From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Deep learning

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

Statistical Learning Theory

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

Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence

Niederreiter, J. (2021) · Italian Economic Journal · doi:10.1007/s40797-021-00171-2 · Cited by 1

Comparison of Feature Extraction Methods for Heart Disease Classification

Fares, A., & Mašetić, Z. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-90055-7_17 · Cited by 1

Deep Neural Networks Predict Inhibitors ofSchistosoma MansoniThioredoxin Glutathione Reductase (SmTGR)

Kwofie, S. K., Agyenkwa-Mawuli, K., Adams, J., Anteh, P., Agyapong, O., & Wilson, M. D. (2021) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416521410040 · Cited by 1

Structure of a Machine Brain

Wang, W., Chen, X., & Yao, T. (2022) · Research on intelligent manufacturing · doi:10.1007/978-981-19-0272-7_1 · Cited by 1

Deep learning in pharmacology: opportunities and threats

Kocić, I., Kocić, M., Rusiecka, I., Kocić, A., & Kocić, E. (2022) · European Journal of Translational and Clinical Medicine · doi:10.31373/ejtcm/149217 · Cited by 1

Big data and deep learning: extracting and revising chemical knowledge from data

Gini, G. C., Hung, C., & Benfenati, E. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85713-0.00030-x · Cited by 1

Drug Discovery Analysis Using Machine Learning Bioinformatics

Prabha, S., Sasikumar, S., Surendra, S., Chennakeshava, P., & Reddy, Y. S. M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7615-5_36 · Cited by 1

A QSAR study to predict the survival motor neuron promoter activity of candidate diaminoquinazoline derivatives for the potential treatment of spinal muscular atrophy

Gurses, G., Erdem, S. S., & Saçan, M. T. (2023) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2023.2200975 · Cited by 1

The impact of artificial intelligence methods on drug design

Gini, G. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-18638-7.00009-8 · Cited by 1

Preparing Pharmacists for The Era of AI-driven Pharmacological Sciences: An Opinion Article

Hisan, U. K., Fauzia, R. D., & Amri, M. M. (2023) · Journal of Pedagogy and Education Science · doi:10.56741/jpes.v2i02.347 · Cited by 1

Techniques for Behaviour Analysis Using Deep Learning

Chhabra, G., Kumar, S., Gupta, S. K., & Nagpal, P. (2023) · Springer eBooks · doi:10.1007/978-981-99-3039-5_4 · Cited by 1

Ligand-Based Drug Discovery on SARSCOV2 Target Protein

Mishra, P., Vipsita, S., & Sahoo, T. K. (2023) · IEEE India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon58499.2023.10270567 · Cited by 1

Molecular Docking on SARSCOV2 Target Protein

Mishra, P., Vipsita, S., & Sahoo, T. K. (2023) · International Conference on Ambient Intelligence in Health Care (ICAIHC) · doi:10.1109/icaihc59020.2023.10431457 · Cited by 1

Multi-target Compounds against Trypanosomatid Parasites and Mycobacterium tuberculosis

Gomes, M. C. D. O., Padilha, E. K. A., Diniz, G. R. A., Gomes, E. C., Santos-Júnior, P. F. D. S., Zhan, P., & da Silva‐Júnior, E. F. (2024) · Current Drug Targets · doi:10.2174/0113894501306843240606114854 · Cited by 2

Artificial Intelligence in Cancer: A SWOT Analysis

Torkay, G., Fadlallah, N., Karagöz, A., Canlı, M., Saydam, E., Mete, A., Kızılışık, F., Darıcı, H., & YEŞİL, Y. (2024) · Journal of AI · doi:10.61969/jai.1469589 · Cited by 2

Innovative Healthcare Advancements: Harnessing Artificial and Human Intelligence for Bionic Solutions

Sapkal, S., Jadhav, S., Mallikarjun, P., Shamim, R., Islam, A. U., & Bamane, K. D. (2024) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon60325.2024.10687526 · Cited by 1

Preselection of Compounds for Lead Identification in Virtual Screening Campaigns

Castello, F. A. (2024) · Computer-aided drug discovery and design · doi:10.1007/978-3-031-69162-1_4 · Cited by 1

A Study on Drug Addiction Prediction in Bangladeshi Universities Using Advanced Machine Learning Algorithms

Ray, B., Ayman, U., Akash, M. A. A. K., Khan, N., Chakraborty, N. R., & Bijoy, M. H. I. (2024) · IEEE International Conference on Computing, Applications and Systems (COMPAS) · doi:10.1109/compas60761.2024.10796447 · Cited by 1

Early Diagnosis of Invasive Ductal Carcinoma Breast Cancer using Deep Learning Framework

Güler, H., Santur, Y., & Ulaş, M. (2025) · Turkish Journal of Science and Technology · doi:10.55525/tjst.1483617 · Cited by 1

Screening of oxidative stress components of cigarette smoke based on machine learning model integration

Hong, Y., Lv, J., Hong, Y., Wang, J., Huang, X., & Chen, C. (2025) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2025.117387 · Cited by 1

A Novel Customized Big Data Analytics Framework for Drug Discovery

Fathima, A. J., & Murugaboopathi, G. (2018) · Journal of Cyber Security and Mobility · doi:10.13052/2245-1439.7111 · Cited by 1

Big Data and Machine Learning Analytics to Detect Epileptic Seizures with Minimum Delay Using Random Window Optimization

Sanila, S., & Sathyalakshmi, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5788-0_22 · Cited by 1

In Silico Modeling of Anticancer Drugs: Recent Advances

Sharma, S., & Bhatia, V. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-71975-3_10 · Cited by 1

Harnessing the Capability of CADD Methods in the Prediction of Anti-COVID Drug Likeliness

Chaturvedi, S., Chaudhary, V., Klauss, T., Barthélémy, P., & Mishra, A. K. (2021) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/978-1-7998-0307-2.ch011 · Cited by 1

A Binary Classification Model for Toxicity Prediction in Drug Design

Varela-Salinas, G., Cruz, H. E. C., Juárez-Saldívar, A., Martinez-Rodriguez, J. L., Rodriguez-Rodriguez, J., & García-Pérez, C. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86271-8_13 · Cited by 1

Artificial intelligence methods to repurpose and discover new drugs to fight the Coronavirus disease-2019 pandemic

Santana, M. V., & Silva, F. P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91172-6.00016-9 · Cited by 1

Addressing Entrepreneurship’s False Dichotomization of Risk and Uncertainty through Modal Logics

Townsend, D. M., & Hunt, R. A. (2019) · Academy of Management Proceedings · doi:10.5465/ambpp.2019.18448abstract · Cited by 1

Bioactivity assessment of natural compounds using machine learning models based on drug target similarityPreprint

Periwal, V., Bassler, S. O., Andrejev, S., Gabrielli, N., Patil, K. R., Typas, A., & Patil, K. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.06.371112 · Cited by 1

MiFL: Multi-Input Neural Networks in Federated LearningPreprint

Casella, B., Riviera, W., Aldinucci, M., & Menegaz, G. (2023) · doi:10.36227/techrxiv.22492732.v1 · Cited by 1

Rapid Diagnosis of Diabetic Kidney Disease Based on Serum Raman Spectroscopy and Deep LearningPreprint

Cui, X., Zhang, X., Peng, C., Xu, L., & Lü, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4679091 · Cited by 1

Using Artificial Intelligence forde novoDrug Design and Retrosynthesis

Arora, R. R., Brosse, N., Descamps, C., Devaux, N., Huu, N. D., Gendreau, P., Gaston‐Mathé, Y., Parrot, M., Perron, Q., & Tajmouati, H. (2024) · Wiley eBooks · doi:10.1002/9783527840748.ch12 · Cited by 2

Molecular Modeling in Drug Design

Wade, R. C., & Salo‐Ahen, O. M. H. (2019) · MDPI eBooks · doi:10.3390/books978-3-03897-615-8

Reinforced seq2seq adversarial autoencoder for de novo molecular design

Putin, E. (2018) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2018-18-6-1084-1090

The Big Data Research Ecosystem

Strydom, M. J., & Buckley, S. B. (2018) · Advances in knowledge acquisition, transfer, and management book series/Advances in knowledge acquisition, transfer and management book series · doi:10.4018/978-1-5225-7077-6.ch003

Artificial Intelligence Based Wrist Fracture Classification

Thomas, D. (2019) · doi:10.31390/gradschool_theses.4991

Futuristic Methods for Treatment of HIV in the Nervous System

Navis, A., & Robinson‐Papp, J. (2019) · Springer eBooks · doi:10.1007/978-3-030-29022-1_18

A Machine Learning alternative to placebo-controlled clinical trials upon new diseases: A primerPreprint

Álvarez, E., Lamagna, F., & Szewc, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12454

Integrated Machine Learning and Bioinformatics Approaches for Prediction of Cancer-Driving Gene Mutations

Odeyemi, O. (2020) · Chapman University Digital Commons (Chapman University) · doi:10.36837/chapman.000155

Computational compound screening of biomolecules and soft materials by molecular simulations

Bereau, T. (2020) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/1361-651x/abd042 · Cited by 29

Time and Frequency Domain Features Extraction Comparison for Motor Imagery Detection

Orjuela-Cañón, Á. D., & Archila, J. S. R. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-69774-7_6

Activities Prediction of Drug Molecules by Using Automated Model Building with Descriptor Selection

Liu, Y., Tian, W., & Zhang, H. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-1160-5_7

Cheminformatic Approaches to Hit-Prioritization and Target Prediction of Potential Anti-MRSA Natural Products

Oselusi, S. O. (2020) · University of the Western Cape Electronic Theses and Dissertations Repository (University of the Western Cape)

Ready or Not: Exploring Machine Learning Readiness in the Supplier Selection Process

Mukhtar, A., & Rashid, N. (2021) · Lund University Publications Student Papers (Lund University)

DeepGREP: A deep convolutional neural network for predicting gene-regulating effects of small molecules

Bardak, B., & Tan, M. (2021) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb49929.2021.9562920

Decision Support System in Determining the Level of Customer Satisfaction in the SME Corner at the Department of Cooperatives, Small and Medium Enterprises, Deli Serdang Regency with the Analytical Hierarchy Process (AHP) Method

Simanjuntak, L. S. (2021) · International Journal of Basic and Applied Science · doi:10.35335/ijobas.v9i4.22

Biomarker Gene Identification for Breast Cancer ClassificationPreprint

Rajpal, S., Rajpal, A., Agarwal, M., & Kumar, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05546

Implementation of Digital Micro Fluidic Biochip with Machine Learning for Drug Development

Obulesu, A. (2021) · Bioscience Biotechnology Research Communications · doi:10.21786/bbrc/14.7.95

AIDrugApp: Artificial Intelligence-based Web-App for Virtual Screening of Inhibitors against SARS-COV-2Preprint

Karade, D., & Karade, V. (2020) · ChemRxiv · doi:10.26434/chemrxiv.13312661

The Role of Big Data Analytics in Drug Discovery and Vaccine Development Against COVID-19

(2022) · Advances in data mining and database management book series · doi:10.4018/978-1-7998-8793-5.ch009

The Big Data Research Ecosystem

Strydom, M. J., & Buckley, S. B. (2020) · IGI Global eBooks · doi:10.4018/978-1-7998-7705-9.ch090

Basic Scientific and Clinical Applications

Chetty, M. S. R., & Babu, C. V. K. (2022) · Wiley eBooks · doi:10.1002/9781119792376.ch11

Application of Artificial Neural Networks for Predicting Imidazole Derivatives Antimicrobial Activity Against Enterococcus FaecalisPreprint

Badura, A., Krysiński, J., Nowaczyk, A., & Buciński, A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4166477

Src kinase app: valid inhibitor generation and prediction with explanation using predictive model and selfiesPreprint

Abdelalim, M. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-090pf

Application of artificial neural networks for predicting imidazole derivatives antimicrobial activity against Enterococcus faecalisPreprint

Badura, A., Krysiński, J., Nowaczyk, A., Poćwiardowska-Głąb, M., & Buciński, A. (2022) · Research Square · doi:10.21203/rs.3.rs-2308866/v1

Intelligent Drug Design and Use for Cancer Treatment: The Roles of AI and Precision Oncology in Targeting Patient-Specific Splicing Profiles

Hull, R., Manabile, M. A., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_11

Aplicação de técnicas de inteligência artificial sobre características físico-estatísticas para classificar lesões de miocardite chagásica crônica

Tetzner, V. M. (2022) · doi:10.11606/d.59.2022.tde-27012023-111234

MiFL: Multi-Input Neural Networks in Federated LearningPreprint

Casella, B., Riviera, W., Aldinucci, M., & Menegaz, G. (2023) · doi:10.36227/techrxiv.22492732

Machine Learning and Deep Learning applications-a vision using the SPSS Method

Bhagwandas, K. K. (2020) · doi:10.46632/rmc/1/3/3 · Cited by 2

Geometric and topological AI for molecular sciences

Wee, J. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/165903

Interpretable predictions with Convolutional Neural Networks for complex data

Lancia, G. (2023) · Utrecht University Repository (Utrecht University) · doi:10.33540/1770

Machine learning vs. deep learning: Features and functions

Panah, E., Som, H. M., & Mukhtar, S. B. (2023) · AIP conference proceedings · doi:10.1063/5.0133398

Classification of Stroke Using Machine Learning Techniques: Review Study

Sawan, A., Awad, M., & Qasrawi, R. (2023) · International Conference on Control, Automation and Diagnosis (ICCAD) · doi:10.1109/iccad57653.2023.10152317

Peer Review #1 of "A fine-tuned YOLOv5 deep learning approach for real-time house number detection (v0.2)"

(2023) · PeerJ Computer Science · doi:10.7287/peerj-cs.1453v0.2/reviews/1

Peer Review #1 of "A fine-tuned YOLOv5 deep learning approach for real-time house number detection (v0.1)"

(2023) · PeerJ Computer Science · doi:10.7287/peerj-cs.1453v0.1/reviews/1

Peer Review #2 of "A fine-tuned YOLOv5 deep learning approach for real-time house number detection (v0.2)"

(2023) · PeerJ Computer Science · doi:10.7287/peerj-cs.1453v0.2/reviews/2

Peer Review #3 of "A fine-tuned YOLOv5 deep learning approach for real-time house number detection (v0.1)"

(2023) · PeerJ Computer Science · doi:10.7287/peerj-cs.1453v0.1/reviews/3

Peer Review #2 of "A fine-tuned YOLOv5 deep learning approach for real-time house number detection (v0.1)"

(2023) · PeerJ Computer Science · doi:10.7287/peerj-cs.1453v0.1/reviews/2

Sentiment prediction based on neural network

Kuangyu, C. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/5/20230583

Pharmacotherapy Safety 360°: NOLI NOCERE!

Sychev, D. A. (2023) · Pharmacogenetics and Pharmacogenomics · doi:10.37489/2588-0527-2023-1-3-5

Molecular docking studies on the interaction of phytocompounds exploring the antifungal potency against Candida spp.

George, S. A., & Bhadran, S. (2023) · doi:10.22192/ttdls.2023.007

Value Added Products from Mushroom – Need for the Hour

Pallavi, E., & Sheeba, E. (2023) · doi:10.22192/ttdls.2023.001

Role of diet and Nutrition in Enhancing sporting performance

Vijayanand, D. S. (2023) · doi:10.22192/ttdls.2023.008

Phytochemicals and their Functional Aspects

Ramojwar, J. (2023) · doi:10.22192/ttdls.2023.005

Nano bio-pesticides in sustainable agriculture

Deepa, V. H., & Prakash, N., V (2023) · doi:10.22192/ttdls.2023.006

A Molecular Approach of Signaling Molecules, Its Receptors and Their Responses

Dr. Malathi, R. (2023) · doi:10.22192/ttdls.2023.002

Bioplastics – A boon or curse

Deepa, V. H. (2023) · doi:10.22192/ttdls.2023.003

Medicinal potential of Fabaceae family members

Lama, S. (2023) · doi:10.22192/ttdls.2023.014

Application of Artificial Intelligence in Biochemistry

Inbavalli., K. (2023) · doi:10.22192/ttdls.2023.015

Detection of heavy metals in plants and its impact on environment

Babu, B. (2023) · doi:10.22192/ttdls.2023.012

Enhancing the Sustainability and Performance of Bio-Concrete: A Comprehensive Review of Microbial- Based Construction Materials

Ekanayake, A. K., Ekanayake, A. K., & Gupta, N. (2023) · doi:10.22192/ttdls.2023.009

Meeting the Unmet Challenges of Pharmaceutical Research Using Artificial Intelligence

Mehrotra, S., & Pathak, K. (2023) · Springer eBooks · doi:10.1007/978-981-99-2302-1_4

On Impact of Data Models on Predictability Assessment of Time Series

Frenkel, S. L. (2023) · Springer optimization and its applications · doi:10.1007/978-3-031-31654-8_7

An Introduction to Machine Learning in Molecular Sciences

Liu, H., & Qu, C. (2023) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-031-37196-7_1

Deep Learning and Latent Variables in Nonuniform Antenna Array Processing for Direction of Arrival

Gómez-Talal, I., Bote-Curiel, L., Rojo‐Álvarez, J. L., Christodoulou, C., & Martínez‐Ramón, M. (2023) · URSI General Assembly and Scientific Symposium · doi:10.23919/ursigass57860.2023.10265467

AI Logic of Care: premises for upgrading the smart bandages for diabetic chronic wounds

Zapata, I., Heinzel, T., & Bernabei, R. (2023) · International Association of Societies of Design Research Congress (IASDR) · doi:10.21606/iasdr.2023.283 · Cited by 2

THE ERA OF ARTIFICIAL INTELLIGENCE AND BIG DATA: A PATHWAY TO PHARMA 4.0

(2023) · International Journal of Biology Pharmacy and Allied Sciences · doi:10.31032/ijbpas/2023/12.10.1018

Artificial intelligence: Evolving role in drug design

Dahiya, A., Sharm, R., Arora, S., Bora, K. S., & Sharma, R. B. (2023) · AIP conference proceedings · doi:10.1063/5.0123091

The influence of artificial intelligence technologies in healthcare systems and rise in their applications

Sharma, A., & Pandey, M. (2023) · IP International Journal of Forensic Medicine and Toxicological Sciences · doi:10.18231/j.ijfmts.2023.020 · Cited by 1

A COMPREHENSIVE REVIEW: EXPLORING ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY AND DEVELOPMENT

(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets48342

Supervised machine learning algorithms used to predict post-surgical outcomes following anterior surgical fixation of odontoid fractures

Kricfalusi, M., Rice, R. C., Cabrera, A., Dasari, P., Chung, D., Case, T. R., Ruckle, D., Liu, J. N., Cheng, W. K., & Danisa, O. (2024) · International Journal of Research in Orthopaedics · doi:10.18203/issn.2455-4510.intjresorthop20240402

Application of machine learning constructs to predict post-operative complications and adverse events following shoulder hemiarthroplasty

Dasari, P., Rice, R. C., Cabrera, A., Kricfalusi, M., Case, T. R., Gulbrandsen, M. T., Brown, J., & Phipatanakul, W. P. (2024) · International Journal of Research in Orthopaedics · doi:10.18203/issn.2455-4510.intjresorthop20240401

Revolutionizing machine learning: A comprehensive analysis of ASIC hardware accelerators and their applications

Lei, W. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/51/20241208

Virtual Tools and Screening Designs for Drug Discovery and New Drug Development

Dubey, S. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815223019124010007

Additional file 2 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

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.14053377

Additional file 1 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

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.14053374

Role of Artificial Intelligence in Pharmacy

Satkar, M. S. S., Jadhav, M. P. A., & Randhe, M. T. A. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-18075

AI and Big Data for the management of COVID-19

Kayaly, D. E., Elhady, S., Hazem, N., Ismail, T., & Fahim, I. S. (2023) · International Conference on Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition (ICRSEtoSET) · doi:10.1109/icrsetoset56772.2023.10525299

Application of digital technologies in neurology.

Витальевна, Б. Е., Moscow, R., Fedotova, N. A., Zimnyakova, O. S., & Shaderkina, A. I. (2023) · Russian Journal of Telemedicine and E-Health · doi:10.29188/2712-9217-2023-9-4-14-22

The Role of Machine Learning in Software Development

Tawar, D. B. S. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24may2519

Subject Detection of Algerian Posts for Opinion Analysis

Bousmaha, K. Z., Hamadouche, K., Cheurfaoui, N., & Belguith, L. H. (2024) · Ingénierie des systèmes d information · doi:10.18280/isi.290303

Artificial Intelligence Approaches for Rational Drug Design and Discovery

Duch, W., Swaminathan, K., & Meller, J. (2007) · Current Pharmaceutical Design · doi:10.2174/138161207780765954 · Cited by 249

A Systematic Literature Review of the Current Status and Future Prospects of Machine Learning Methods and Techniques Applied to Novel Drug Discovery

Abdelkrim, A., Bouramoul, A., & Zenbout, I. (2022) · International Journal of Organizational and Collective Intelligence · doi:10.4018/ijoci.312223 · Cited by 3

Aprendizado de Máquina Aplicado à Descoberta de Fármacos

Machado, K., Werhli, A. V., Kremer, F. S., & Borges, R. J. (2026) · doi:10.5753/sbc.19670.4.6

Drug Discovery Using Machine Learning and Data Analysis

Pali, P., Jain, A., Kyadarkunte, P., Patil, S., Jais, S., Nasare, R. K., & Chhabria, S. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.47504 · Cited by 3

New-Generation Drug Discovery using Machine Learning

Gupta, N., & Khobragade, P. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.52112