The DOI matches its Crossref record; 59 of the 60 references it cites were found in a registry or an index.
From machine learning to deep learning: progress in machine intelligence for rational drug discovery
Abstract
Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“There are various tasks in the drug discovery process where machine learning can be used to streamline the tasks. This includes drug compound property and activity prediction, de novo design of drug compounds, drug–receptor interactions, and drug reaction prediction [16].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [16]
In the citing paper
“QSAR modeling methods, such as linear discriminant analysis (LDA), support vector machines (SVMs), random forest (RF), and decision trees, have been used to identify prospective drug candidates and have evolved into AI-based QSAR approaches can use that to speed up QSAR analysis”
From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Zhang 2017 & Jain 2015
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,798
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
Altman, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,657
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873
Bioisosterism: A Rational Approach in Drug Design
Patani, G. A., & LaVoie, E. J. (1996) · Chemical Reviews · doi:10.1021/cr950066q · Cited by 1,344
Big data deep learning: Challenges and perspectives
Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255
Flagged first, then by citations
1 of the 60 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Hinton, 2006, 8, 1527
- Jain, 2015, In silico de novo design of novel NNRTIs: a bio-molecular modelling approach, RSC Adv., 5, 14814, 10.1039/C4RA15478A · DOI
- Unterthiner, 2014, Deep learning as an opportunity in virtual screening, Proc. Deep Learning Workshop NIPS
Cited by
Sorted by citations
Items 701–758 of 758
Res‐ MulFra : Multilevel and Multiscale Framework for Brain Tumor Segmentation
Huang, D., Qiu, L., Liu, Z., Ding, Y., & Cao, M. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23135
Multi-task MSSP model can accurately predict selective Src inhibitorsPreprint
Tian, X., Han, L., Su, Y., Sun, H., Zhang, S., Chen, C., Chen, C., Li, C., Lv, X., & Li, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4475200/v1
Prediction of National Innovation Using Data Scientific Approach
Chung, D., Lee, C. G., & Seo, S. (2024) · International Journal of Innovation and Technology Management · doi:10.1142/s021987702450055x
Introduction to predictive analytics and MATLAB®
Kumar, L. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-29888-2.00001-5
AI and deep learning in cancer genomics
Awasthi, P., Singh, N. K., Gupta, A., Anand, N., & Hasan, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-27574-6.00017-5
Li, X., Peng, T., Zhang, L., Xiao, H., & Liu, Y. (2026) · International Journal of Biomedical Engineering and Technology · doi:10.1504/ijbet.2026.153231
Self-citationThe Influence of Artificial Intelligence in Drug Discovery and Development
Krishnan, S., Rajendran, K., Yogeshwaran, V. N., & Radhakrishnan, B. (2024) · Medical virology · doi:10.1007/978-981-97-2938-8_3
Computational Theories Shaping the Future of Drug Discovery
Venkatesh, P. H., Uttarkar, A., & Niranjan, V. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-5598-5.ch006
Machine Learning and Neural Network Methods Applied to Drug Discovery
de Sousa, D. S., da Silva, A. P., de Angelo, R. M., Chiari, L. P. A., Honório, K. M., & da Silva, A. B. F. (2024) · Computer-aided drug discovery and design · doi:10.1007/978-3-031-76718-0_4
Ghosh, D., Chakraborty, S., Pal, D., Tarafdar, P., Pal, R., Maity, S., Jaiswal, U., Singh, D., & Mallick, A. (2024) · International Conference on Big Data Analytics in Bioinformatics (DABCon) · doi:10.1109/dabcon63472.2024.10919313
Arun, N., & Ravichandran, K. (2024) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa64454.2024.00051
Framework for Drug User Analysis and Prediction
Sethuraman, D., Ananya, E., Joseph Jeffrey, J., Thangavel, S. K., Rangasami, P., & Somasundaram, K. (2025) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl65848.2025.10933071
Multitask Learning Framework for Screening of Corrosion Inhibitors for Mild Steel
Agarwal, A., Rathore, P., Kumar, D., Nistala, S. H., Sanjana, S., Jain, V. K., & Rai, B. (2022) · IEEE Lasers and Electro-Optics Society Annual Meeting (LEOS) · doi:10.5006/c2022-17757
Aitouhanni, I., Berqia, A., Vibhute, A. D., Mouniane, Y., & Manohar, B. M. (2025) · Advances in environmental engineering and green technologies book series · doi:10.4018/979-8-3693-8532-6.ch018
The Impact of Technological Progress on Pharmacovigilance
Yang, J., & Li, F. (2025) · Pharmaceutical science. · doi:10.5772/intechopen.1006906
Schreier, O., Yazdani, A., Galdadas, I., Kabak, R., Gervasio, F. L., Mu, G., & Teodoro, D. (2025) · doi:10.2196/preprints.77732
Khriss, A., Badaoui, M., Boutayeb, W., Ali, H. A., & Elmiad, A. K. (2025) · Industrial and applied mathematics · doi:10.1007/978-981-96-1925-2_9
Schreier, O., Yazdani, A., Galdadas, I., Kabak, R., Gervasio, F. L., Mu, G., & Teodoro, D. (2025) · medRxiv · doi:10.1101/2025.07.14.25331486
Secure Cloud Infrastructures for Deploying AI-Powered Drug Discovery Pipelines
Taneja, A. (2022) · International Journal of Emerging Research in Engineering and Technology · doi:10.63282/3050-922x.ijeret-v3i4p106
Zhang, J., Xu, B., Kang, Y., Liu, J., Cao, J., & Chen, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5380273
Zhang, J., Xu, B., Kang, Y., Liu, J., Cao, J., & Chen, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5380288
273 Explainable AI approaches in drug classification from biomarkers of epileptic seizure
Ghosh, D., Singh, D., & Neogi, B. (2025) · De Gruyter eBooks · doi:10.1515/9783111504667-003
Emerging Trends in Genomics: Big Data and Deep Learning Technique
Chaturvedi, S., Agrawal, A., & Gupta, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-4151-2_4
Data Science and AI for Transforming R&D
Sood, P., Kaur, G., Kumar, J., Kaur, N., Jangra, N., & Gautam, R. K. (2025) · Wiley eBooks · doi:10.1002/9781394287383.ch3
Applications of AI/ML in Drug Repurposing for Antiviral Therapy
Sen, C., Paul, P., Tiwari, A., Ojha, D., Hazra, A., Zaman, N., Khan, K. A., Shit, P. K., & Samanta, A. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898811143125010008
Khouzani, L. J., Sardari, S., Khouzani, S. J., Pérez‐Sánchez, H., & Ghasemi, F. (2025) · PLoS ONE · doi:10.1371/journal.pone.0333553
Jiang, D., Wu, Z., HSIEH, C., Chen, G., Liao, B., Wang, Z., Shen, C., Cao, D., Wu, J., & Hou, T. (2021) · Figshare · doi:10.6084/m9.figshare.14053380
Margulis, E., Slavutsky, Y., Lang, T., Behrens, M., Benjamini, Y., & Niv, M. Y. (2022) · Figshare · doi:10.6084/m9.figshare.20267838
Tinivella, A., Pinzi, L., & Rastelli, G. (2021) · Figshare · doi:10.6084/m9.figshare.14175727
Yin, M., Yin, S., Yang, Z., Liu, H., Jin, X., Wu, Z., Wang, L., & Jin, Z. (2025) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2025.102043
29Chapter 2 Machine Learning and Computation-Enabled Smart Systems
Shrivastava, P., & Shrivastava, A. (2025) · De Gruyter eBooks · doi:10.1515/9783112219430-002
Artificial Intelligence Enabled IoT in Healthcare Ecosystem
Manisha, Bhardwaj, J., Kuhar, S., Sharma, T. P., & Chaudhary, R. (2025) · International Conference on Computational Intelligence and Computing Applications (ICCICA) · doi:10.1109/iccica67008.2025.11337842
Imaging of the fish embryo model and applications to toxicology
Genest, D. (2019) · theses.fr (ABES) · doi:10.70675/aaaff214zcdb9z4820zbe38z5ecda94a48fa · Cited by 1
Toofanee, M. S. A. (2023) · theses.fr (ABES) · doi:10.70675/600337cazbfe4z405fz9538za725b88272b7
Artificial intelligence and antiaging research
Arumugam, S., Muthuvel, R., Antony, B. S., Chandrasekaran, K., & Subbaraj, G. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33159-6.00009-6
Machine Learning for Energy Prediction in Wireless Sensor Networks (WSN)
Gupta, S., Girija, R., Kathpal, N., Kumari, S., & Singh, V. (2026) · doi:10.1002/9781394314751.ch18
Pathak, M., & Sandhu, A. S. (2026) · Current Physical Chemistry · doi:10.2174/0118779468402854251126094245
Data driven based investigation of polyethylene glycol heat capacity
Al-Nabulsi, J., Alsalami, Z. A., Deepak, J., Johar, M., Routray, A., Karthikeyan, A., Gill, H. S., Bisht, Y. S., & Abumalek, A. (2026) · Chemical Papers · doi:10.1007/s11696-026-04976-2
AI Regulations in Medical Devices
Khinvasara, T. (2026) · Springer handbooks · doi:10.1007/978-3-032-01636-2_40
Nezhad, S. K., Solout, M. V., Meknatkhah, S., Amini, F., & Ghasemi, J. B. (2026) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2026.141153
Dong, Y., Xu, M., & Song, H. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133301
Prediction of DNA N4-Methylcytosine Sites Based on a Position-Aligned Multi-branch Fusion Network
Wang, H., Li, J., Ruan, Y., & Feng, H. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0147
Tailored injectable nanocomposite hydrogels for high-performance biomedical applications
Rathee, G., Singh, N. K., Kohli, S., Deshmukh, K., Rathee, J., Puertas‐Segura, A., Solanki, P. R., & Ajeet, K. (2026) · Biomaterials Science · doi:10.1039/d6bm00535g
Hashmi, A. H., Thankamony, R., Hammadi, H. A. A., Thiemann, T., & Siddiqui, A. (2026) · Frontiers in Marine Science · doi:10.3389/fmars.2026.1856396
Machine Learning (ML) and Deep Learning (DL) Approaches in Multi-target Drug Design
de Menezes, R. P. B., da Silva, P. R., Nobre, T. S., Pontes, A. H. D. O., de Morais, L. F. M., Scotti, L., & Scotti, M. T. (2026) · Royal Society of Chemistry eBooks · doi:10.1039/9781837070206-00519
Aktaş, E., & Ventura, S. (2026) · Intelligent Pharmacy · doi:10.1016/j.ipha.2026.07.001
AI-Driven Innovation in Healthcare Applications
Sahoo, T., Mandal, S., Roy, P., & Podder, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-23511-4_9
AI-driven drug design: a comprehensive review
Dong, Z., Shou, B., Chen, J., Fang, H., Wu, S., Zhang, Y., Wang, C., Sun, G., & Wan, Y. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00923-1
Putri, D. A., & Batubara, A. S. (2026) · ETDC Indonesian Journal of Research and Educational Review · doi:10.51574/ijrer.v6i1.5306
Quantum computing in drug discovery
Gadbail, H. N., Rewatkar, R. M., Jumde, N. R., Manker, M., Nagre, P., & Zade, S. (2026) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2026.1815176
Artificial intelligence enabled virtual screening
Kumar, S., & Gupta, M. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40338-5.00010-x
Perceptions of Iraqi pharmacy lecturers toward the use of artificial intelligence in drug discovery
Sobh, A., Al-Khayyat, N. S., Mohammed, A. T., Mahmood, S. S., Muafah, M. Y., Seif, M. T., Mahdi, W. S., Yaqoob, H. A., Abdulkareem, H. R., Mohammed, M. F., et al. (2026) · International Journal of Clinical Medical Research · doi:10.61466/ijcmr4040003
You, Y., Liu, Y., & Zhao, R. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103582
Jachak, S., Vispute, P., Sonar, S., Bhalerao, R., Rode, M., Shaikh, M., & Suryawanshi, M. (2026) · Next research. · doi:10.1016/j.nexres.2026.102569
Revolutionizing Pharmaceuticals With AI-Driven Drug Delivery, Design, and Development
Kesavardhini, K., Saranraj, P., Lokeshwari, B., Gayathri, K. V., & Arthi, K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8562-4.ch008
BindingDB (2026) · UC San Diego · doi:10.7270/q2q52vj4
Metadata
- Authors
- Zhang, L., Tan, J., Han, D., & Zhu, H.
- Affiliations
- Beijing University of Technology (CN)
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Tan, Jianjun
- Zhu, Hao
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 22
- Issue
- 11
- Pages
- 1680-1685
- ISSN
- 1359-6446 (ISSN-L)
- OpenAlex
- W2753588101
- Cited by
- 760
- References
- 60
- Citations by year
- 2026: 42 · 2025: 90 · 2024: 105 · 2023: 134 · 2022: 112 · 2021: 96 · 2020: 79 · 2019: 61 · 2018: 39 · 2017: 2
- FWCI
- 4.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Drug Discovery Today (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Chinese Natural Science Foundation Project (21173014)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- rational drug discovery, deep learning, machine learning, QSAR, big data, history of machine learning, computer-aided drug design
- MeSH terms
- Machine Learning, Artificial Intelligence, Humans, Models, Theoretical, Drug Design, Quantitative Structure-Activity Relationship, Drug Discovery
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
