Assessing the impact of generative AI on medicinal chemistry

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612

SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

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Applications of machine learning in drug discovery and development

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.

Gómez‐Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera‐Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru‐Guzik, A. (2018) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,813

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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Computer-based de novo design of drug-like molecules

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GuacaMol: Benchmarking Models for de Novo Molecular Design

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druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00346 · Cited by 587

Active learning inspired method in generative models

Lan, G., Xiao, S., Yang, J., Wen, J., Lu, W. B., & Gao, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123582 · Cited by 20

Unraveling generative AI in BBC News: application, impact, literacy and governance

Lao, Y., & You, Y. (2024) · Transforming Government People Process and Policy · doi:10.1108/tg-01-2024-0022 · Cited by 19

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

Tse, E. G., Aithani, L., Anderson, M., Cardoso-Silva, J., Cincilla, G., Conduit, G. J., Galushka, M., Guan, D., Hallyburton, I., Irwin, B. W. J., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c00313 · Cited by 18

Integrating computational and experimental advances in bone multiscale mechanics

Rowe, J., Shen, S. C.-Y., de Alcântara, A. C. S., Skaf, M. S., Dini, D., Harrison, N. M., Hansen, U., Buehler, M. J., & Abel, R. L. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101474 · Cited by 18

Next Decade’s AI-Based Drug Development Features Tight Integration of Data and Computation

Luo, Y., Peng, J., & Ma, J. (2022) · Health Data Science · doi:10.34133/2022/9816939 · Cited by 17

Discovery of TIGIT inhibitors based on DEL and machine learning

Xiong, F., Yu, M., Xu, H., Zhong, Z., Li, Z., Guo, Y., Zhang, T., Zeng, Z., Jin, F., & He, X. (2022) · Frontiers in Chemistry · doi:10.3389/fchem.2022.982539 · Cited by 15

AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data

Shimizu, Y., Ohta, M., Ishida, S., Terayama, K., Osawa, M., Honma, T., & Ikeda, K. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00791-z · Cited by 20

Chemistry-centric explanation of machine learning models

Rodríguez-Pérez, R., & Bajorath, J. (2021) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2021.100009 · Cited by 15

Machine learning & deep learning tools in pharmaceutical sciences: A comprehensive review

Javid, S., Rahmanulla, A., Ahmed, M. G., Sultana, R., & Kumar, B. R. P. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.11.003 · Cited by 18

Derivatization Design of Synthetically Accessible Space for Optimization: In Silico Synthesis vs Deep Generative Design

Makara, G. M., Kovács, L., Szabó, I., & Pőcze, G. (2021) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.0c00540 · Cited by 14

Artificial Intelligence in Compound Design

Grebner, C., Matter, H., & Heßler, G. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_15 · Cited by 17

Quality by Computational Design: Harnessing AI to Advance Rational Drug Development

Wang, N., Zhong, H., Xiong, P., Zhu, J., & Ouyang, D. (2025) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2025.115764 · Cited by 17

Medicinal Chemists versus Machines Challenge: What Will It Take to Adopt and Advance Artificial Intelligence for Drug Discovery?

Zhavoronkov, A. A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00435 · Cited by 13

AI in 3D compound design

Hadfield, T. E., & Deane, C. M. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.102326 · Cited by 12

Beyond the Turing Test: Exploring the implications of generative AI for category construction

Phillips, N., Kalvapalle, S. G., & Kennedy, M. (2024) · Organization Theory · doi:10.1177/26317877241275113 · Cited by 14

The physics-AI dialogue in drug design

Vargas-Rosales, P. A., & Caflisch, A. (2025) · RSC Medicinal Chemistry · doi:10.1039/d4md00869c · Cited by 15

Guided structure-based ligand identification and design via artificial intelligence modeling

Filippo, J. I. D., & Cavasotto, C. N. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1979514 · Cited by 12

Transform Drug Discovery and Development With Generative Artificial Intelligence

Lavecchia, A. (2025) · Wiley eBooks · doi:10.1002/9781394280735.ch25 · Cited by 14

Artificial Intelligence-Assisted Multiparameter Size Discrimination of Silver Nanoparticles through Electrochemical Collision

Xu, Y., Jiang, W., Bai, Y., Yang, Y.-J., & Zhang, Z. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.3c05115 · Cited by 10

Integrating smart city technologies for sustainable pavement infrastructure

Furtado, L. S., Bessa, I. S., Gurjão, N. D. O., & Soares, J. B. (2025) · Canadian Journal of Civil Engineering · doi:10.1139/cjce-2024-0217 · Cited by 11

Maladaptive trained immunity of adipose tissue macrophages unraveling the link to obesity, T2DM, and beyond

Chen, S., Fu, J., Yan, J., Zhao, R., Qian, C., Yisimayili, Z., & Wu, J. (2025) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00071.2025 · Cited by 10

Feature-Based Interpolation and Geodesics in the Latent Spaces of Generative Models

Struski, Ł., Sadowski, M., Danel, T., Tabor, J., & Podolak, I. T. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3251848 · Cited by 10

Data-oriented protein kinase drug discovery

Xerxa, E., & Bajorath, J. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.116413 · Cited by 11

Measuring Impact of Generative AI in Software Development and Innovation

Kumar, T., Garg, V., Lalar, S., & Kumar, R. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-1682-1_6 · Cited by 9

Allostery Illuminated: Harnessing AI and Machine Learning for Drug Discovery

Blanco, M.-J., Buskes, M. J., Govindaraj, R. G., Ipsaro, J. J., Prescott-Roy, J., & Padyana, A. K. (2024) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.4c00260 · Cited by 9

DRACON: disconnected graph neural network for atom mapping in chemical reactions

Nikitin, F., Isayev, O., & Strijov, V. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp04748a · Cited by 9

MAIP: An Open-Source Tool to Enrich High-Throughput Screening Output and Identify Novel, Druglike Molecules with Antimalarial Activity

Bosc, N., Félix, E., Gardner, M., Mills, J. E. J., Timmerman, M., Asveld, D., Rensen, K., Mukherjee, P., Das, R., Chenu, E., et al. (2023) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.3c00369 · Cited by 8

Closing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards

Subramanian, A., Gao, W., Barzilay, R., Grossman, J. C., Jaakkola, T., Jegelka, S., Li, M., Li, J., Matusik, W., Olivetti, E., et al. (2024) · An MIT Exploration of Generative AI · doi:10.21428/e4baedd9.92e511e3 · Cited by 7

Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence

Miljković, F. N., & Bajorath, J. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.4c00659 · Cited by 8

Foundation models in clinical oncology: Progresses and perspectives

Li, J., Fu, J., Geng, X., & Wang, H. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.218220 · Cited by 8

Exploring the Ethical Implications of Generative AI in Healthcare

Kumar, D., Dhalwal, R., & Chaudhary, A. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-2964-1.ch011 · Cited by 6

A Survey on Evolutionary Computation-Based Drug Discovery

Yu, Q., Lin, Q., Ji, J., Zhou, W., He, S., Zhu, Z., & Tan, K. C. (2024) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2024.3382145 · Cited by 8

Comparison of Structure- and Ligand-Based Scoring Functions for Deep Generative Models: A GPCR Case StudyPreprint

Thomas, M., Smith, R., O’Boyle, N. M., de Graaf, C., & Bender, A. (2021) · ChemRxiv · doi:10.26434/chemrxiv.14138147 · Cited by 6

Machine Learning for the Optimization of Chemical Reaction Conditions

Almeida, A. F., & Rodrigues, T. (2023) · Wiley eBooks · doi:10.1002/9781119855668.ch10 · Cited by 6

Advances in the Application of In Silico ADMET Models – An Industry Perspective

Wang, W., Melnikov, F., Napoli, J., & Desai, P. V. (2024) · Wiley eBooks · doi:10.1002/9783527840748.ch21 · Cited by 7

A Risk-Tiered Validation Framework for Artificial Intelligence in Drug Discovery: From Reproducibility to Clinical Translation

Niazi, S. K. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27104349 · Cited by 7

Artificial Intelligence in drug discovery and development: current landscape, challenges, and future perspectives

Seyhan, A. A., & Carini, C. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2026.105461 · Cited by 7

Deep generative models in the quest for anticancer drugs: ways forward

Romanelli, V., Cerchia, C., & Lavecchia, A. (2024) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2024.1362956 · Cited by 5

Generative AI in Drug Designing: Current State-of-the-Art and Perspectives

Ahmad, S., Bano, N., Sharma, S., Sakina, S., Ahmad, N., & Raza, K. (2024) · Studies in computational intelligence · doi:10.1007/978-981-97-8460-8_20 · Cited by 8

Generative Kinase Inhibitor Modeling Viewed From a Medicinal Chemistry Perspective

Bajorath, J. (2023) · Future Medicinal Chemistry · doi:10.4155/fmc-2023-0029 · Cited by 5

Revolutionizing Program Evaluation with Generative AI: An Evidence-Based Methodology

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Tools for AI-driven Development of Research Competencies

Valbuena, C. N. A. (2023) · LatIA · doi:10.62486/latia202316 · Cited by 20

Generative artificial intelligence in healthcare: current status and future directions

Ouanes, K. (2024) · Italian Journal of Medicine · doi:10.4081/itjm.2024.1782 · Cited by 6

Lessons learnt from machine learning in early stages of drug discovery

Cavasotto, C. N., Filippo, J. I. D., & Scardino, V. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2354279 · Cited by 5

A Framework for Autonomous AI-Driven Drug DiscoveryPreprint

Selinger, D. W., Wall, T. R., Stylianou, E. S., Khalil, E. M., Gaetz, J., & Levy, O. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.17.629024 · Cited by 5

AI-enabled individual learning strategies and scientific innovation: a case from the field of computer science

Lin, R., Li, Y., Li, W., Ji, Z., & Li, B. (2025) · Scientometrics · doi:10.1007/s11192-025-05345-8 · Cited by 6

Repositioning Natural Products in Modern Drug Discovery: Technological Innovation, Systems Pharmacology, and Pathological Validation

Nakadate, K., Ito, N., & Kawakami, K. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27104330 · Cited by 4

Harnessing Artificial Intelligence (AI) for a greener future: a review of AI advancements in green chemistry, chemical processes and sustainable materials

Elkhatat, A. M., & Al‐Muhtaseb, S. A. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07326-7 · Cited by 5

Comparing Molecules Generated by MMPDB and REINVENT4 with Ideas from Drug Discovery Design Teams

Sun, S., & Huggins, D. J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00250 · Cited by 4

Discoidin Domain Receptor 1 Inhibitors: Advances and Future Directions for Novel Therapeutics with Aid of DNA Encoded Library Screens and Artificial Intelligence

Sanawar, R., Sahayasheela, V. J., Sarath, P., & Dan, V. M. (2023) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/1389557523666230125114921 · Cited by 4

Artificial Intelligence and Machine Learning for Sustainable Molecular-to-Systems Engineering

Dowling, A. W. (2024) · Systems and Control Transactions · doi:10.69997/sct.114705 · Cited by 5

Applying Deutsch’s concept of good explanations to artificial intelligence and neuroscience – An initial exploration

Elton, D. C. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2020.12.002 · Cited by 5

Computational approaches for drug discovery against COVID-19

Sharma, P., Joshi, T., Mathpal, S., Tamta, S., & Chandra, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91794-0.00024-x · Cited by 4

Deep Denerative Models for Drug Design and ResponsePreprint

Zadorozhny, K., & Nuzhna, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06469 · Cited by 4

Learning from the successes and failures of early artificial intelligence (AI) adoption for drug discovery in Big BioPharma

Braddock, M., & Jeziorski, K. (2026) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2026.2624023 · Cited by 3

Generative Artificial Intelligence in Visual Content: A Review of the Influence on Consumer Perception and Perspective

Singh, A., Kumar, G., & Dhariwal, A. (2025) · Springer eBooks · doi:10.1007/978-981-96-2355-6_5 · Cited by 3

Demystifying Artificial Intelligence (AI) for Early Childhood and Elemen- tary Education: A Case Study of Perceptions of AI of State of Missouri Educators

Arnone, K., Hutson, J., & Woodruff, K. (2023) · Journal of Innovation and Technology · doi:10.61453/joit.v2023no18 · Cited by 3

Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technology

Zeng, Z., Nie, Y.-C., Ding, N., Ding, Q.-J., Ye, W.-T., Yang, C. H., Sun, M., Weinan, E., Zhu, R., & Liu, Z. (2023) · Chemical Science · doi:10.1039/d3sc02483k · Cited by 3

Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for ResearchPreprint

Lukyanenko, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5153894 · Cited by 3

Applications of machine learning in quantitative structure-activity relationships (QSAR)

Walters, W. P. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00032-7 · Cited by 2

Advances in small molecule representations and AI-driven drug research: bridging the gap between theory and application

Liu, J., Chang, S., Deng, Q., Ding, Y., & Pan, Y. F. (2025) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(25)60946-0 · Cited by 3

Fine-Tuning a Transformer Model for METTL3 Lead Optimization

Matter, C. M., & Caflisch, A. (2026) · ACS Bio & Med Chem Au · doi:10.1021/acsbiomedchemau.5c00198 · Cited by 2

Design and Research of a Dual-Target Drug Molecular Generation Model Based on Reinforcement Learning

Li, P., Yan, Z., Zhou, Y., Li, H., Gao, W., & Li, D. (2026) · Inventions · doi:10.3390/inventions11010012 · Cited by 2

Application of Reinforcement Learning Techniques in De Novo Drug Design: A Systematic Literature Review

Sampa, M. B., & Aziz, N. H. A. (2026) · Health Science Reports · doi:10.1002/hsr2.72132 · Cited by 3

Target discovery and drug design in the era of artificial intelligence

Fleming, P., & Ivanov, A. A. (2026) · Medicinal Chemistry Research · doi:10.1007/s00044-026-03562-1 · Cited by 4

Machine Learning-Driven QSAR Modeling of FXIa Inhibitors for Virtual Screening and Rational Drug Design

Kaya, A. O., Emre, M. C., & Emre, N. (2026) · Pharmaceuticals · doi:10.3390/ph19060912 · Cited by 3

The Fragility of Bioactivity Prediction: Rigorous Dataset Splits Expose the Illusion of ML Accuracy

Lee, K., Moldagulov, G., & Grzybowski, B. A. (2026) · Chemistry - A European Journal · doi:10.1002/chem.71208 · Cited by 3

Machine-Learning-Driven Molecular Design and Structure–Property–Performance Relationships in Pharmaceutical Chemistry

Kabdraisova, A. Z., Umbetova, A. K., Kairalapova, G., Litvinenko, Y. A., Sassykova, L. R., Yelibayeva, N. S., Burasheva, G. S., Berganayeva, A. E., Assylkhanov, Z. S., Dauletova, M. D., et al. (2026) · Molecules · doi:10.3390/molecules31122162 · Cited by 3

Artificial intelligence in healthcare and drug discovery: A scoping review of current trends, challenges, and future directions

Shakeri, E., & Far, B. H. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100442 · Cited by 3

Specific contributions of artificial intelligence to interdisciplinary life science research – exploring and communicating new opportunities

Bajorath, J. (2022) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100052 · Cited by 2

Artificial intelligence in early stages of structure-based drug discovery

Ravichandran, R., Park, S., Skorupan, S., Bessette, K., & Gentile, F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-23932-8.00001-7 · Cited by 2

Generative Artificial Intelligence for Virology

Bhargava, H., Sharma, A., Valadi, J. K., Suravajhala, P., & Chatterjee, S. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4546-8_11 · Cited by 2

MolScore: A scoring and evaluation framework for de novo drug designPreprint

Thomas, M. C., O’Boyle, N. M., Bender, A., & de Graaf, C. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-c4867 · Cited by 1

MolScore: A scoring and evaluation framework for de novo drug designPreprint

Thomas, M. C., O’Boyle, N. M., Bender, A., & de Graaf, C. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2023-c4867-v2 · Cited by 1

AI-Guided Discovery of LDHA Inhibitors Targeting Cancer Metabolism Using Machine Learning and Generative Chemistry: An End-to-End Drug Discovery PipelinePreprint

Petalcorin, M. F., & Petalcorin, M. I. R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.13.653702 · Cited by 2

Generative AI: Evolution, Applications, Challenges And Future ProspectsPreprint

Garg, P. K., Bhatt, M., Parmar, R., & Arsalan, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5357852 · Cited by 2

Benchmarking Real-World Applicability of Molecular Generative Models from De novo Design to Lead Optimization with MolGenBenchPreprint

Cao, D., Fan, Z., Yu, J., Chen, M., Jiang, X., Sheng, X., Wang, X., Zeng, C., Luo, X., Teng, D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.03.686215 · Cited by 2

Reimagining Education - The Role of E-Learning, Creativity, and Technology in the Post-Pandemic Era

Mistretta, S. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.110350 · Cited by 5

Drug discovery technologies–Current and future trends

Noe, M. C., Steppan, C. M., & Weston, A. D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00019-4 · Cited by 1

The role of Artificial Intelligence in drug discovery

Rotake, S. B., Hatwar, P. R., Bakal, R. L., & Meshram, S. I. (2025) · Journal of Drug Delivery and Therapeutics · doi:10.22270/jddt.v15i7.7251 · Cited by 1

Redefining Biomolecular Frontiers: The Impact of Artificial Intelligence in Biochemistry and Medicine

Anih, D. C., Arowora, K. A., Abah, M. A., Ugwuoke, K. C., & Habibu, B. (2025) · Journal of Medical Sciences(Faisalabad) · doi:10.3923/jms.2025.1.10 · Cited by 2

Artificial intelligence in GPCR drug discovery: A paradigm shift in computational pharmacology

Vinter, A., & Grgičević, I. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00047-9 · Cited by 1

Artificial intelligence-enabled personalisation of oral drug delivery: From data-driven design to on-demand manufacturing

Abdalla, Y., Nandiraju, L. P., Yue, H., Monsales, C. B. D., Yeung, C., & Basit, A. W. (2026) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2026.115855 · Cited by 3

Artificial Intelligence for Predicting Small-Molecule Bioactive Conformations

Liu, Y., Chen, S., Lin, K., Gao, S.-G., & Li, X. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c03198 · Cited by 1

Defining a Chemical Space of π-Conjugated Hydrocarbons by Unit-Based Construction

SUGA, K., Takahashi, H., Terayama, K., Sumita, M., & Saito, S. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00213 · Cited by 1

Data and methods for identifying artificial intelligence-related patents

Wu, T., Min, C., Ding, W. W., Wang, G., & Zhang, K. (2026) · Research Policy · doi:10.1016/j.respol.2026.105599 · Cited by 1

Does generative AI potentially revolutionize the international operations and the supply chain industry in the data-driven era?

Kumar, A., Shankar, A., Behl, A., & Zhang, Z. (2026) · International Marketing Review · doi:10.1108/imr-10-2024-0375 · Cited by 1

HTS-Oracle v2: Prospective Ai-Guided Discovery and Experimental Validation of Small Molecule Modulators Across Multiple Targets

Abdel‐Rahman, S. A., & Gabr, M. T. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01522 · Cited by 1

AI-driven computational drug design: tools, workflow and challenges

Dhir, R., Ghosh, P., Sharma, D., & Asati, V. (2026) · RSC Advances · doi:10.1039/d6ra02374f · Cited by 1

In silico drug discovery pipelines targeting antibiotic resistance: from genomes to leads

Gupta, R., Singh, P., Ravi, K., Abomughaid, M. M., Lakhanpal, S., Sharma, R. V., Kumar, N., & Jha, N. K. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1839606 · Cited by 3

GENERATIVE AI: EVOLUTION, APPLICATIONS, CHALLENGES, AND FUTURE PROSPECTS

Parmar, R., Garg, P. K., Bhatt, M., & Arsalan, M. (2025) · Global Conference on AI Research and Emerging Developments (G-CARED) · doi:10.63169/gcared2025.p6 · Cited by 1

Artificial Intelligence as an Enabler for Phenotypic Drug Discovery

Renaud, N., & Wang, Y. (2020) · Royal Society of Chemistry eBooks · doi:10.1039/9781839160721-00104 · Cited by 1

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

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