UniKP: a unified framework for the prediction of enzyme kinetic parameters

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Basic local alignment search tool

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669 · see citing works

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

SMOTE synthetic minority over-sampling technique

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works

UniProt: the Universal Protein Knowledgebase in 2023

The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1052 · Cited by 7,210 · see citing works

PubChem Substance and Compound databases

Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436 · see citing works

Supervised Machine Learning: A Review of Classification Techniques

Kotsiantis, S. B. (2007) · Informatica · Cited by 4,192 · see citing works

The class imbalance problem: A systematic study1

Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295 · see citing works

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · see citing works

Ensemble learning: A survey

Sagi, O., & Rokach, L. (2018) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1249 · Cited by 3,103 · see citing works

Class-Balanced Loss Based on Effective Number of Samples

Cui, Y., Jia, M., Lin, T.-Y., Song, Y., & Belongie, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00949 · Cited by 3,094 · see citing works

ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329 · cited 4× · see citing works

A Note on the Kinetics of Enzyme Action

Briggs, G. E., & Haldane, J. B. S. (1925) · Biochemical Journal · doi:10.1042/bj0190338 · Cited by 1,338 · see citing works

The Moderately Efficient Enzyme: Evolutionary and Physicochemical Trends Shaping Enzyme Parameters

Bar-Even, A., Noor, E., Savir, Y., Liebermeister, W., Davidi, D., Tawfik, D. S., & Milo, R. (2011) · Biochemistry · doi:10.1021/bi2002289 · Cited by 1,050 · see citing works

Despite slow catalysis and confused substrate specificity, all ribulose bisphosphate carboxylases may be nearly perfectly optimized

Tcherkez, G. G. B., Farquhar, G. D., & Andrews, T. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600605103 · Cited by 772 · see citing works

Deep learning improves antimicrobial peptide recognition

Veltri, D., Kamath, U., & Shehu, A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty179 · Cited by 610 · see citing works

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Li, F., Yuan, L., Lu, H., Li, G., Chen, Y., Engqvist, M. K. M., Kerkhoven, E. J., & Nielsen, J. (2022) · Nature Catalysis · doi:10.1038/s41929-022-00798-z · Cited by 466 · cited 8× · see citing works

BRENDA, the enzyme information system in 2011

Scheer, M., Grote, A., Chang, A., Schomburg, I., Munaretto, C., Rother, M., Sohngen, C., Stelzer, M., Thiele, J., & Schomburg, D. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1089 · Cited by 409 · see citing works

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

Currin, A., Swainston, N., Day, P. J., & Kell, D. B. (2014) · Chemical Society Reviews · doi:10.1039/c4cs00351a · Cited by 384 · see citing works

Directed evolution of enzyme catalysts

Kuchner, O., & Arnold, F. H. (1997) · Trends in biotechnology · doi:10.1016/s0167-7799(97)01138-4 · Cited by 373 · see citing works

An introduction to domain adaptation and transfer learningPreprint

Kouw, W. M., & Loog, M. (2018) · arXiv (Cornell University) · doi:10.13140/rg.2.2.33906.56004 · Cited by 338 · see citing works

Genome-wide Analysis of Substrate Specificities of the Escherichia coli Haloacid Dehalogenase-like Phosphatase Family

Kuznetsova, E., Proudfoot, M., González, C. F., Brown, G., Omelchenko, M. V., Borozan, I., Carmel, L., Wolf, Y. I., Mori, H., Savchenko, A. V., et al. (2006) · Journal of Biological Chemistry · doi:10.1074/jbc.m605449200 · Cited by 310 · see citing works

Building a global alliance of biofoundries

Hillson, N., Caddick, M., Cai, Y.-Z., Carrasco, J., Chang, M., Curach, N., Bell, D. J., Lefeuvre, R., Friedman, D., Fu, X.-F., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10079-2 · Cited by 295 · see citing works

Evolution of translation machinery in recoded bacteria enables multi-site incorporation of nonstandard amino acids

Amiram, M., Haimovich, A. D., Fan, C., Wang, Y., Aerni, H. R., Ntai, I., Moonan, D. W., Ma, N. J., Rovner, A. J., Hong, S. H., et al. (2015) · Nature Biotechnology · doi:10.1038/nbt.3372 · Cited by 283 · see citing works

SABIO-RK--database for biochemical reaction kinetics

Wittig, U., Kania, R., Golebiewski, M., Rey, M., Shi, L., Jong, L. M., Algaa, E., Weidemann, A., Sauer‐Danzwith, H., Mir, S., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1046 · Cited by 260 · see citing works

Delving into deep imbalanced regression

Yang, Y., Zha, K., Chen, Y.-C., Wang, H., & Katabi, D. (2021) · arXiv · doi:10.48550/arxiv.2102.09554 · Cited by 229 · see citing works

The BRENDA enzyme information system–From a database to an expert system

Schomburg, I., Jeske, L., Ulbrich, M., Placzek, S., Chang, A., & Schomburg, D. (2017) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2017.04.020 · Cited by 222 · see citing works

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

Heckmann, D., Lloyd, C. J., Mih, N., Ha, Y., Zielinski, D. C., Haiman, Z. B., Desouki, A. A., Lercher, M. J., & Palsson, B. O. (2018) · Nature Communications · doi:10.1038/s41467-018-07652-6 · Cited by 218 · see citing works

Highly Active and Specific Tyrosine Ammonia-Lyases from Diverse Origins Enable Enhanced Production of Aromatic Compounds in Bacteria and Saccharomyces cerevisiae

Jendresen, C. B., Stahlhut, S. G., Li, M., Gaspar, P., Siedler, S., Förster, J., Maury, J., Borodina, I., & Nielsen, A. T. (2015) · Applied and Environmental Microbiology · doi:10.1128/aem.00405-15 · Cited by 200 · see citing works

Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters

Adadi, R., Volkmer, B., Milo, R., Heinemann, M., & Shlomi, T. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002575 · Cited by 179 · see citing works

An approach for classification of highly imbalanced data using weighting and undersampling

Anand, A., Pugalenthi, G., Fogel, G. B., & Suganthan, P. N. (2010) · Amino Acids · doi:10.1007/s00726-010-0595-2 · Cited by 172 · see citing works

Production of p-hydroxycinnamic acid from glucose in Saccharomyces cerevisiae and Escherichia coli by expression of heterologous genes from plants and fungi

Vannelli, T., Qi, W., Sweigard, J. A., Gatenby, A. A., & Sariaslani, F. S. (2006) · Metabolic Engineering · doi:10.1016/j.ymben.2006.11.001 · Cited by 168 · see citing works

Anticancer peptides prediction with deep representation learning features

Lv, Z., Cui, F., Zou, Q., Zhang, L., & Xu, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab008 · Cited by 145 · see citing works

Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks

Zhang, Y., Wei, X.-S., Zhou, B., & Wu, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16458 · Cited by 139 · see citing works

Deep learning allows genome-scale prediction of Michaelis constants from structural features

Kroll, A., Engqvist, M. K. M., Heckmann, D., & Lercher, M. J. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001402 · Cited by 132 · see citing works

Metabolic Models of Protein Allocation Call for the Kinetome

Nilsson, A., Nielsen, J., & Palsson, B. O. (2017) · Cell Systems · doi:10.1016/j.cels.2017.11.013 · Cited by 87 · see citing works

Identification of Protein Subcellular Localization With Network and Functional Embeddings

Pan, X., Li, H., Zeng, T., Li, Z., Chen, L., Huang, T., & Cai, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.626500 · Cited by 63 · see citing works

Underground metabolism: network-level perspective and biotechnological potential

Notebaart, R. A., Kintses, B., Feist, A. M., & Papp, B. (2017) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.07.015 · Cited by 62 · see citing works

Structural Flexibility Modulates the Activity of Human Glutathione Transferase P1-1

Caccuri, A. M., Ascenzi, P., Antonini, G., Parker, M. W., Oakley, A. J., Chiessi, E., Nuccetelli, M., Battistoni, A., Bellizia, A., & Ricci, G. (1996) · Journal of Biological Chemistry · doi:10.1074/jbc.271.27.16193 · Cited by 58 · see citing works

Plant uptake of NaCl in relation to enzyme kinetics and toxic effects

Trapp, S., Feifičová, D., Rasmussen, N. F., & Bauer‐Gottwein, P. (2008) · Environmental and Experimental Botany · doi:10.1016/j.envexpbot.2008.05.001 · Cited by 52 · see citing works

Functional expression in Escherichia coli of the tyrosine-inducible tyrosine ammonia-lyase enzyme from yeast Trichosporon cutaneum for production of p-hydroxycinnamic acid

Vannelli, T., Xue, Z., Breinig, S., Qi, W. W., & Sariaslani, F. S. (2007) · Enzyme and Microbial Technology · doi:10.1016/j.enzmictec.2007.03.013 · Cited by 51 · see citing works

DLKcat cannot predict meaningful kcat values for mutants and unfamiliar enzymes

Kroll, A., & Lercher, M. J. (2024) · Biology Methods and Protocols · doi:10.1093/biomethods/bpae061 · Cited by 47 · see citing works

Characterization of mutants of a tyrosine ammonia-lyase from Rhodotorula glutinis

Zhou, S., Liu, P., Chen, J., Du, G., Li, H., & Zhou, J. (2016) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-016-7672-8 · Cited by 45 · see citing works

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

Chen, T., Wang, X., Chu, Y., Wang, Y., Jiang, M., Wei, D., & Xiong, Y. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.580382 · Cited by 42 · see citing works

Predicting plant Rubisco kinetics from RbcL sequence data using machine learning

Iqbal, W. A., Lisitsa, A., & Kapralov, M. V. (2022) · Journal of Experimental Botany · doi:10.1093/jxb/erac368 · Cited by 37 · see citing works

Discovery of Novel Tyrosine Ammonia Lyases for the Enzymatic Synthesis of p‐Coumaric Acid

Brack, Y., Sun, C., Yi, D., & Bornscheuer, U. T. (2022) · ChemBioChem · doi:10.1002/cbic.202200062 · Cited by 36 · see citing works

Prediction of enzyme kinetic parameters based on statistical learning.

Borger, S., Liebermeister, W., & Klipp, E. (2006) · PubMed · Cited by 31 · see citing works

IPPF-FE: an integrated peptide and protein function prediction framework based on fused features and ensemble models

Yu, H., & Luo, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac476 · Cited by 21 · see citing works

A Unified Geolocation Channel Model--Part I (Path Loss)

Progri, I. F. (2017) · Journal of Geolocation Geo-information and Geo-intelligence · doi:10.18610/jg3.2017.071604 · Cited by 18 · see citing works

Exploiting spatial dimensions to enable parallelized continuous directed evolution

Wei, T., Lai, W., Chen, Q., Zhang, Y., Sun, C., He, X., Zhao, G., Fu, X., & Liu, C. (2022) · Molecular Systems Biology · doi:10.15252/msb.202210934 · Cited by 8 · see citing works

Luo-SynBioLab/UniKP: UniKP: v1

Luo-SynBioLab, & Yu, H. (2023) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.10115497 · Cited by 1 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Mao, J., Zhang, H., Chen, Y., Wei, L., Liu, J., Nielsen, J., Chen, Y., & Xu, N. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108401 · Cited by 163 · see citing works

Robust enzyme discovery and engineering with deep learning using CataPro

Wang, Z.-C., Xie, D., Wu, D., Luo, X.-Z., Wang, S., Li, Y., Yang, Y.-M., Li, W.-F., & Zheng, L.-Z. (2025) · Nature Communications · doi:10.1038/s41467-025-58038-4 · Cited by 128 · see citing works

CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters

Boorla, V. S., & Maranas, C. D. (2025) · Nature Communications · doi:10.1038/s41467-025-57215-9 · Cited by 114 · see citing works

Cell factory design with advanced metabolic modelling empowered by artificial intelligence

Lu, H., Xiao, L., Liao, W., Yan, X., & Nielsen, J. (2024) · Metabolic Engineering · doi:10.1016/j.ymben.2024.07.003 · Cited by 57 · see citing works

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction

Wang, J., Yang, Z., Chen, C., Ge, Y., Wan, X., Bao, S., Ding, J., Wang, L., & Jiang, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae387 · Cited by 55 · see citing works

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures

Wang, T., Xiang, G., He, S., Su, L., Wang, Y., Yan, X., & Lu, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae409 · Cited by 57 · see citing works

AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring

Zhao, Y., Sun, T., Zhang, H., Li, W. J., Lian, C., Jiang, Y., Qu, M., Zhao, Z., Wang, Y., Sun, Y., et al. (2025) · Biosensors · doi:10.3390/bios15090565 · Cited by 53 · see citing works

DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates

Qiu, S., Zhao, S., & Yang, A. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad506 · Cited by 43 · see citing works

Paradigm of engineering recalcitrant non-model microorganism with dominant metabolic pathway as a biorefinery chassis

Yan, X., Bao, W., Wu, Y., Zhang, C., Mao, Z., Yuan, Q., Hu, Z., He, P., Peng, Q., Hu, M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54897-5 · Cited by 42 · see citing works

Strategies to increase the robustness of microbial cell factories

Xu, P., Lin, N.-Q., Zhang, Z., & Liu, J.-Z. (2024) · Advanced Biotechnology · doi:10.1007/s44307-024-00018-8 · Cited by 41 · see citing works

Perspectives on computational modeling of biological systems and the significance of the SysMod community

Puniya, B. L., Verma, M., Damiani, C., Bakr, S. H., & Dräger, A. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae090 · Cited by 41 · see citing works

Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives

Moorhoff, F., Zhang, Y., Qiu, S., Dong, W., Medina-Ortiz, D., Zhao, J. B., & Davari, M. D. (2025) · ACS Catalysis · doi:10.1021/acscatal.5c04814 · Cited by 41 · see citing works

Machine learning in predictive biocatalysis: A comparative review of methods and applications

Tripathi, N., Hérisson, J., & Faulon, J. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108698 · Cited by 36 · see citing works

EITLEM-Kinetics: A deep-learning framework for kinetic parameter prediction of mutant enzymes

Shen, X., Cui, Z., Long, J., Zhang, S., Chen, B., & Tan, T. (2024) · Chem Catalysis · doi:10.1016/j.checat.2024.101094 · Cited by 34 · see citing works

Artificial Intelligence-Driven Enzyme Engineering from Structural Prediction to De Novo Design

Shi, H., Bai, X., Tian, F., Li, Y., Li, D., Yao, L., Xue, C., & Tang, C. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.6c01781 · Cited by 31 · see citing works

Artificial Intelligence Technology Assists Enzyme Prediction and Rational Design

Wang, Y., Han, S., Wang, Y., Liang, Q., & Luo, W. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c13201 · Cited by 29 · see citing works

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

Ding, N., Yuan, Z., Ma, Z., Wu, Y., & Yin, L. (2024) · Molecules · doi:10.3390/molecules29153512 · Cited by 27 · see citing works

A novel intelligent sensing strategy: Integration of metal-doped carbon dots nanozymes and machine learning for rapid screening of biothiols in disease

Wei, M., Yang, M. J., Leng, H., & Shu, Y. (2025) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2025.138304 · Cited by 25 · see citing works

Dynamic and Static Regulation of Nicotinamide Adenine Dinucleotide Phosphate: Strategies, Challenges, and Future Directions in Metabolic Engineering

Ding, N., Yuan, Z., Sun, L., & Yin, L. (2024) · Molecules · doi:10.3390/molecules29153687 · Cited by 24 · see citing works

The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions

Toumpe, I., Choudhury, S., Vassily Hatzimanikatis, A., & Mišković, L. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00868 · Cited by 21 · see citing works

Protein constraints in genome‐scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes

Ferreira, M. A. D. M., da Silveira, W. B., & Nikoloski, Z. (2024) · Biotechnology and Bioengineering · doi:10.1002/bit.28650 · Cited by 22 · see citing works

Phosphorus-transforming microbes enhance phosphatase catalytic efficiency to alleviate phosphorus limitation under nitrogen and phosphorus additions in subtropical forest soil

Liu, S., Zhang, X., Wang, H., Kuzyakov, Y. V., Pan, J., Chen, F., Wang, F., Li, D., Tang, Y., & Ma, Z. (2025) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2025.109915 · Cited by 21 · see citing works

Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis

Vornholt, T., Stockinger, P., Mutný, M., Jeschek, M., Nestl, B. M., Oberdorfer, G., Osuna, S., Pleiss, J., Welner, D. H., Krause, A., et al. (2025) · ACS Central Science · doi:10.1021/acscentsci.5c00949 · Cited by 19 · see citing works

DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks

Wang, Y., Cheng, L., Zhang, Y., Cao, Y., & Alghazzawi, D. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf187 · Cited by 21 · see citing works

Chemo-enzymatic synthesis of bioactive compounds from traditional Chinese medicine and medicinal plants

Liu, S., Yang, S., & Su, P. (2024) · Science of Traditional Chinese Medicine · doi:10.1097/st9.0000000000000027 · Cited by 18 · see citing works

New insights in metabolism modelling to decipher plant–microbe interactions

Blonde, C., Caddeo, A., Nasser, W., Reverchon, S., Peyraud, R., & Haichar, F. E. Z. (2025) · New Phytologist · doi:10.1111/nph.70063 · Cited by 17 · see citing works

From machine learning to multimodal models: The AI revolution in enzyme engineering

Shi, Z., Xu, S., Xue, S., Chen, K., Lu, Y., Wang, F., Long, S., Tian, Y., Zhang, P., Wang, J., et al. (2025) · BioDesign Research · doi:10.1016/j.bidere.2025.100044 · Cited by 18 · see citing works

Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters

Malli, A., Vasyutyn, D., & Kim, J. R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02428 · Cited by 19 · see citing works

EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity

Li, G., Zhang, N., Dai, X., & Fan, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00920 · Cited by 16 · see citing works

Machine learning to guide the use of plasma technology for antibiotic degradation

Li, X., Jing, R., Zhong, N., Nie, X., Du, Y., Luo, J., & Huang, K. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.135787 · Cited by 16 · see citing works

Combing Directed Enzyme Evolution with Metabolic Engineering to Develop Efficient Microbial Cell Factories

Ren, Y., Celińska, E., Cai, P., & Zhou, Y. J. (2025) · Chem & Bio Engineering · doi:10.1021/cbe.5c00002 · Cited by 16 · see citing works

Machine learning for predicting protein properties: A comprehensive review

Wang, Y., Zhang, Y., Zhan, X., He, Y., Yang, Y., Cheng, L., & Alghazzawi, D. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128103 · Cited by 15 · see citing works

PreTKcat: A pre-trained representation learning and machine learning framework for predicting enzyme turnover number

Cai, Y., Zhang, W., Dou, Z., Wang, C., Yu, W., & Wang, L. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108327 · Cited by 15 · see citing works

Computational biology predicts metabolic engineering targets for increased production of 103 valuable chemicals in yeast

Domenzain, I., Lu, Y. P., Wang, H., Shi, J., Lu, H., & Nielsen, J. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2417322122 · Cited by 20 · see citing works

Robust Prediction of Enzyme Variant Kinetics with RealKcatPreprint

Sajeevan, K. A., Osinuga, A., Arunraj, B., Ferdous, S., Shahreen, N., Noor, M., Koneru, S., Santa-Correa, L. M., Salehi, R., Chowdhury, N. B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.10.637555 · Cited by 14 · see citing works

Development of a high-efficiency N-acetylneuraminic acid production platform through multi-pathway synergistic engineering

Ma, G., Jiang, X., Yang, B., Li, L., Liu, R., Meng, Q., Li, J., Xie, L., Guo, H., Liu, S., et al. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.07.016 · Cited by 13 · see citing works

Encapsulation of Immobilized β-Glucosidase with Calcium Metal–Organic Frameworks for Enhanced Stability in Hydrolysis of Cellobiose

Xu, L., Liu, H., Wang, X., Li, Q., Xu, S., Sun, C., & Suo, H. (2024) · Langmuir · doi:10.1021/acs.langmuir.4c02436 · Cited by 13 · see citing works

Unveiling Highly Active and Stable l -Glutaminase through Ancestral Sequence Reconstruction and Turnover Number Prediction

Qiu, W., Yang, P., Ye, J., Zhou, J., & Liu, S. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c11502 · Cited by 13 · see citing works

NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling

Zhai, J., Qi, X., Cai, L., Liu, Y., Tang, H., Xie, L., & Wang, J. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf212 · Cited by 12 · see citing works

Production of chemicals by metabolically engineered Escherichia coli

Jang, G., Kim, M.-J., & Lee, S. Y. (2025) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2025.103367 · Cited by 12 · see citing works

Engineering amino acid-derived malonyl-CoA pathways to boost polyketide production in Yarrowia lipolytica

Wang, J., Hong, Y., Wu, Z., Fishman, A., & Xu, P. (2025) · Metabolic Engineering · doi:10.1016/j.ymben.2025.11.015 · Cited by 12 · see citing works

Deep Learning-Based Prediction of Enzyme Optimal pH and Design of Point Mutations to Improve Acid Resistance

Qiu, S., Wang, N.-K., Lu, Y., Gong, J., Shi, J., & Yang, A. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00679 · Cited by 12 · see citing works

Advances in Zero‐Shot Prediction‐Guided Enzyme Engineering Using Machine Learning

Liu, C., Wu, J., Chen, Y., Liu, Y., Zheng, Y., Liu, L., & Zhao, J. B. (2024) · ChemCatChem · doi:10.1002/cctc.202401542 · Cited by 12 · see citing works

Rational Engineering of Self-Sufficient P450s to Boost Catalytic Efficiency of Carbene-Mediated C–S Bond Formation

Wang, B., Zhou, T., Shen, Y. C., Hu, W. W., Zhou, J., Tang, J., Han, R., Xu, G., Schwaneberg, U., Wang, B., et al. (2025) · ACS Catalysis · doi:10.1021/acscatal.5c01569 · Cited by 13 · see citing works

Recent advances and future trends for protein–small molecule interaction predictions with protein language models

Kroll, A., & Rousset, Y. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103070 · Cited by 12 · see citing works

Push–Pull Electronic Effect and D‐Band Center Bi‐Modulated Bio‐Heterojunction Enzyme Enables All‐Stage Infected Wound Healing

He, M., Wang, Q., Lin, Z., Sun, D., Yin, G., Deng, Y., & Yang, W. (2025) · Advanced Materials · doi:10.1002/adma.202510161 · Cited by 12 · see citing works

Rubisco is slow across the tree of life

de Pins, B., Malbranke, C., Jabłońska, J., Shmuel, A., Sharon, I., Bitbol, A., Mueller‐Cajar, O., Noor, Ε., & Milo, R. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2501433122 · Cited by 12 · see citing works

Accelerating Enzyme Engineering With Artificial Intelligence in Biocatalysis: Challenges and Opportunities

Lu, X., Cao, M., Ma, M., Wu, Y., Qu, M., Du, F., Ji, R., Duan, M., Dong, L., Liu, K., et al. (2025) · Food Bioengineering · doi:10.1002/fbe2.70029 · Cited by 12 · see citing works

Enzyme property prediction using artificial intelligence

Yuan, L., Shafaei, S., & Zhao, H. (2025) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2025.101208 · Cited by 13 · see citing works

Advances in the Modification of Enzymatic Properties Based on Protein Engineering Strategies

Huang, Y., Wen, L., & Yang, B. K. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c13434 · Cited by 11 · see citing works

Natural Enzymes and Nanozymes: A Comparative Review of Structure, Mechanism and Kinetics

Sharma, M. V., Ibeyaima, A., Sharmah, B., Afzal, N. U., & Manna, P. (2025) · Topics in Catalysis · doi:10.1007/s11244-025-02201-0 · Cited by 10 · see citing works

Machine learning for enzyme catalytic activity: current progress and future horizons

Qiu, S., Saeed, H., Leonard, W., Li, F., & Yang, A. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag002 · Cited by 13 · see citing works

Cutting-edge plant natural product pathway elucidation

Han, J.-N., Miller, E. P., & Li, S. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103137 · Cited by 10 · see citing works

Discovery of alkaline laccases from basidiomycete fungi through machine learning-based approach

Wan, X., Shahrear, S., Chew, S. W., Vilaplana, F., & Mäkelä, M. (2024) · Biotechnology for Biofuels and Bioproducts · doi:10.1186/s13068-024-02566-6 · Cited by 10 · see citing works

Overcoming bottlenecks towards complete biocatalytic conversions and complete product recovery

Wohlgemuth, R. (2024) · Reaction Chemistry & Engineering · doi:10.1039/d4re00349g · Cited by 10 · see citing works

Discovery of highly active kynureninases for cancer immunotherapy through protein language model

Eom, H., Park, S., Cho, K. S., Lee, J., Kim, H., Kim, S., Yang, J., Han, Y.-H., Lee, J., Seok, C., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1245 · Cited by 10 · see citing works

Modern machine learning methods for protein property prediction.

Dosajh, A., Agrawal, P., Chatterjee, P., & Priyakumar, U. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.102990 · Cited by 7 · see citing works

Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis

Chen, A., Peng, X., Shen, T., Zheng, L., Wu, D., & Wang, S. (2025) · mLife · doi:10.1002/mlf2.70009 · Cited by 10 · see citing works

Biochemical evaluation of molecular parts for flavonoid production using plant synthetic biology

Lee, H., Lee, S. B., Park, S., Song, J., & Kim, B. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1528122 · Cited by 10 · see citing works

Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review

Kong, D., Qian, J., Gao, C., Wang, Y., Shi, T., & Ye, C. (2025) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-025-05260-x · Cited by 11 · see citing works

Recent advances in microbial synthesis of polyphenols

Hong, Y., Lertphadungkit, P., Lv, Y., & Xu, P. (2025) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2025.103308 · Cited by 9 · see citing works

Mapping multi-substrate specificity of Arabidopsis aminotransferases

Koper, K., de Oliveira, M. V. V., Huß, S., Hataya, S., Babadi, F. S., Hawkins, C. L., Rhee, S. Y., Takasuka, T. E., Nikoloski, Z., & Maeda, H. A. (2025) · Nature Plants · doi:10.1038/s41477-025-02095-6 · Cited by 9 · see citing works

Fine-tuning of conditional Transformers improves in silico enzyme prediction and generation

Nicolini, M., Saitto, E., Franco, R. E. J., Cavalleri, E., Galeano, A., Malchiodi, D., Paccanaro, A., Robinson, P. N., Casiraghi, E., & Valentini, G. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.037 · Cited by 8 · see citing works

Metabolic modeling of host-microbe interactions

Srinak, N., Krüger, F., Kaleta, C., & Taubenheim, J. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.10.006 · Cited by 8 · see citing works

Open Enzyme Database: a community-wide repository for sharing enzyme data

Yuan, L., Bianchi, D. M., Arneson, K., Guo, B., Lambert, S., Stephens, C., Wasnik, Y., Pond, C., Berry, M. J., & Zhao, H. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1082 · Cited by 8 · see citing works

Semi-rational design in simultaneous improvement of thermostability and activity of β-1,3-glucanase from Alkalihalobacillus clausii KSM K16

Zhang, Y., Zhang, T., & Miao, M. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.137779 · Cited by 8 · see citing works

Enzyme Co-Scientist: Harnessing Large Language Models for Enzyme Kinetic Data Extraction from LiteraturePreprint

Jiang, J., Hu, J., Xie, S., Guo, M.-H., Dong, Y., Fu, S., Jiang, X., Yue, Z., Shi, J., Zhang, X., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.03.641178 · Cited by 8 · see citing works

A structure-oriented kinetics dataset of enzyme-substrate interactions

Krishnan, S. R., Pandey, N., Srinivasan, R., & Roy, A. (2025) · Scientific Data · doi:10.1038/s41597-025-05829-5 · Cited by 8 · see citing works

Metal ion of metal-organic frameworks facilitating immobilized chitinase performance

Xing, A., Xue, C., Mao, X., & Hu, Y. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.141972 · Cited by 7 · see citing works

UniESA: a unified data-driven framework for enzyme stereoselectivity and activity prediction

Weng, C., Li, J., Chen, Q., Han, J., Dong, Z.-T., Liu, Z., & Zheng, Y. (2025) · Green Chemistry · doi:10.1039/d4gc06253a · Cited by 7 · see citing works

Metabolic modelling: Insights into the machine room of plant metabolism

Machado, T. M., Töpfer, N., & Soltani, F. (2025) · Journal of Plant Physiology · doi:10.1016/j.jplph.2025.154584 · Cited by 7 · see citing works

GotEnzymes2: expanding coverage of enzyme kinetics and thermal properties

Lyu, B., Wu, K., Huang, Y., Anton, M., Li, X., Viknander, S., Anwer, D., Yang, Y., Lu, D., Kerkhoven, E. J., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1053 · Cited by 8 · see citing works

AI-driven food bioprocessing: How far its integration with practice?

Wang, Q., Tan, Y., Li, W., Zeng, H., & Wang, Y. (2026) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2026.105662 · Cited by 8 · see citing works

Efficient Synthesis of γ-Aminobutyric Acid from Monosodium Glutamate Using an Engineered Glutamate Decarboxylase Active at a Neutral pH

Miao, L., Zheng, Y., Cheng, R., Liu, J., Zheng, Z., Yang, H. Y., & Zhao, J. (2024) · Catalysts · doi:10.3390/catal14120905 · Cited by 6 · see citing works

IECata: interpretable bilinear attention network and evidential deep learning improve the catalytic efficiency prediction of enzymes

Wang, J., Zhao, Y., Yang, Z., Ge, Y., Han, P., Liu, J., Chen, C., Zan, P., Wan, X., Bo, X., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf283 · Cited by 6 · see citing works

Enhancing Lactulose Production by An Engineered Escherichia coli via Enzymatic Modification and Compartmentalization

Mutailifu, M., Lin, N.-Q., Liu, F., & Liu, J.-Z. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c04270 · Cited by 6 · see citing works

Engineered ω-Transaminase Enables Remote Stereocontrol in the Synthesis of Chiral Pharmaceutical N-Heterocyclic Amines with Nonadjacent Stereocenters

Zhang, Y., Wang, X., Yin, X., Huang, T., & Lin, S. (2025) · ACS Catalysis · doi:10.1021/acscatal.5c07044 · Cited by 6 · see citing works

A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions

Li, Y., & Wang, K. (2025) · Applied Sciences · doi:10.3390/app15074017 · Cited by 6 · see citing works

Advancing Enzyme Optimal pH Prediction via Retrieved Embedding Data Augmentation

Zhang, Z., Wei, Z., Qin, Z., Wang, L., Gong, J., Shi, J., Wu, J., & Deng, Z. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00526 · Cited by 6 · see citing works

MPEK: a multi-task learning based on pre-trained language model for predicting enzymatic reaction kinetic parametersPreprint

Jiang, H., Wang, J., Yang, Z., Chen, C., Ge, Y., Bao, S., Wan, X., Ding, J., & Wang, L. (2024) · Research Square · doi:10.21203/rs.3.rs-3916417/v1 · Cited by 6 · see citing works

Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes

Razaghi‐Moghadam, Z., Babadi, F. S., & Nikoloski, Z. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012576 · Cited by 5 · see citing works

Evolution of Vitamin E Production: From Chemical Synthesis and Plant Extraction to Microbial Cell Factories

Yang, H. J., Yu, H., & Ye, L. (2024) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c08813 · Cited by 5 · see citing works

Systematically exploring yeast metabolism through retrobiosynthesis and deep learning

Wu, K., Liu, H., Zhou, R., Sun, M., Mao, R., Jiang, Y., Kerkhoven, E. J., Chen, Y., Nielsen, J., Tang, H., et al. (2026) · Nature Catalysis · doi:10.1038/s41929-026-01523-w · Cited by 5 · see citing works

ThermoFinder: A sequence-based thermophilic proteins prediction framework

Yu, H., & Luo, X. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.132469 · Cited by 5 · see citing works

MetaEnzyme: Meta Pan-Enzyme Learning for Task-Adaptive Redesign

Zheng, J., Zhang, H., Xu, Q., Zeng, A., & Li, S. Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680883 · Cited by 5 · see citing works

A novel interpretability framework for enzyme turnover number prediction boosted by pre-trained enzyme embeddings and adaptive gate network

Du, B.-X., Yu, H., Zhu, B., Long, Y., Wu, M., & Shi, J. (2025) · Methods · doi:10.1016/j.ymeth.2025.02.010 · Cited by 5 · see citing works

A multimodal deep learning framework for enzyme turnover prediction with missing modality

Sun, X., Wang, Y. G., & Shen, Y. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110348 · Cited by 5 · see citing works

A Highly Efficient and Biocompatible Cupper‐Based Single‐Atom Nanocatalyst with Unsaturated Coordination Structure for Efficient Diabetic Wound Healing

Xu, S., Yin, Y., Pan, Y., Zhang, H., Yin, S., Zhang, X., Zhang, Y., Dai, S., Shen, H., Li, Y., et al. (2025) · Small · doi:10.1002/smll.202502896 · Cited by 5 · see citing works

A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-Dependent kcat/Km Prediction in β-Glucosidases

Erkanli, M. E., Jang, Y., Malli, A., El‐Halabi, K., Ryu, C., & Kim, J. R. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00257 · Cited by 5 · see citing works

A geometric deep learning framework for genome-wide prediction of enzyme turnover number

Pan, T., Cui, X., Koh, H. Y., Bi, Y., Wang, X., Zhang, Y., Hu, S., Webb, G. I., Kurgan, L., Zhang, G., et al. (2026) · Genome biology · doi:10.1186/s13059-026-03986-3 · Cited by 8 · see citing works

Computational Bioprospecting of Enzymes

Xiang, R., Martinez-Sugrañes, M., Muñoz‐Tafalla, R., Floor, M., & Guallar, V. (2025) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70037 · Cited by 5 · see citing works

EvoZymePro-Cat: A Protein–Ligand-Aware Deep Learning Framework for Predicting Mutation Effects in Enzyme Function

Xu, R., Li, X., Sui, J., Wu, L., Ling, C., Zheng, L., & Guo, J. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00755 · Cited by 4 · see citing works

Data engineering for AI applications in energetic materials

Zang, X., Feng, Q., Zhu, P., Ye, Y., & Shen, R. (2025) · FirePhysChem · doi:10.1016/j.fpc.2025.11.010 · Cited by 4 · see citing works

AI-enabled protein design facilitates future plant research and crop breeding

Lou, Y., Wu, T., Xia, F., Zhao, A., & Wang, X. (2026) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiag147 · Cited by 4 · see citing works

DeepSub: Utilizing Deep Learning for Predicting the Number of Subunits in Homo-Oligomeric Protein Complexes

Deng, R., Wu, K., Lin, J., Wang, D., Huang, Y., Li, Y., Shi, Z., Zhang, Z., Wang, Z., Mao, Z., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25094803 · Cited by 4 · see citing works

SELFprot: Effective and Efficient Multitask Finetuning Methods for Protein Parameter Prediction

Wilson, M. O., Coudrat, T., & Warden, A. C. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02230 · Cited by 4 · see citing works

Rules, hypergraphs, and probabilities: The three-level analysis of chemical reaction systems and other stochastic stoichiometric population processes

Smith, E., Smith, H. B., & Andersen, J. L. (2024) · PLOS complex systems. · doi:10.1371/journal.pcsy.0000022 · Cited by 4 · see citing works

CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters k cat , K m and K iPreprint

Boorla, V. S., & Maranas, C. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.10.584340 · Cited by 4 · see citing works

Engineering a two-enzyme system in Mycolicibacterium neoaurum for efficient biotransformation of phytosterols to dihydrotestosterone

Su, Z., Shi, C., Fan, Y., Gao, Y., Wang, L., Ren, X., Shen, Y., & Wang, M. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.138443 · Cited by 3 · see citing works

Biosynthesis of 10-Hydroxy-2-Decenoic Acid in Escherichia coli

He, D., Chen, Y., Shen, J., Han, Y., Keasling, J. D., & Luo, X. (2025) · Metabolic Engineering · doi:10.1016/j.ymben.2025.01.006 · Cited by 3 · see citing works

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction

Wang, J., Yang, Z., Chen, C., Ge, Y., Wan, X., Bao, S., Ding, J., Wang, L., & Jiang, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae387 · Cited by 55 · see citing works

MPEK: a multi-task learning based on pre-trained language model for predicting enzymatic reaction kinetic parametersPreprint

Jiang, H., Wang, J., Yang, Z., Chen, C., Ge, Y., Bao, S., Wan, X., Ding, J., & Wang, L. (2024) · Research Square · doi:10.21203/rs.3.rs-3916417/v1 · Cited by 6 · see citing works

TCNeKP: A Novel Deep Learning Architecture for Enzyme Catalytic Activity Prediction

Lei, Y., Liu, R., Yu, H., Xu, W., Long, T., & Hu, M. Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01830 · Cited by 2 · see citing works

EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic ConstantsPreprint

Khan, A. A., Fahad, M. S., Priyanka, Chandra, R., & Singh, G. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2512.00379

Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational AdaptationPreprint

Wang, F., Zheng, X., Li, K., Wei, Y., Liu, Y., Hu, G., Bao, T., & Yang, J. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2603.12845