Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507

UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations

Rappé, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A., & Skiff, W. M. (1992) · Journal of the American Chemical Society · doi:10.1021/ja00051a040 · Cited by 10,603

Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608

How to improve R&D productivity: the pharmaceutical industry's grand challenge

Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566

A Nonlinear Mapping for Data Structure Analysis

Sammon, J. W. (1969) · IEEE Transactions on Computers · doi:10.1109/t-c.1969.222678 · Cited by 3,364

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

Tensor-Train Decomposition

Oseledets, I. V. (2011) · SIAM Journal on Scientific Computing · doi:10.1137/090752286 · Cited by 2,860

ZINC: A Free Tool to Discover Chemistry for Biology

Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S. D., & Coleman, R. G. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3001277 · Cited by 2,611

Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach

Çeli̇k, F. K., Doğan, S., & Karaduman, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109209 · Cited by 15

Application of machine learning in LC-MS-based non-targeted analysis

Jin, Z.-L., Chen, L., Wang, Y., Shi, C.-T., Zhou, Y., & Xia, B. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118243 · Cited by 19

A Point of Inflection and Reflection on Systems Chemical Biology

Johnson, E. O., & Hung, D. T. (2019) · ACS Chemical Biology · doi:10.1021/acschembio.9b00714 · Cited by 15

Non-linear Deep Neural Network for Rapid and Accurate Prediction of Phenotypic Responses to Kinase Inhibitors

Vijay, S., & Gujral, T. S. (2020) · iScience · doi:10.1016/j.isci.2020.101129 · Cited by 15

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

Generating stable molecules using imitation and reinforcement learning

Meldgaard, S. A., Köhler, J., Mortensen, H. L., Christiansen, M.-P. V., Noé, F., & Hammer, B. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac3eb4 · Cited by 19

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

Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods

Sauer, S., Matter, H., Heßler, G., & Grebner, C. (2022) · Frontiers in Chemistry · doi:10.3389/fchem.2022.1012507 · Cited by 14

QuantumTox: Utilizing quantum chemistry with ensemble learning for molecular toxicity prediction

Wang, X., Wang, L., Wang, S., Ren, Y., Chen, W., Li, X., Han, P., & Song, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106744 · Cited by 14

Scikit-Mol brings cheminformatics to Scikit-LearnPreprint

Bjerrum, E. J., Bachorz, R. A., Bitton, A., Choung, O.-H., Chen, Y., Esposito, C., Ha, S. V., & Poehlmann, A. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-fzqwd · Cited by 14

From genomics to clinic: the transformative impact of AI in pharmacogenomics and personalized medicine

Jakka, A. L., Chacko, R. M., Vasam, M., Alagarsamy, S., Chandragiri, S. S., Gavini, S. D., & Mathew, M. J. (2025) · Pharmacogenomics · doi:10.1080/14622416.2025.2591596 · Cited by 15

In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot

An, Y., Lim, J., Glavatskikh, M., Wang, X., Norris-Drouin, J. L., Hardy, P. B., Leisner, T. M., Pearce, K. H., & Kireev, D. (2024) · Nature Communications · doi:10.1038/s41467-024-49892-9 · Cited by 12

Machine Learning and Deep Learning Models for Predicting Noncovalent Inhibitors of AmpC β-Lactamase

Bagdad, Y., Sisquellas, M., Arthur, M., & Miteva, M. A. (2024) · ACS Omega · doi:10.1021/acsomega.4c03834 · Cited by 14

Dielectric tensor prediction for inorganic materials using latent information from preferred potential

Mao, Z., Li, W., & Tan, J. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01450-z · Cited by 13

Utilizing AI for the Identification and Validation of Novel Therapeutic Targets and Repurposed Drugs for Endometriosis

Liu, B. H. M., Lin, Y., Long, X., Hung, S. W., Gaponova, A. V., Ren, F., Zhavoronkov, A. A., Pun, F. W., & Wang, C. C. (2024) · Advanced Science · doi:10.1002/advs.202406565 · Cited by 16

PRMT5 inhibition has a potent anti-tumor activity against adenoid cystic carcinoma of salivary glands

Mishra, V., Singh, A., Korzinkin, M., Cheng, X., Wing, C., Sarkisova, V. A., Koppayi, A. L., Pogorelskaya, A., Glushchenko, O. E., Sundaresan, M., et al. (2025) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-024-03270-x · Cited by 17

Barlow Twins deep neural network for advanced 1D drug–target interaction prediction

Schuh, M. G., Boldini, D., Bohne, A. I., & Sieber, S. A. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00952-2 · Cited by 13

DiffMC‐Gen: A Dual Denoising Diffusion Model for Multi‐Conditional Molecular Generation

Yang, Y., Gu, S., Liu, B., Gong, X., Lu, R., Qiu, J., Yao, X., & Liu, H. (2025) · Advanced Science · doi:10.1002/advs.202417726 · Cited by 15

A 3D generation framework using diffusion model and reinforcement learning to generate multi-target compounds with desired properties

Yuan, Y., Pan, X., Li, X., Zhang, R., & Su, W. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01035-y · 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

Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow

Madzhidov, T., Rakhimbekova, A., Afonina, V. A., Gimadiev, T. R., Mukhametgaleev, R. N., Ирекович, Н. Р., Baskin, I. I., & Varnek, A. A. (2021) · Mendeleev Communications · doi:10.1016/j.mencom.2021.11.003 · Cited by 15

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

Computational Toxicology

Schmidt, F. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11534-x · Cited by 13

KUALA: a machine learning-driven framework for kinase inhibitors repositioning

Simone, G. D., Sardina, D. S., Gulotta, M. R., & Perricone, U. (2022) · Scientific Reports · doi:10.1038/s41598-022-22324-8 · Cited by 13

Deep attention based variational autoencoder for antimicrobial peptide discoveryPreprint

Ghorbani, M., Prasad, S., Brooks, B. R., & Klauda, J. B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.08.499340 · Cited by 15

Applications of Deep Learning in Healthcare in the Framework of Industry 5.0

Tripathi, P., Kumar, N., Paroha, K. K., Rai, M., & Panda, M. (2024) · Advances in web technologies and engineering book series · doi:10.4018/979-8-3693-0782-3.ch005 · Cited by 12

Applications of artificial intelligence in drug discovery for neurological diseases

Ekins, S., & Lane, T. R. (2025) · Neurotherapeutics · doi:10.1016/j.neurot.2025.e00624 · Cited by 12

Machine Learning for De Novo Molecular Generation: A Comprehensive Review

Chen, Y., & Xue, W. (2026) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.5c00861 · Cited by 13

Natural Compound ZINC12899676 Reduces Porcine Epidemic Diarrhea Virus Replication by Inhibiting the Viral NTPase Activity

Wang, P., Wang, X., Liu, X., Sun, M., Liang, X., Bai, J., & Jiang, P. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.879733 · Cited by 12

A novel strategy for designing the magic shotguns for distantly related target pairs

Luo, Y., Wang, P., Mou, M., Zheng, H., Hong, J., Tao, L., & Zhu, F. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac621 · Cited by 13

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets

Odunitan, T. T., Saibu, O. A., Apanisile, B. T., Omoboyowa, D. A., Balogun, T. A., Awe, A. V., Ajayi, T. M., Olagunju, G. V., Mahmoud, F. M., Akinboade, M. W., et al. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107737 · Cited by 13

Co-guided Dual-channel Graph Neural Networks for the prediction of compound–protein interaction

Wu, Z., Ma, H., Deng, B., Li, Z., & Chang, L. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111875 · Cited by 12

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Sun, D., Macedonia, C. R., Chen, Z., Chandrasekaran, S., Najarian, K., Zhou, S. Y., Cernak, T., Ellingrod, V. L., Jagadish, H. V., Marini, B. L., et al. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01684 · Cited by 14

Pharma innovation: how evolutionary economics is shaping the future of pharma R&D

Schuhmacher, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104222 · Cited by 14

Structure-Based Rational Design and Evaluation of BET-Aurora Kinase Dual-Inhibitors for Treatment of Cancers

Lyu, K., Ren, Y., Mou, J., Yang, Y., Pan, Y., Zhang, H., Li, Y., Cao, D., Chen, L., Chen, D., et al. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01933 · Cited by 14

Integrating Pharmacokinetics and Quantitative Systems Pharmacology Approaches in Generative Drug Design

van den Maagdenberg, H. W., van Otterloo, J. D. M., van Hasselt, J. G. C., van der Graaf, P. H., & van Westen, G. J. P. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00107 · Cited by 13

Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring

Fujita, T., Terayama, K., Sumita, M., Tamura, R., Nakamura, Y., Naito, M., & Tsuda, K. (2022) · Science and Technology of Advanced Materials · doi:10.1080/14686996.2022.2075240 · Cited by 12

Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability

Galkin, F., Kochetov, K., Keller, M., Zhavoronkov, A. A., & Etcoff, N. L. (2022) · Aging · doi:10.18632/aging.204061 · Cited by 12

Epidemiological challenges in pandemic coronavirus disease ( COVID ‐19): Role of artificial intelligence

Dasgupta, A., Bakshi, A., Mukherjee, S., Das, K., Talukdar, S., Chatterjee, P., Mondal, S., Das, P., Ghosh, S., Som, A., et al. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1462 · Cited by 10

Neutralizing antibodies to block viral entry and for identification of entry inhibitors

Tam, E.-H., Peng, Y., Cheah, M. X. Y., Yan, C., & Xiao, T. (2024) · Antiviral Research · doi:10.1016/j.antiviral.2024.105834 · Cited by 12

Deep propensity network using a sparse autoencoder for estimation of treatment effects

Ghosh, S., Bian, J., Guo, Y. M., & Prosperi, M. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa346 · Cited by 10

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

Utilising Graph Machine Learning within Drug Discovery and DevelopmentPreprint

Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R. J., Roberts, C., Tang, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.05716 · Cited by 11

Thinking on the Use of Artificial Intelligence in Drug Discovery

Wang, Y., Hu, Z., Chang, J., & Yu, B. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c00373 · Cited by 12

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Singla, R. K., & Bishayee, A. (2025) · Cancers · doi:10.3390/cancers17152507 · Cited by 12

A data-driven group retrosynthesis planning model inspired by neurosymbolic programming

Zhang, X., Lin, H., Zhang, M., Zhou, Y., & Ma, J. (2025) · Nature Communications · doi:10.1038/s41467-024-55374-9 · Cited by 11

Artificial intelligence in combating challenges in antimicrobial resistance: a narrative review

Salama, R. A. A., Kader, R. G. A., & Wadid, N. (2026) · Infection Prevention in Practice · doi:10.1016/j.infpip.2026.100522 · Cited by 12

Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space

Wang, M., Wu, Z., Wang, J., Weng, G., Kang, Y., Pan, P., Li, D., Deng, Y., Yao, X., Bing, Z., et al. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01964 · Cited by 13

An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors

Lin, T. E., Yen, D., HuangFu, W., Wu, Y.-W., Hsu, J., Yen, S., Sung, T.-Y., Hsieh, J., Pan, S., Yang, C., et al. (2024) · Protein Science · doi:10.1002/pro.5007 · Cited by 11

The role of DDR1 in cancer and the progress of its selective inhibitors

Wang, J., Wang, L., Qiang, W., & Ge, W. (2024) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2024.108018 · Cited by 12

High‐Throughput Combinatorial Metal Complex Synthesis

Welsh, A., Husbands, D. R., & Frei, A. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202420204 · Cited by 12

Machine learning and cheminformatics-based Identification of lichen-derived compounds targeting mutant PBP4R200L in Staphylococcus aureus

Mathpal, S., Joshi, T., Priyamvada, P., Ramaiah, S., & Anbarasu, A. (2025) · Molecular Diversity · doi:10.1007/s11030-025-11125-6 · Cited by 11

A structure-based framework for selective inhibitor design and optimization

Zou, Y., Guo, T., Fu, Z., Guo, Z., Bo, W., Yan, D., Wang, Q., Zeng, J., Xu, D., Wang, T., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-07840-3 · Cited by 11

Deciphering the landscape of allosteric glutaminase 1 inhibitors as anticancer agents

Vagaggini, C., D’Ursi, P., Poggialini, F., Fossa, P., Francesconi, V., Trombetti, G., Orro, A., Dreassi, E., Schenone, S., Tonelli, M., et al. (2025) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2025.108523 · Cited by 12

Artificial intelligence in drug development for delirium and Alzheimer’s disease

Ai, R., Xiao, X., Deng, S., Yang, N., Xing, X., Watne, L. O., Selbæk, G., Wedatilake, Y., Xie, C., Rubinsztein, D. C., et al. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.04.026 · Cited by 11

Toward efficient generation, correction, and properties control of unique drug‐like structures

Druchok, M., Yarish, D., Gurbych, O., & Maksymenko, M. (2021) · Journal of Computational Chemistry · doi:10.1002/jcc.26494 · Cited by 11

iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles

Han, H., Ding, C., Cheng, X., Sang, X., & Liu, T. (2021) · Molecules · doi:10.3390/molecules26092487 · Cited by 11

Machine Learning for Antimicrobial Resistance Research and Drug Development

Shankarnarayan, S. A., Guthrie, J. D., & Charlebois, D. A. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.104841 · Cited by 12

Generating and screeningde novocompounds against given targets using ultrafast deep learning models as core components

Zhang, H., Saravanan, K. M., Yang, Y., Wei, Y., Pan, Y. F., & Zhang, J. Z. H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac226 · Cited by 11

A novel DDR1 inhibitor enhances the anticancer activity of gemcitabine in pancreatic cancer.

Ko, S., Jung, K. H., Yoon, Y., Han, B. S., Park, M. S., Lee, Y. J., Kim, S. E., Cho, Y. J., Lee, P., Lim, J. H., et al. (2022) · PubMed Central · Cited by 11

Transformer-based multitask learning for reaction prediction under low-resource circumstances

Qiao, H., Wu, Y., Zhang, Y., Zhang, C., Wu, X., Wu, Z., Zhao, Q., Wang, X., Li, H., & Duan, H. (2022) · RSC Advances · doi:10.1039/d2ra05349g · Cited by 11

Exploration of Chemical Space Guided by PixelCNN for Fragment-Based De Novo Drug Discovery

Noguchi, S. F., & Inoue, J. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01345 · Cited by 11

Machine Learning for Drug Discovery and Manufacturing

Reddy, B. M. (2023) · Advanced technologies and societal change · doi:10.1007/978-981-99-0377-1_1 · Cited by 11

Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process

Krishnan, S. R., Bung, N., Srinivasan, R., & Roy, A. (2024) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2024.108734 · Cited by 11

MBLinhibitors.com, a Website Resource Offering Information and Expertise for the Continued Development of Metallo-β-Lactamase Inhibitors

Cheng, Z., Thomas, C. A., Joyner, A., Kimble, R. L., Sturgill, A. M., Tran, N.-Y., Vulcan, M., Klinsky, S. A., Orea, D., Platt, C., et al. (2020) · Biomolecules · doi:10.3390/biom10030459 · Cited by 11

Setting New Routes for Antifungal Drug Discovery Against Pathogenic Fungi

Silva, K. S. F. E., Silva, L. D. C., Gonçales, R. A., Neves, B. J., Soares, C. M. D. A., & Pereira, M. (2020) · Current Pharmaceutical Design · doi:10.2174/1381612826666200317125956 · Cited by 11

cheML.io: an online database of ML-generated molecules

Zhumagambetov, R., Kazbek, D., Shakipov, M., Maksut, D., Peshkov, V. A., & Fazli, S. (2020) · RSC Advances · doi:10.1039/d0ra07820d · Cited by 10

Metal-free synthesis of dihydrofuran derivatives as anti-vicinal amino alcohol isosteres

Nangunuri, B. G., Shirke, R. P., & Kim, M. (2023) · Organic & Biomolecular Chemistry · doi:10.1039/d2ob02077g · Cited by 11

Hyperbaric oxygen therapy decreases blood pressure in patients with chronic wounds.

Château‐Degat, M., & Belley, R. (2012) · PubMed · Cited by 11

The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal ComplexesPreprint

Balcells, D., & Skjelstad, B. B. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12894818 · Cited by 20

Diverse impacts of AI investments on productivity gains: Effects of industry and innovation characteristics

Park, G., Kang, S., Yi, S., & Kim, J. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124471 · Cited by 13

From Prompt to Drug: Toward Pharmaceutical Superintelligence

Zhavoronkov, A. A., Gennert, D. G., & Shi, J. (2026) · ACS Central Science · doi:10.1021/acscentsci.5c01473 · Cited by 15

Deep generative molecular design and its value in modern drug discovery

Özdemir, E. S., Jang, H., Keskin, Ö., Gürsoy, A., & Nussinov, R. (2026) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2026.2636192 · Cited by 10

COMA: efficient structure-constrained molecular generation using contractive and margin losses

Choi, J., Seo, S., & Park, S. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00679-y · Cited by 10

Novel Computational Methods for Cancer Drug Design

Talluri, S., Kamal, M. A., & Malla, R. R. (2023) · Current Medicinal Chemistry · doi:10.2174/0929867330666230403100008 · Cited by 10

Hierarchical reinforcement learning from imperfect demonstrations through reachable coverage-based subgoal filtering

Tang, Y. F., Guo, S., Liu, J., Wan, B., An, L., & Liu, J. K. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111736 · Cited by 10

DrugPose: benchmarking 3D generative methods for early stage drug discovery

Jocys, Z., Grundy, J., & Farrahi, K. (2024) · Digital Discovery · doi:10.1039/d4dd00076e · Cited by 12

Advancing Bioactivity Prediction Through Molecular Docking and Self-Attention

Yin, Y., Lam, H. Y. I., Mu, Y., Li, H., & Kong, A. W. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3448455 · Cited by 10

Artificial-intelligence-driven shot reduction in quantum measurement

Liang, S., Zhu, L., Liu, X., Yang, C., & Li, X. (2024) · Chemical Physics Reviews · doi:10.1063/5.0219663 · Cited by 14

Rational Design and Identification of ISM7594 as a Tissue-Agnostic FGFR2/3 Inhibitor

Wang, Y., Liu, J., Zhang, Y., Zhao, J., Wang, C. W. C., Cui, H. S., Meng, F., Aliper, A. M., Liang, T., Yan, F., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c00928 · Cited by 10

Current Challenges in the Pharmaceutical Industry and AI's Role in Addressing Them

Gauthaman, S. A., Kumar, S. S., Kamaldheen, R., Murugesan, K., Viswanathan, M. K., Devendran, H., & Muniasamy, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2043-4.ch001 · Cited by 11

AI-driven discovery of high-performance corrosion inhibitors using a BERT-GPT framework for molecular generation

Gong, H., Ma, L., Liu, D., & Zhang, D. (2025) · Corrosion Science · doi:10.1016/j.corsci.2025.113327 · Cited by 10

Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation

Schütte, A. D., Hetzel, J., Gatidis, S., Hepp, T., Dietz, B., Bauer, S., & Schwab, P. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00507-3 · Cited by 97

Discovery of Novel Dihydrolipoamide S-Succinyltransferase Inhibitors Based on Fragment Virtual Screening

Wei, C., Huang, J., Wang, Y., Chen, Y., Luo, X., Wang, S., Wu, Z., & Chen, J. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222312953 · Cited by 10

rzMLP-DTA: gMLP network with ReZero for sequence-based drug-target affinity prediction

Qiu, Z., Jiao, Q., Wang, Y., Chen, C., Zhu, D., & Cui, X. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669276 · Cited by 9

Survey on Generative Adversarial Behavior in Artificial Neural Tasks

Qamar, R., Bajao, N. A., Suwarno, I., & Jokhio, F. A. (2022) · Iraqi Journal for Computer Science and Mathematics · doi:10.52866/ijcsm.2022.02.01.009 · Cited by 11

BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data

Kwon, Y., Park, S., Lee, J., Kang, J., Lee, H. J., & Kim, W. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01300 · Cited by 10

Molecular Generation with Reduced Labeling through Constraint Architecture

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Yi, J., Lee, S., Lim, S., Cho, C., Piao, Y., Yeo, M., Kim, D., Kim, S., & Lee, S. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.08.016 · Cited by 11

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Liu, S., Li, X., Chen, C., Lin, X., Zuo, W., Peng, C. Y., Jiang, Q., Huang, W., & He, G. (2023) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2023.116100 · Cited by 11

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Teixeira, M. A. S., Neves, F., Koubâa, A., de Arruda, L. V. R., & de Oliveira, A. S. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3035355 · Cited by 10

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Korshunova, M. V., Huang, N., Capuzzi, S. J., Radchenko, D. S., Savych, O., Moroz, Y. S., Wells, C. I., Willson, T. M., Tropsha, A., & Isayev, O. (2021) · ChemRxiv · doi:10.26434/chemrxiv.14045072.v1 · Cited by 10

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Yoshimori, A., Asawa, Y., Kawasaki, E., Tasaka, T., Matsuda, S., Sekikawa, T., Tanabe, S., Neya, M., Natsugari, H., & Kanai, C. (2020) · ChemMedChem · doi:10.1002/cmdc.202000786 · Cited by 48

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Li, Y., Zhang, L., Wang, Y., Zou, J., Yang, R., Luo, X., Wu, C., Yang, W. M., Tian, C., Xu, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34692-w · Cited by 113

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