cross Ref]

[38] Ghandi Mahmoud, Huang Franklin W., Jané-Valbuena Judit, Kryukov Gregory V., Lo Christopher C., McDonald E. Robert, Barretina Jordi, Gelfand Ellen T., Bielski Craig M., Li Haoxin, Hu Kevin, Andreev-Drakhlin Alexander Y., Kim Jaegil, Hess Julian M., Haas Brian J., Aguet François, Weir Barbara A., Rothberg Michael V., Paolella Brenton R., Lawrence Michael S., Akbani Rehan, Lu Yiling, Tiv Hong L., Gokhale Prafulla C., de Weck Antoine, Mansour Ali Amin, Oh Coyin, Shih Juliann, Hadi Kevin, Rosen Yanay, Bistline Jonathan, Venkatesan Kavitha, Reddy Anupama, Sonkin Dmitriy, Liu Manway, Lehar Joseph, Korn Joshua M., Porter Dale A., Jones Michael D., Golji Javad, Caponigro Giordano, Taylor Jordan E., Dunning Caitlin M., Creech Amanda L., Warren Allison C., McFarland James M., Zamanighomi Mahdi, Kauffmann Audrey, Stransky Nicolas, Imielinski Marcin, Maruvka Yosef E., Cherniack Andrew D., Tsherniak Aviad, Vazquez Francisca, Jaffe Jacob D., Lane Andrew A., Weinstock David M., Johannessen Cory M., Morrissey Michael P., Stegmeier Frank, Schlegel Robert, Hahn William C., Getz Gad, Mills Gordon B., Boehm Jesse S., Golub Todd R., Garraway Levi A., Sellers William R.. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. Vol. 569(7757):503–508. 2019. Springer Science and Business Media LLC. [Cross Ref]

[39] March-Vila Eric, Pinzi Luca, Sturm Noé, Tinivella Annachiara, Engkvist Ola, Chen Hongming, Rastelli Giulio. On the Integration of In Silico Drug Design Methods for Drug Repurposing. Frontiers in Pharmacology. Vol. 8:2017. Frontiers Media SA. [Cross Ref]

[40] You Wenjie, Yang Zijiang, Ji Guoli. PLS-based recursive feature elimination for high-dimensional small sample. Knowledge-Based Systems. Vol. 55:15–28. 2014. Elsevier BV. [Cross Ref]

[41] Gottlieb Assaf, Stein Gideon Y, Ruppin Eytan, Sharan Roded. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular Systems Biology. Vol. 7(1)2011. Springer Science and Business Media LLC. [Cross Ref]

[42] Wang Jianlin, Wang Wenxiu, Yan Chaokun, Luo Junwei, Zhang Ge. Predicting Drug-Disease Association Based on Ensemble Strategy. Frontiers in Genetics. Vol. 12:2021. Frontiers Media SA. [Cross Ref]

[43] Durant Joseph L., Leland Burton A., Henry Douglas R., Nourse James G.. Reoptimization of MDL Keys for Use in Drug Discovery. Journal of Chemical Information and Computer Sciences. Vol. 42(6):1273–1280. 2002. American Chemical Society (ACS). [Cross Ref]

[44] Ahangari Farida, Becker Christine, Foster Daniel G., Chioccioli Maurizio, Nelson Meghan, Beke Keriann, Wang Xing, Justet Aurelien, Adams Taylor, Readhead Benjamin, Meador Carly, Correll Kelly, Lili Loukia N., Roybal Helen M., Rose Kadi-Ann, Ding Shuizi, Barnthaler Thomas, Briones Natalie, DeIuliis Giuseppe, Schupp Jonas C., Li Qin, Omote Norihito, Aschner Yael, Sharma Lokesh, Kopf Katrina W., Magnusson Björn, Hicks Ryan, Backmark Anna, Dela Cruz Charles S., Rosas Ivan, Cousens Leslie P., Dudley Joel T., Kaminski Naftali, Downey Gregory P.. Saracatinib, a Selective Src Kinase Inhibitor, Blocks Fibrotic Responses in Preclinical Models of Pulmonary Fibrosis. American Journal of Respiratory and Critical Care Medicine. Vol. 206(12):1463–1479. 2022. American Thoracic Society. [Cross Ref]

[45] Shameer Khader, Glicksberg Benjamin S, Hodos Rachel, Johnson Kipp W, Badgeley Marcus A, Readhead Ben, Tomlinson Max S, O’Connor Timothy, Miotto Riccardo, Kidd Brian A, Chen Rong, Ma’ayan Avi, Dudley Joel T. Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. Briefings in Bioinformatics. Vol. 19(4):656–678. 2018. Oxford University Press (OUP). [Cross Ref]

[46] Peek C. J., Glasgow R. E., Stange K. C., Klesges L. M., Purcell E. P., Kessler R. S.. The 5 R's: An Emerging Bold Standard for Conducting Relevant Research in a Changing World. The Annals of Family Medicine. Vol. 12(5):447–455. 2014. Annals of Family Medicine. [Cross Ref]

[47] Zdrazil Barbara, Felix Eloy, Hunter Fiona, Manners Emma J, Blackshaw James, Corbett Sybilla, de Veij Marleen, Ioannidis Harris, Lopez David Mendez, Mosquera Juan F, Magarinos Maria Paula, Bosc Nicolas, Arcila Ricardo, Kizilören Tevfik, Gaulton Anna, Bento A Patrícia, Adasme Melissa F, Monecke Peter, Landrum Gregory A, Leach Andrew R. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Research. Vol. 52(D1)2024. Oxford University Press (OUP). [Cross Ref]

[48] Lamb Justin, Crawford Emily D., Peck David, Modell Joshua W., Blat Irene C., Wrobel Matthew J., Lerner Jim, Brunet Jean-Philippe, Subramanian Aravind, Ross Kenneth N., Reich Michael, Hieronymus Haley, Wei Guo, Armstrong Scott A., Haggarty Stephen J., Clemons Paul A., Wei Ru, Carr Steven A., Lander Eric S., Golub Todd R.. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science. Vol. 313(5795):1929–1935. 2006. American Association for the Advancement of Science (AAAS). [Cross Ref]

[49] Morgan Steve, Grootendorst Paul, Lexchin Joel, Cunningham Colleen, Greyson Devon. The cost of drug development: A systematic review. Health Policy. Vol. 100(1):4–17. 2011. Elsevier BV. [Cross Ref]

[50] Corsello Steven M, Bittker Joshua A, Liu Zihan, Gould Joshua, McCarren Patrick, Hirschman Jodi E, Johnston Stephen E, Vrcic Anita, Wong Bang, Khan Mariya, Asiedu Jacob, Narayan Rajiv, Mader Christopher C, Subramanian Aravind, Golub Todd R. The Drug Repurposing Hub: a next-generation drug library and information resource. Nature Medicine. Vol. 23(4):405–408. 2017. Springer Science and Business Media LLC. [Cross Ref]

[51] Shoemaker Robert H.. The NCI60 human tumour cell line anticancer drug screen. Nature Reviews Cancer. Vol. 6(10):813–823. 2006. Springer Science and Business Media LLC. [Cross Ref]

[52] Ochoa David, Hercules Andrew, Carmona Miguel, Suveges Daniel, Baker Jarrod, Malangone Cinzia, Lopez Irene, Miranda Alfredo, Cruz-Castillo Carlos, Fumis Luca, Bernal-Llinares Manuel, Tsukanov Kirill, Cornu Helena, Tsirigos Konstantinos, Razuvayevskaya Olesya, Buniello Annalisa, Schwartzentruber Jeremy, Karim Mohd, Ariano Bruno, Martinez Osorio Ricardo Esteban, Ferrer Javier, Ge Xiangyu, Machlitt-Northen Sandra, Gonzalez-Uriarte Asier, Saha Shyamasree, Tirunagari Santosh, Mehta Chintan, Roldán-Romero Juan María, Horswell Stuart, Young Sarah, Ghoussaini Maya, Hulcoop David G, Dunham Ian, McDonagh Ellen M. The next-generation Open Targets Platform: reimagined, redesigned, rebuilt. Nucleic Acids Research. Vol. 51(D1)2023. Oxford University Press (OUP). [Cross Ref]

[53] Blanco-González Alexandre, Cabezón Alfonso, Seco-González Alejandro, Conde-Torres Daniel, Antelo-Riveiro Paula, Piñeiro Ángel, Garcia-Fandino Rebeca. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals. Vol. 16(6)2023. MDPI AG. [Cross Ref]

[54] Park Kyungsoo. The use of real-world data in drug repurposing. Translational and Clinical Pharmacology. Vol. 29(3)2021. Korean Society for Clinical Pharmacology and Therapeutics. [Cross Ref]

[55] Ayuso-Muñoz Adrián, Prieto-Santamaría Lucía, Ugarte-Carro Esther, Serrano Emilio, Rodríguez-González Alejandro. Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data. Artificial Intelligence in Medicine. Vol. 145:2023. Elsevier BV. [Cross Ref]