Artificial intelligence in drug discovery and development

How artificial intelligence will change the future of marketing

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00696-0 · Cited by 2,875 · see citing works

Clinical development success rates for investigational drugs

Hay, M., Thomas, D. W., Craighead, J. L., Economides, C., & Rosenthal, J. (2014) · Nature Biotechnology · doi:10.1038/nbt.2786 · Cited by 2,536 · see citing works

Planning chemical syntheses with deep neural networks and symbolic AI

Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847 · see citing works

ARTIFICIAL INTELLIGENCE FOR THE REAL WORLD

Davenport, T. H., & Ronanki, R. (2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets42512 · Cited by 1,829 · see citing works

DeepDTA: deep drug–target binding affinity prediction

Öztürk, H., Özgür, A., & Özkırımlı, E. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty593 · Cited by 1,728 · see citing works

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537 · see citing works

Deep reinforcement learning for de novo drug design

Popova, M., Isayev, O., & Tropsha, A. (2018) · Science Advances · doi:10.1126/sciadv.aap7885 · Cited by 1,379 · see citing works

Artificial Intelligence and the Public Sector—Applications and Challenges

Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019) · International Journal of Public Administration · doi:10.1080/01900692.2018.1498103 · Cited by 1,341 · see citing works

DeepTox: toxicity prediction using deep learning

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081 · see citing works

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works

Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review

Fogel, D. B. (2018) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2018.08.001 · Cited by 1,035 · 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

Artificial intelligence in medicine

Ramesh, A., Kambhampati, C., Monson, J. Q., & Drew, P. J. (2004) · Annals of The Royal College of Surgeons of England · doi:10.1308/147870804290 · Cited by 1,008 · see citing works

Large-scale prediction and testing of drug activity on side-effect targets

Lounkine, E., Keiser, M. J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J. L., Lavan, P., Weber, E., Doak, A. K., Côté, S., et al. (2012) · Nature · doi:10.1038/nature11159 · Cited by 880 · see citing works

Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice

Syam, N., & Sharma, A. (2018) · Industrial Marketing Management · doi:10.1016/j.indmarman.2017.12.019 · Cited by 852 · see citing works

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

Zhang, L., Tan, J., Han, D., & Zhu, H. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.08.010 · Cited by 760 · see citing works

Recurrent Neural Networks

(1999) · CRC Press eBooks · doi:10.1201/9781420049176 · Cited by 717 · see citing works

Computer-Assisted Design of Complex Organic Syntheses

Corey, E. J., & Wipke, W. T. (1969) · Science · doi:10.1126/science.166.3902.178 · Cited by 688 · see citing works

Artificial intelligence for clinical trial design

Harrer, S., Shah, P., Antony, B., & Hu, J. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.05.005 · Cited by 672 · see citing works

Advancing drug discovery via artificial intelligence

Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.06.004 · Cited by 654 · see citing works

Organic synthesis in a modular robotic system driven by a chemical programming language

Steiner, S., Wolf, J. B., Glatzel, S., Andreou, A., Granda, J. M., Keenan, G., Hinkley, T., Aragón-Camarasa, G., Kitson, P. J., Angelone, D., et al. (2018) · Science · doi:10.1126/science.aav2211 · Cited by 630 · see citing works

Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules

Lusci, A., Pollastri, G., & Baldi, P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400187y · Cited by 557 · see citing works

How artificial intelligence is changing drug discovery

Fleming, N. (2018) · Nature · doi:10.1038/d41586-018-05267-x · Cited by 546 · cited 1× · see citing works

DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks

Karimi, M., Wu, D. J., Wang, Z., & Shen, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz111 · Cited by 536 · see citing works

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

(2018) · doi:10.24963/ijcai.2018 · Cited by 526 · see citing works

Polypharmacology: drug discovery for the future

Reddy, A. S., & Zhang, S. (2012) · Expert Review of Clinical Pharmacology · doi:10.1586/ecp.12.74 · Cited by 486 · see citing works

Big data and artificial intelligence modeling for drug discovery

Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455 · see citing works

The Future of Pharmaceutical Manufacturing Sciences

Rantanen, J., & Khinast, J. (2015) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.24594 · Cited by 452 · see citing works

Recurrent neural networks: design and applications

Stoianov, I. (1999) · Cited by 442 · see citing works

Micro‐/Nanorobots at Work in Active Drug Delivery

Luo, M., Feng, Y., Wang, T., & Guan, J. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201706100 · Cited by 442 · see citing works

A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data

Yu, H., Chen, M., Xu, X. J., Li, Y., Zhao, H., Fang, Y., Li, X., Zhou, W., Wang, W., & Wang, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037608 · Cited by 422 · see citing works

Boosting docking-based virtual screening with deep learning.

Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00355 · Cited by 395 · see citing works

Artificial Intelligence in Drug Design

Heßler, G., & Baringhaus, K. H. (2018) · Molecules · doi:10.3390/molecules23102520 · Cited by 387 · see citing works

Artificial Intelligence for Drug Toxicity and Safety

Basile, A. O., Yahi, A., & Tatonetti, N. P. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.07.005 · Cited by 385 · see citing works

Reinforced Adversarial Neural Computer for de Novo Molecular Design

Lane, E., Asadulaev, A., Ivanenkov, Y. A., Aladinskiy, V., Sánchez-Lengeling, B., Aspuru‐Guzik, A., & Zhavoronkov, A. A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00690 · Cited by 381 · see citing works

<i>De Novo</i> Design of Bioactive Small Molecules by Artificial Intelligence

Merk, D., Friedrich, L., Grisoni, F., & Schneider, G. (2018) · Molecular Informatics · doi:10.1002/minf.201700153 · Cited by 373 · see citing works

End-to-End Differentiable Learning of Protein Structure

AlQuraishi, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.006 · Cited by 371 · see citing works

Sales profession and professionals in the age of digitization and artificial intelligence technologies: concepts, priorities, and questions

Singh, J., Flaherty, K. E., Sohi, R. S., Deeter‐Schmelz, D. R., Habel, J., Meunier‐FitzHugh, K. L., Malshe, A., Mullins, R. R., & Onyemah, V. (2019) · Journal of Personal Selling and Sales Management · doi:10.1080/08853134.2018.1557525 · Cited by 366 · see citing works

Target identification among known drugs by deep learning from heterogeneous networks

Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., Fang, J., Huang, Y., Guo, H., Li, L., et al. (2020) · Chemical Science · doi:10.1039/c9sc04336e · Cited by 357 · see citing works

Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory

Klucznik, T., Mikulak-Klucznik, B., McCormack, M. P., Lima, H. M., Szymkuć, S., Bhowmick, M., Molga, K., Zhou, Y., Rickershauser, L., Gajewska, E. P., et al. (2018) · Chem · doi:10.1016/j.chempr.2018.02.002 · Cited by 353 · see citing works

Process control and scale-up of pharmaceutical wet granulation processes: a review

Faure, A., York, P., & Rowe, R. C. (2001) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/s0939-6411(01)00184-9 · Cited by 316 · see citing works

A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials

Gayvert, K. M., Madhukar, N. S., & Elemento, O. (2016) · Cell chemical biology · doi:10.1016/j.chembiol.2016.07.023 · Cited by 310 · see citing works

The significance of artificial intelligence in drug delivery system design

Hassanzadeh, P., Atyabi, F., & Dinarvand, R. (2019) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2019.05.001 · Cited by 307 · 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

Process Modeling in the Pharmaceutical Industry using the Discrete Element Method

Ketterhagen, W. R., Ende, M. T. A., & Hancock, B. C. (2008) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.21466 · Cited by 250 · see citing works

Interpretable Drug Target Prediction Using Deep Neural Representation

Gao, K. Y., Fokoue, A., Luo, H., Iyengar, A., Dey, S., & Zhang, P. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/468 · Cited by 248 · see citing works

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 245 · see citing works

The Artificial Life Route to Artificial Intelligence

Steels, L., & Brooks, R. A. (2018) · Routledge eBooks · doi:10.4324/9781351001885 · Cited by 241 · see citing works

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

Ciallella, H. L., & Zhu, H. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00393 · Cited by 227 · see citing works

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

Blasiak, A., Khong, J., & Kee, T. W. (2019) · SLAS TECHNOLOGY · doi:10.1177/2472630319890316 · Cited by 225 · see citing works

Artificial intelligence approach fighting COVID-19 with repurposing drugs

Ke, Y., Peng, T.-T., Yeh, T., Huang, W.-Z., Chang, S.-E., Wu, S.-H., Hung, H., Hsu, T., Lee, S., Song, J.-S., et al. (2020) · Biomedical Journal · doi:10.1016/j.bj.2020.05.001 · Cited by 222 · see citing works

iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach

Xiao, X., Min, J., Lin, W., Liu, Z., Cheng, X., & Chou, K. (2014) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2014.998710 · Cited by 218 · see citing works

eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates

Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212 · see citing works

Positioning strategies in business markets

Kalafatis, S. P., Tsogas, M. H., & Blankson, C. (2000) · Journal of Business and Industrial Marketing · doi:10.1108/08858620010349501 · Cited by 201 · see citing works

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

Zang, Q., Mansouri, K., Williams, A. J., Judson, R., Allen, D., Casey, W., & Kleinstreuer, N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00625 · Cited by 199 · see citing works

Global perspectives on the ecology of human-machine systems

Lee, A. T. (1996) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199606)47:6 · Cited by 194 · see citing works

Boosting compound-protein interaction prediction by deep learning

Tian, K., Shao, M., Wang, Y., Guan, J., & Zhou, S. (2016) · Methods · doi:10.1016/j.ymeth.2016.06.024 · Cited by 183 · see citing works

Artificial Intelligence in Drug Discovery

Sellwood, M. A., Ahmed, M., Segler, M., & Brown, N. (2018) · Future Medicinal Chemistry · doi:10.4155/fmc-2018-0212 · Cited by 174 · see citing works

Automated De Novo Drug Design: Are We Nearly There Yet?

Schneider, G., & Clark, D. E. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201814681 · Cited by 168 · see citing works

Brand Positioning Strategy Using Search Engine Marketing1

Dou, Lim, K. H., Su, Zhou, N., & , C. (2010) · MIS Quarterly · doi:10.2307/20721427 · 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

Artificial intelligence in nanomedicine

Ho, D., Wang, P., & Kee, T. W. (2018) · Nanoscale Horizons · doi:10.1039/c8nh00233a · Cited by 150 · see citing works

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

Thafar, M. A., Raies, A. B., Albaradei, S., Essack, M., & Bajić, V. B. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00782 · Cited by 146 · see citing works

Deep Learning in Drug Discovery and Medicine; Scratching the Surface

Dana, D., Gadhiya, S., Surin, L. S., Li, D., Naaz, F., Ali, Q., Paka, L., Yamin, M., Narayan, M., Goldberg, I. D., et al. (2018) · Molecules · doi:10.3390/molecules23092384 · Cited by 141 · see citing works

Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics

Koromina, M., Pandi, M.-T., & Patrinos, G. P. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0151 · Cited by 140 · see citing works

Artificial intelligence in nanotechnology

Sacha, G. M., & Varona, P. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/45/452002 · Cited by 134 · see citing works

Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles

Li, X., Xu, Y., Cui, H. S., Huang, T., Wang, D., Lian, B., Li, W., Qin, G., Chen, L., & Xie, L. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.05.008 · Cited by 127 · see citing works

Using artificial intelligence methods to speed up drug discovery

Álvarez-Machancoses, Ó., & Fernández‐Martínez, J. L. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1621284 · Cited by 127 · see citing works

Artificial intelligence in drug combination therapy

Tsigelny, I. F. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby004 · Cited by 120 · see citing works

Future trends in process automation

Jämsä‐Jounela, S. (2007) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2007.08.003 · Cited by 118 · see citing works

Brand positioning strategy using search engine marketing

Dou, W., Lim, K. H., Su, C., Zhou, N., & Cui, N. (2010) · MIS Quarterly · doi:10.5555/2017458.2017462 · Cited by 115 · see citing works

An intelligent market segmentation system using k-means and particle swarm optimization

Chiu, C.-Y., Chen, Y.-F., Kuo, I.-T., et al. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.05.029 · Cited by 114 · see citing works

Chematica: A Story of Computer Code That Started to Think like a Chemist

Grzybowski, B. A., Szymkuć, S., Gajewska, E. P., Molga, K., Dittwald, P., Wołos, A., & Klucznik, T. (2018) · Chem · doi:10.1016/j.chempr.2018.02.024 · Cited by 114 · cited 1× · see citing works

Computational modeling for formulation design

Mehta, C. H., Narayan, R., & Nayak, U. Y. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.018 · Cited by 114 · see citing works

Intelligent systems in manufacturing: current developments and future prospects

Meziane, F., Vadera, S., Kobbacy, K. A. H., et al. (2000) · Integrated Manufacturing Systems · doi:10.1108/09576060010326221 · Cited by 113 · see citing works

2010 International Conference on Computational Intelligence and Software Engineering

(2010) · doi:10.1109/cise16410.2010 · Cited by 110 · see citing works

PADME: A Deep Learning-based Framework for Drug-Target Interaction PredictionPreprint

Feng, Q., Dueva, E., Cherkasov, A. R., & Ester, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.09741 · Cited by 107 · see citing works

FUTURE TRENDS IN PROCESS AUTOMATION

Jämsä‐Jounela, S. (2007) · IFAC Proceedings Volumes · doi:10.3182/20070213-3-cu-2913.00003 · Cited by 102 · see citing works

Search Algorithms as a Framework for the Optimization of Drug Combinations

Calzolari, D., Bruschi, S., Coquin, L., Schofield, J., Feala, J., Reed, J. C., McCulloch, A. D., & Paternostro, G. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000249 · Cited by 101 · see citing works

Overview of Methods for Overcoming Hindrance to Drug Delivery to Tumors, with Special Attention to Tumor Interstitial Fluid

Baronzio, G., Parmar, G., & Baronzio, M. (2015) · Frontiers in Oncology · doi:10.3389/fonc.2015.00165 · Cited by 100 · see citing works

The potential application of artificial intelligence in transport

Miles, J. C., & Walker, A. J. (2006) · IEE Proceedings - Intelligent Transport Systems · doi:10.1049/ip-its:20060014 · Cited by 96 · see citing works

Deep learning with feature embedding for compound-protein interaction predictionPreprint

Wan, F., & Zeng, J. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/086033 · Cited by 93 · see citing works

A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation

Aksu, B., Paradkar, A. R., de Matas, M., et al. (2012) · Pharmaceutical Development and Technology · doi:10.3109/10837450.2012.705294 · Cited by 91 · see citing works

An integrative machine learning approach for prediction of toxicity-related drug safety

Lysenko, A., Sharma, A., Boroevich, K. A., & Tsunoda, T. (2018) · Life Science Alliance · doi:10.26508/lsa.201800098 · Cited by 88 · see citing works

Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening

Jiménez‐Carretero, D., Abrishami, V., Fernández-de-Manúel, L., Palacios, I., Quílez-Álvarez, A., Díez-Sánchez, A., del Pozo, M. Á., & Montoya, M. C. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006238 · Cited by 84 · see citing works

Computational Screening for Active Compounds Targeting Protein Sequences: Methodology and Experimental Validation

Wang, F., Liu, D., Wang, H., et al. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200264h · Cited by 80 · see citing works

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques

Kumar, R., Sharma, A., Siddiqui, M. H., & Tiwari, R. (2017) · Current Drug Discovery Technologies · doi:10.2174/1570163814666170404160911 · Cited by 79 · see citing works

Controlled drug release by a nanorobot

Fu, J., & Yan, H. (2012) · Nature Biotechnology · doi:10.1038/nbt.2206 · Cited by 78 · see citing works

The Future of Pharmaceutical Product Development and Research

(2020) · Elsevier eBooks · doi:10.1016/c2017-0-01075-3 · Cited by 78 · see citing works

Computational Tools for Polypharmacology and Repurposing

Achenbach, J., Tiikkainen, P., Franke, L., & Proschak, E. (2011) · Future Medicinal Chemistry · doi:10.4155/fmc.11.62 · Cited by 77 · see citing works

Innovation and Marketing in the Pharmaceutical Industry

Ding, M., Eliashberg, J., & Stremersch, S. (2013) · International series in quantitative marketing · doi:10.1007/978-1-4614-7801-0 · Cited by 77 · see citing works

Brand positioning strategies for industrial firms providing customer solutions

Jalkala, A. M., & Keränen, J. (2014) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-10-2011-0138 · Cited by 76 · see citing works

Artificial Neural Network for Drug Design, Delivery and Disposition

(2016) · Elsevier eBooks · doi:10.1016/c2014-0-00253-5 · Cited by 70 · see citing works

Neural networks for cost estimation of shell and tube heat exchangers

Durán, O., Rodríguez, N., & Consalter, L. A. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.014 · Cited by 69 · see citing works

A comparative study of family-specific protein–ligand complex affinity prediction based on random forest approach.

Wang, Y., Guo, Y., Kuang, Q., Pu, X., Ji, Y., Zhang, Z., & Li, M. (2015) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-014-9827-y · Cited by 69 · see citing works

Toward intelligent decision support for pharmaceutical product development

Zhao, C., Jain, A., Hailemariam, L., et al. (2006) · Journal of Pharmaceutical Innovation · doi:10.1007/bf02784878 · Cited by 66 · see citing works

A Qualitative Research on Marketing and Sales in the Artificial Intelligence Age

Yang, Y., & Siau, K. L. (2018) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 63 · see citing works

MOARF, an Integrated Workflow for Multiobjective Optimization: Implementation, Synthesis, and Biological Evaluation

Firth, N. C., Atrash, B., Brown, N., & Blagg, J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00073 · Cited by 61 · see citing works

Artificial intelligence in the pharmaceutical sector: current scene and future prospect

Kalyane, D., Sanap, G., Paul, D., Shenoy, S., Anup, N., Polaka, S., Tambe, V., & Tekade, R. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814455-8.00003-7 · Cited by 61 · see citing works

iDTi-CSsmoteB: Identification of Drug–Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over-Sampling Technique SMOTE

Mahmud, S., Chen, W., Jahan, H., Liu, Y., Sujan, N. I., & Ahmad, S. (2019) · IEEE Access · doi:10.1109/access.2019.2910277 · Cited by 59 · see citing works

Artificial Intelligence and Collusion

Beneke, F., & Mackenrodt, M.-O. (2018) · GRURRR. Gewerblicher Rechtsschutz und Urheberrecht, Rechtsprechungs-Report/GRUR-DVD/GRUR-CD/IIC/Gewerblicher Rechtsschutz und Urheberrecht/Gewerblicher Rechtsschutz und Urheberrecht. Internationaler Teil · doi:10.1007/s40319-018-00773-x · Cited by 55 · see citing works

On the Use of Artificial Neural Networks to Monitor a Pharmaceutical Freeze-Drying Process

Drăgoi, E.-N., Curteanu, S., & Fissore, D. (2013) · Drying Technology · doi:10.1080/07373937.2012.718308 · Cited by 51 · see citing works

Application of a Recurrent Neural Network to Prediction of Drug Dissolution Profiles

Goh, W. Y., Lim, C. P., Peh, K. K., & Subari, K. (2002) · Neural Computing and Applications · doi:10.1007/s005210200003 · Cited by 50 · see citing works

Artificial Neural Networks

Buscema, M., Massini, G., Breda, M., et al. (2018) · Studies in systems, decision and control · doi:10.1007/978-3-319-75049-1_2 · Cited by 49 · see citing works

Artificial Intelligence and Related Technologies Enabled Nanomedicine for Advanced Cancer Treatment

Wilson, B., & Geetha, K. M. (2020) · Nanomedicine · doi:10.2217/nnm-2019-0366 · Cited by 48 · see citing works

KinomeX: a web application for predicting kinome-wide polypharmacology effect of small molecules

Li, Z. S., Li, X., Liu, X., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz519 · Cited by 47 · see citing works

Integrating Artificial and Human Intelligence into Tablet Production Process

Gams, M., Horvat, M., Ožek, M., et al. (2014) · AAPS PharmSciTech · doi:10.1208/s12249-014-0174-z · Cited by 46 · see citing works

A grand product design model for crystallization solvent design

Chai, S., Liu, Q., Liang, X., et al. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106764 · Cited by 45 · see citing works

Mathematical Model-Based Accelerated Development of Extended-release Metformin Hydrochloride Tablet Formulation

Chen, W., Desai, D., Good, D., et al. (2015) · AAPS PharmSciTech · doi:10.1208/s12249-015-0423-9 · Cited by 43 · see citing works

Using Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel Structures

Mason, D. J., Eastman, R. T., Lewis, R. P. I., Stott, I. P., Guha, R., & Bender, A. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01096 · Cited by 43 · see citing works

COMPARISON OF ARTIFICIAL INTELLIGENCE METHODS FOR MODELING PHARMACEUTICAL QSARS

King, R. D., Hirst, J. D., & Sternberg, M. J. E. (1995) · Applied Artificial Intelligence · doi:10.1080/08839519508945474 · Cited by 39 · see citing works

Automated De Novo Drug Design: Are We Nearly There Yet?

Schneider, G., & Clark, D. E. (2019) · Angewandte Chemie · doi:10.1002/ange.201814681 · Cited by 37 · see citing works

Cellular Neural Networks: Analysis, Design and Optimization

Hänggi, M., & Moschytz, G. S. (2000) · Medical Entomology and Zoology · Cited by 35 · see citing works

In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

Jain, N., Gupta, S., Sapre, N., & Sapre, N. S. (2015) · RSC Advances · doi:10.1039/c4ra15478a · Cited by 35 · see citing works

Generalization of a prototype intelligent hybrid system for hard gelatin capsule formulation development

Wilson, W. I., Peng, Y. F., & Augsburger, L. L. (2005) · AAPS PharmSciTech · doi:10.1208/pt060356 · Cited by 35 · see citing works

Estimation of Acid Dissociation Constants Using Graph Kernels

Rupp, M., Körner, R., & Tetko, I. V. (2010) · Molecular Informatics · doi:10.1002/minf.201000072 · Cited by 33 · see citing works

AI protein-folding algorithms solve structures faster than ever

Hutson, M. (2019) · Nature · doi:10.1038/d41586-019-01357-6 · Cited by 33 · see citing works

Artificial Intelligence Tools for Scaling Up of High Shear Wet Granulation Process

Landín, M. (2016) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2016.09.022 · Cited by 32 · see citing works

How Artificial Intelligence and Machine Learning Can Impact Market Design

Milgrom, P., & Tadelis, S. (2019) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226613475.003.0023 · Cited by 30 · see citing works

Artificial Neural Networks

Venkatesan, R., & Li, B. (2017) · CRC Press eBooks · doi:10.4324/9781315154282-3 · Cited by 27 · see citing works

A Prototype Intelligent Hybrid System for Hard Gelatin Capsule Formulation Development

Guo, M., Kalra, G., Wilson, W. I., et al. (2002) · Pharmaceutical technology · Cited by 24 · see citing works

Foundations of Artificial Neural Networks

Bielecki, A. (2018) · Studies in computational intelligence · doi:10.1007/978-3-319-90140-4_3 · Cited by 24 · see citing works

ANN in Pharmaceutical Product and Process Development

Das, M. K., & Chakraborty, T. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801559-9.00014-4 · Cited by 21 · see citing works

Intelligent Quality Management Using Knowledge Discovery in Databases

Wang, X. (2009) · doi:10.1109/cise.2009.5364999 · Cited by 19 · see citing works

Models of Neurons and Perceptrons: Selected Problems and Challenges

Bielecki, A. (2018) · Studies in computational intelligence · doi:10.1007/978-3-319-90140-4 · Cited by 18 · see citing works

A Decision Model for Pharmaceutical Marketing and a Case Study in Turkey

Toker, D., Tozan, H., & Vayvai, O. (2013) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2013.11517592 · Cited by 16 · see citing works

Artificial Intelligence in Prediction of Secondary Protein Structure Using CB513 Database.

Avdagić, Z., Purisevic, E., Omanović, S., et al. (2009) · PubMed · Cited by 12 · see citing works

A LITERATURE REVIEW OF FACTORS AFFECTING PRICE AND COMPETITION IN THE GLOBAL PHARMACEUTICAL MARKET

Park, Y., Goto, D., Yang, K., et al. (2016) · Value in Health · doi:10.1016/j.jval.2016.03.816 · Cited by 9 · see citing works

Management and evolution of the European Union member states in the Big Data era

Anghelache, C., & Pellat, G. (2020) · Sciendo eBooks · doi:10.2478/9788395720475 · Cited by 2 · see citing works

A Review of the Role of Artificial Intelligence in Healthcare

Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915 · 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

Generative AI in medical practice: in-depth exploration of privacy and security challenges

Chen, Y., & Esmaeilzadeh, P. (2024) · Journal of Medical Internet Research · doi:10.2196/53008 · Cited by 353 · see citing works

A Review of Trustworthy and Explainable Artificial Intelligence (XAI)

Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352 · see citing works

Blockchain and Artificial Intelligence Technology in E-Health

Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16223-0 · Cited by 281 · see citing works

Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare

Yelne, S., Chaudhary, M., Dod, K., Sayyad, A., & Sharma, R. (2023) · Cureus · doi:10.7759/cureus.49252 · Cited by 190 · see citing works

Protein neddylation and its role in health and diseases

Zhang, S., Yu, Q., Li, Z., Zhao, Y., & Sun, Y. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01800-9 · Cited by 171 · see citing works

Curriculum frameworks and educational programs in AI for medical students, residents, and practicing physicians: scoping review

Tolentino, R., Baradaran, A., Gore, G., Pluye, P., & Abbasgholizadeh-Rahimi, S. (2024) · JMIR Medical Education · doi:10.2196/54793 · Cited by 165 · see citing works

Advancing drug safety in drug development: bridging computational predictions for enhanced toxicity prediction

Amorim, A. M. B., Piochi, L. F., Gaspar, A. T., Preto, A. J., Rosário-Ferreira, N., & Moreira, I. S. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00352 · Cited by 132 · see citing works

Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients

Busch, F., Hoffmann, L., Xu, L., Zhang, L. J., Hu, B., García-Juárez, I., Toapanta-Yanchapaxi, L. N., Gorelik, N., Gorelik, V., Rodriguez-Granillo, G. A., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.14452 · Cited by 124 · see citing works

Explainable artificial intelligence for drug discovery and development: a comprehensive survey

Alizadehsani, R., Oyelere, S. S., Hussain, S., Jagatheesaperumal, S. K., Calixto, R. R., Rahouti, M., Roshanzamir, M., & De Albuquerque, V. H. C. (2024) · IEEE Access · doi:10.1109/access.2024.3373195 · Cited by 124 · see citing works

Artificial intelligence and neuroscience: transformative synergies in brain research and clinical applications

Onciul, R., Tataru, C.-I., Dumitru, A. V., Crivoi, C., Serban, M., Covache-Busuioc, R.-A., Radoi, M. P., & Toader, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020550 · Cited by 113 · see citing works

Toward Trustworthy Artificial Intelligence (TAI) in the Context of Explainability and Robustness

chander, B., John, C., Warrier, L., & Gopalakrishnan, K. (2025) · ACM Computing Surveys · doi:10.1145/3675392 · Cited by 108 · see citing works

THE ROLE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE: A SYSTEMATIC REVIEW OF APPLICATIONS AND CHALLENGES

Udegbe, F. C., Ebulue, O. R., Ebulue, C. C., & Ekesiobi, C. S. (2024) · International Medical Science Research Journal · doi:10.51594/imsrj.v4i4.1052 · Cited by 103 · see citing works

Artificial intelligence &amp; clinical nutrition: What the future might have in store

Bond, A., Mccay, K., & Lal, S. (2023) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2023.07.082 · Cited by 79 · see citing works

International pharmacy students' perceptions towards artificial intelligence in medicine—A multinational, multicentre cross‐sectional study

Busch, F., Hoffmann, L., Truhn, D., Palaian, S., Alomar, M., Shpati, K., Makowski, M. R., Bressem, K. K., & Adams, L. C. (2024) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15911 · Cited by 80 · see citing works

Knowledge, attitude and practice among pharmacy students and faculty members towards artificial intelligence in pharmacy practice: A multinational cross-sectional study

Hasan, H. E., Jaber, D., Al Tabbah, S., Lawand, N., Habib, H. A., & Farahat, N. M. (2024) · PLOS ONE · doi:10.1371/journal.pone.0296884 · Cited by 76 · see citing works

Can Artificial Intelligence Replace the Unique Nursing Role?

Mohanasundari, S. K., Kalpana, M., Madhusudhan, U., Vasanthkumar, K., B, R., Singh, R., Vashishtha, N., & Bhatia, V. (2023) · Cureus · doi:10.7759/cureus.51150 · Cited by 71 · see citing works

Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes

Niazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18091271 · Cited by 61 · see citing works

Artificial intelligence for dementia drug discovery and trials optimization

Doherty, T., Yao, Z., Khleifat, A. A. L., Tantiangco, H., Tamburin, S., Albertyn, C., Thakur, L., Llewellyn, D. J., Oxtoby, N. P., Lourida, I., et al. (2023) · Alzheimer's &amp; Dementia · doi:10.1002/alz.13428 · Cited by 48 · see citing works

Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels

Henzler, D., Schmidt, S., Koçar, A., Herdegen, S., Lindinger, G. L., Maris, M. T., Bak, M. A. R., Willems, D. L., Tan, H. L., Lauerer, M., et al. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-12664-2 · Cited by 54 · see citing works

ChatGPT in pharmacy practice: a cross-sectional exploration of Jordanian pharmacists' perception, practice, and concerns

(2023) · Journal of Pharmaceutical Policy and Practice · doi:10.1186/s40545-023-00624-2 · Cited by 42 · 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

A new paradigm for applying deep learning to protein–ligand interaction prediction

Wang, Z., Wang, S., Li, Y., Guo, J., Wei, Y., Mu, Y., Zheng, L., & Li, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae145 · Cited by 41 · see citing works

Accelerating Parkinson’s Disease drug development with federated learning approaches

Khanna, A., Adams, J., Antoniades, C., Bloem, B. R., Carroll, C., Cedarbaum, J., Cosman, J., Dexter, D. T., Dockendorf, M. F., Edgerton, J., et al. (2024) · npj Parkinson's Disease · doi:10.1038/s41531-024-00837-5 · Cited by 21 · see citing works

A Review on the Integration of Artificial Intelligence in Healthcare

Pargaien, A. V., Pargaien, S., Nawaz, A., & Kumar, T. (2024) · 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc60852.2024.10689737 · Cited by 4 · see citing works

People's Drug Guide to Deadly Drug Interactions Focuses on Drug/Drug, Drug/Food, and Drug/Vitamin Interactions

(1998) · Gastroenterology Nursing · doi:10.1097/00001610-199805000-00012

Widening bottlenecks in drug discovery Glimpses from Drug Discovery Technology Europe 2005

Högberg, T. (2005) · Drug Discovery Today · doi:10.1016/s1359-6446(05)03490-2 · Cited by 3 · see citing works

Whole-animal cellular and molecular imaging to accelerate drug development

Contag, P. R. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(02)02268-7 · Cited by 94 · see citing works

Biochemical marker discovery, testing and evaluation for facilitating OA drug discovery and development

Bay‐Jensen, A., Thudium, C. S., Gualillo, O., & Mobasheri, A. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.10.008 · Cited by 25 · see citing works

Minimizing the Potential for Drug Bioactivation of Drug Candidates to Success in Clinical Development

Nassar, A. F. (2010) · Pharmaceutical Sciences Encyclopedia · doi:10.1002/9780470571224.pse111