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

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works

XGBoost: A Scalable Tree Boosting System

Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works

SciPy 1.0: fundamental algorithms for scientific computing in Python

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412 · see citing works

KEGG: Kyoto Encyclopedia of Genes and Genomes

Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works

Untitled work

(2014) · Cited by 33,419 · see citing works

KEGG: Kyoto Encyclopedia of Genes and Genomes

Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works

SciPy 1.0: fundamental algorithms for scientific computing in Python

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460 · see citing works

Double-slit photoelectron interference in strong-field ionization of the neon dimer

Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-07882-8 · Cited by 8,162 · see citing works

Extended-Connectivity Fingerprints

Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works

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

Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436 · see citing works

Graph Neural Networks: A Review of Methods and Applications

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2018) · arXiv · doi:10.48550/arxiv.1812.08434 · Cited by 6,880 · see citing works

Graph neural networks: A review of methods and applications

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880 · see citing works

UniProt: the universal protein knowledgebase in 2021

The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1100 · Cited by 6,372 · see citing works

Neural Message Passing for Quantum ChemistryPreprint

Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,414 · see citing works

ADADELTA: An Adaptive Learning Rate Method

Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322 · see citing works

Rapid and sensitive protein similarity searches

Lipman, D. J., & Pearson, W. R. (1985) · Science · doi:10.1126/science.2983426 · Cited by 4,033 · see citing works

PubChem 2019 update: improved access to chemical data

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1033 · Cited by 3,249 · see citing works

The Pfam protein families database

Bateman, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh121 · Cited by 3,127 · see citing works

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628 · see citing works

Analyzing Learned Molecular Representations for Property Prediction

Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00237 · Cited by 1,923 · see citing works

Reoptimization of MDL Keys for Use in Drug Discovery

Durant, J. L., Leland, B. A., Henry, D. R., & Nourse, J. G. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010132r · Cited by 1,886 · 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

Molecular graph convolutions: moving beyond fingerprints

Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631 · see citing works

Unified rational protein engineering with sequence-based deep representation learning

Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131 · cited 19× · 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

QuickGO: a web-based tool for gene ontology searching

Binns, D., Dimmer, E., Huntley, R., Barrell, D., O'Donovan, C., & Apweiler, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp536 · Cited by 971 · see citing works

K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

Jiménez-Luna, J., Škalič, M., Martínez-Rosell, G., & Fabritiis, G. D. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00650 · Cited by 923 · see citing works

Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited

Dalby, A., Nourse, J. G., Hounshell, W. D., Gushurst, A. K. I., Grier, D., Leland, B., & Laufer, J. (1992) · Journal of chemical information and computer sciences · doi:10.1021/ci00007a012 · Cited by 600 · see citing works

Differential influences of environment and self-motion on place and grid cell firing

Chen, G., Lü, Y., King, J. A., Cacucci, F., & Burgess, N. (2019) · Nature Communications · doi:10.1038/s41467-019-08550-1 · Cited by 592 · see citing works

On Two Novel Parameters for Validation of Predictive QSAR Models

Roy, P. P., Paul, S., Mitra, I., & Roy, K. (2009) · Molecules · doi:10.3390/molecules14051660 · Cited by 570 · see citing works

Toward more realistic drug-target interaction predictions

Pahikkala, T., Airola, A., Pietilä, S., Shakyawar, S. K., Szwajda, A., Tang, J. P., & Aittokallio, T. A. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu010 · Cited by 564 · see citing works

iML1515, a knowledgebase that computes Escherichia coli traits

Monk, J. M., Lloyd, C. J., Brunk, E., Mih, N., Sastry, A. V., King, Z. A., Takeuchi, R., Nomura, W., Zhang, Z., Mori, H., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3956 · Cited by 542 · 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

Discriminative Embeddings of Latent Variable Models for Structured DataPreprint

Dai, H., Dai, B., & Song, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.05629 · Cited by 534 · see citing works

SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines

He, T., Heidemeyer, M., Ban, F., Cherkasov, A. R., & Ester, M. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0209-z · Cited by 516 · see citing works

Some case studies on application of “ r m 2 ” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data

Roy, K., Chakraborty, P., Mitra, I., Ojha, P. K., Kar, S., & Das, R. N. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23231 · Cited by 468 · see citing works

BRENDA in 2019: a European ELIXIR core data resource

Jeske, L., Placzek, S., Schomburg, I., Chang, A., & Schomburg, D. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1048 · Cited by 385 · see citing works

A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

Lu, H., Li, F., Sánchez, B. J., Zhu, Z., Li, G., Domenzain, I., Marcišauskas, S., Anton, P. M., Lappa, D., Lieven, C., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11581-3 · Cited by 344 · see citing works

DEEPre: sequence-based enzyme EC number prediction by deep learning

Li, Y., Wang, S., Umarov, R. K., Xie, B., Fan, M., Li, L., & Gao, X. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx680 · Cited by 291 · see citing works

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro <i>k</i> _cat measurements

Davidi, D., Noor, E., Liebermeister, W., Bar-Even, A., Flamholz, A., Tummler, K., Barenholz, U., Goldenfeld, M., Shlomi, T., & Milo, R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1514240113 · Cited by 266 · 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

BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

Norsigian, C. J., Pusarla, N., McConn, J. L., Yurkovich, J. T., Dräger, A., Palsson, B. Ø., & King, Z. A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1054 · Cited by 250 · see citing works

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

Khodayari, A., & Maranas, C. D. (2016) · Nature Communications · doi:10.1038/ncomms13806 · Cited by 230 · 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

MBROLE 2.0—functional enrichment of chemical compounds

López-Ibáñez, J., Pazos, F., & Chagoyen, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw253 · Cited by 210 · see citing works

The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes

Furnham, N., Holliday, G. L., de Beer, T., Jacobsen, J. O. B., Pearson, W. R., & Thornton, J. M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1243 · Cited by 181 · see citing works

Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks

Saa, P. A., & Nielsen, L. K. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.09.005 · Cited by 166 · see citing works

WideDTA: prediction of drug-target binding affinityPreprint

Öztürk, H., Özkırımlı, E., & Özgür, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04166 · 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

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

Metabolic kinetic modeling provides insight into complex biological questions, but hurdles remain

Strutz, J., Martin, J. P., Greene, J. L., Broadbelt, L. J., & Tyo, K. E. J. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.02.005 · Cited by 62 · 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

Predicting Km values of beta-glucosidases using cellobiose as substrate

Yan, S., Shi, D., Nong, H., & Wu, G. (2012) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-012-0115-z · Cited by 21 · see citing works

Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates

Heckmann, D., Zielinski, D. C., & Palsson, B. Ø. (2018) · Nature Communications · doi:10.1038/s41467-018-07649-1 · Cited by 20 · see citing works

Costless Performance Improvement in Machine Learning for Graph-Based Molecular Analysis

Na, G. S., Kim, H. W., & Chang, H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00816 · Cited by 17 · 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 · see citing works

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

Yu, H., Deng, H., He, J., Keasling, J. D., & Luo, X. (2023) · Nature Communications · doi:10.1038/s41467-023-44113-1 · Cited by 226 · see citing works

A general model to predict small molecule substrates of enzymes based on machine and deep learning

Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-38347-2 · Cited by 193 · see citing works

Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning

Kroll, A., Rousset, Y., Hu, X.-P., Liebrand, N. A., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-39840-4 · Cited by 186 · see citing works

Navigating the landscape of enzyme design: from molecular simulations to machine learning.

Zhou, J., & Huang, M.-L. (2024) · Chemical Society Reviews · doi:10.1039/d4cs00196f · Cited by 106 · see citing works

Machine learning-enabled retrobiosynthesis of molecules

Yu, T., Boob, A. G., Volk, M., Liu, X., Cui, H., & Zhao, H. (2023) · Nature Catalysis · doi:10.1038/s41929-022-00909-w · Cited by 120 · see citing works

Functional annotation of enzyme-encoding genes using deep learning with transformer layers

Kim, G. B., Kim, J. Y., Lee, J.-A., Norsigian, C. J., Palsson, B., & Lee, S. Y. (2023) · Nature Communications · doi:10.1038/s41467-023-43216-z · Cited by 115 · 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

GotEnzymes: an extensive database of enzyme parameter predictions

Li, F., Chen, Y., Anton, M., & Nielsen, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac831 · Cited by 71 · 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

Artificial intelligence for life sciences: A comprehensive guide and future trends

Luo, M., Yang, W., Bai, L., Zhang, L., Huang, J., Cao, Y.-H., Xie, Y., Tong, L., Zhang, H., Lei, Y., et al. (2024) · The Innovation Life · doi:10.59717/j.xinn-life.2024.100105 · Cited by 50 · see citing works

Deep Learning from Phylogenies for Diversification Analyses

Lambert, S., Voznica, J., & Morlon, H. (2023) · Systematic Biology · doi:10.1093/sysbio/syad044 · Cited by 46 · see citing works

Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review

Nuñez, L. F. S., Barrera-Ocampo, A. A., Caicedo, P. A., Cortés, N., Osorio, E. H., Villegas-Torres, M. F., & Barrios, A. F. G. (2024) · Metabolites · doi:10.3390/metabo14030154 · Cited by 45 · 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

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

Machine Learning: A Suitable Method for Biocatalysis

Sampaio, P., & Fernandes, P. C. D. B. (2023) · Catalysts · doi:10.3390/catal13060961 · Cited by 39 · see citing works

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships

Kroll, A., Ranjan, S., & Lercher, M. J. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012100 · Cited by 36 · 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 35 · see citing works

Machine learning applications in precision medicine: Overcoming challenges and unlocking potential

Nilius, H., Tsouka, S., Nagler, M., & Masoodi, M. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117872 · Cited by 36 · see citing works

Model-driven insights into the effects of temperature on metabolism

Wendering, P., & Nikoloski, Z. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108203 · Cited by 33 · see citing works

Enzyme Databases in the Era of Omics and Artificial Intelligence

Prešern, U., & Goličnik, M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242316918 · Cited by 31 · see citing works

Machine learning predicts system-wide metabolic flux control in cyanobacteria

Kugler, A., & Stensjö, K. (2024) · Metabolic Engineering · doi:10.1016/j.ymben.2024.02.013 · Cited by 31 · 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

A topical review on AI-interlinked biodomain sensors for multi-purpose applications

Thapa, R., Poudel, S., Krukiewicz, K., & Kunwar, A. (2024) · Measurement · doi:10.1016/j.measurement.2024.114123 · Cited by 30 · see citing works

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Vasina, M., Kovar, D. R., Damborský, J., Ding, Y., Yang, T., deMello, A. J., Mazurenko, S., Stavrakis, S., & Prokop, Z. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108171 · Cited by 27 · see citing works

MECE: a method for enhancing the catalytic efficiency of glycoside hydrolase based on deep neural networks and molecular evolution

Liu, H., Guan, F., Liu, T., Yang, L., Fan, L., Liu, X., Luo, H., Wu, N., Yao, B., Tian, J., et al. (2023) · 中国科学通报：英文版 · doi:10.1016/j.scib.2023.09.039 · Cited by 25 · see citing works

Data-driven enzyme engineering to identify function-enhancing enzymes

Jiang, Y., Ran, X., & Yang, Z. J. (2022) · Protein Engineering Design and Selection · doi:10.1093/protein/gzac009 · 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

renz: An R package for the analysis of enzyme kinetic data

Aledo, J. C. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04729-4 · Cited by 20 · see citing works

Genetically Encoded Lizard Color Divergence for Camouflage and Thermoregulation

Sun, B., Li, W.-M., Lv, P., Wen, G., Wu, D.-Y., Tao, S., Liao, M., Yu, C., Jiang, Z., Wang, Y., et al. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae009 · Cited by 20 · see citing works

REME: an integrated platform for reaction enzyme mining and evaluation

Shi, Z., Wang, D., Li, Y., Deng, R., Lin, J., Liu, C., Li, H., Wang, R., Zhao, M., Mao, Z., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae405 · Cited by 18 · 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

DeepMolecules: a web server for predicting enzyme and transporter–small molecule interactions

Kroll, A., Rousset, Y., Spitzlei, T., & Lercher, M. J. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf343 · 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

Machine Learning Enables Prediction of Pyrrolysyl-tRNA Synthetase Substrate Specificity

Zhang, Q., Zheng, W., Song, Z., Zhang, Q., Yang, L., Wu, J., Lin, J., Xu, G., & Yu, H. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00225 · Cited by 16 · see citing works

Advances in AI-based strategies and tools to facilitate natural product and drug development

Basnet, B. B., Zhou, Z., Wei, B., & Wang, H. (2025) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2025.2478094 · Cited by 17 · see citing works

MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling

Maeda, K., Hatae, A., Sakai, Y., Boogerd, F. C., & Kurata, H. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-05009-x · Cited by 15 · 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

ENKIE: a package for predicting enzyme kinetic parameter values and their uncertainties

Gollub, M. G., Backes, T., Kaltenbach, H., & Stelling, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae652 · 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

Non-canonical plant metabolism

Sweetlove, L. J., Ratcliffe, R. G., & Fernie, A. R. (2025) · Nature Plants · doi:10.1038/s41477-025-01965-3 · Cited by 14 · see citing works

The return of metabolism: biochemistry and physiology of glycolysis

Grüning, N., Agostini, F., Caldana, C., Hartl, J., Heinemann, M., Keller, M. A., Krüsemann, J. L., Lamperti, C., Linster, C. L., Lindner, S. N., et al. (2025) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.70104 · Cited by 16 · see citing works

GraphKM: machine and deep learning for KM prediction of wildtype and mutant enzymes

He, X. D., & Yan, M. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05746-1 · Cited by 14 · see citing works

Click, Compute, Create: A Review of Web‐based Tools for Enzyme Engineering

Tripp, A., Braun, M., Wieser, F., Oberdorfer, G., & Lechner, H. (2024) · ChemBioChem · doi:10.1002/cbic.202400092 · Cited by 15 · see citing works

Mathematical properties of optimal fluxes in cellular reaction networks at balanced growth

Dourado, H., Liebermeister, W., Ebenhöh, O., & Lercher, M. J. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011156 · Cited by 14 · see citing works

Discover the maze-like network for glabridin biosynthesis

Zhang, Z., Li, W., Meng, F., Jin, Y., Sun, W., Kuang, Q., Ren, S., Liu, W., Zhang, L., Qin, L., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68881-8 · Cited by 13 · see citing works

Molecular understanding of Eubacterium limosum chemostat methanol metabolism

Wood, J. C., González-García, R. A., Daygon, V. D., Talbo, G. H., Plan, M. R., Marcellin, E., & Virdis, B. (2023) · Sustainable Energy & Fuels · doi:10.1039/d2se01551j · Cited by 12 · see citing works

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

(2022) · doi:10.1016/j.ymben.2022.09.002 · Cited by 13 · see citing works

Directed Evolution of Escherichia coli Nissle 1917 to Utilize Allulose as Sole Carbon Source

Xu, B., Liu, L., Lai, S., Chen, J., Wu, S., Lei, W., Lin, H., Zhang, Y., Hu, Y., He, J., et al. (2024) · Small Methods · doi:10.1002/smtd.202301385 · Cited by 12 · see citing works

Enzymatic conversion of glycerol to dihydroxyacetone using a hydrogel bioreactor entrapping glycerol dehydrogenase immobilized on silica nanoparticles

Oh, W. T., Ham, J., Kim, S., & Koh, W. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.159250 · Cited by 12 · 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

ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular ModelingPreprint

Zheng, K.-J., Long, S., Lu, T.-Y., Yang, J.-W., Dai, X., Zhang, M., Nie, Z.-Q., Ma, W.-Y., & Zhou, H. (2024) · bioRxiv · doi:10.1101/2024.03.04.583284 · Cited by 25 · see citing works

Graph-Aware AURALSTM: An Attentive Unified Representation Architecture with BiLSTM for Enhanced Molecular Property Prediction

Pala, M. A. (2025) · Molecular Diversity · doi:10.1007/s11030-025-11197-4 · Cited by 11 · see citing works

Engineering UDP-Glycosyltransferase UGTPg29 for the Efficient Synthesis of Ginsenoside Rg3 from Protopanaxadiol

He, H., Chen, J., Xie, J., Ding, J., Pan, H., Li, Y., & Jia, H. (2024) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-024-05009-y · Cited by 9 · see citing works

Increased yield of 2‐O‐α‐ d ‐glucopyranosyl‐ l ‐ascorbic acid synthesis by α‐glucosidase using rational design that regulating the ground state of enzyme and substrate complex

Shao-yan, Z., Zhou, W., Lin, X., Li, F., Xie, C., Liu, D., & Yao, D. (2023) · Biotechnology Journal · doi:10.1002/biot.202300122 · Cited by 9 · see citing works

Machine learning for sustainable reutilization of waste materials as energy sources – a comprehensive review

Peng, W., & Sadaghiani, O. K. (2023) · International Journal of Green Energy · doi:10.1080/15435075.2023.2255647 · Cited by 9 · see citing works

A compact model of Escherichia coli core and biosynthetic metabolism

Corrao, M., He, H., Liebermeister, W., Noor, Ε., & Bar‐Even, A. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013564 · Cited by 9 · 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

The substrate scopes of enzymes: a general prediction model based on machine and deep learningPreprint

Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.24.493213 · Cited by 8 · 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 7 · see citing works

The Arabidopsis neutral amino acid transporter UmamiT20 confers Botrytis cinerea susceptibility

Prior, M. J., Weidauer, D., Locci, F., Liao, J.-Y., Kuwata, K., Deng, C., Ye, H. B., Cai, Q., Bezrutczyk, M., Zhao, C., et al. (2025) · Journal of Experimental Botany · doi:10.1093/jxb/eraf496 · Cited by 7 · see citing works

Electrostatic potential as a reactivity scoring function in computer‐assisted enzyme engineering

Vega, A., Planas, A., & Biarnés, X. (2025) · FEBS Journal · doi:10.1111/febs.70121 · 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

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

Drug-target interaction prediction using a multi-modal transformer network demonstrates high generalizability to unseen proteinsPreprint

Kroll, A., Ranjan, S., & Lercher, M. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.21.554147 · 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

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

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

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

Control analysis in the identification of key enzymes driving metabolic adaptations: Towards drug target discovery

de Atauri, P., Foguet, C., & Cascante, M. (2023) · Biosystems · doi:10.1016/j.biosystems.2023.104984 · Cited by 5 · see citing works

AI delivers Michaelis constants as fuel for genome-scale metabolic models

Antolín, A. A., & Cascante, M. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001415 · Cited by 5 · see citing works

Influence of Modification of Cysteine Residues in the Carbohydrate-Binding Domain on Cellulase Activity: Achieving Enhanced Performance and pH-Sensitive Recovery

Zhang, H., Su, Z., Sun, Y., Li, L., & Lu, Y. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c12952 · Cited by 4 · see citing works

CACLENS: A Multitask Deep Learning System for Enzyme Discovery

Xin, Y., Tan, Y., Lin, H., Zhang, G., Tian, Y., & Wu, A. (2025) · Advanced Science · doi:10.1002/advs.202518063 · Cited by 5 · see citing works

How to enhance enzymatic reaction rates? The Sabatier principle and beyond

Chiba, Y., & Ooka, H. (2025) · Bioscience Biotechnology and Biochemistry · doi:10.1093/bbb/zbaf181 · Cited by 4 · 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

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

Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions

Kennedy, L., Harper, M., & Čuperlović-Culf, M. (2025) · Briefings Bioinform. · doi:10.1093/bib/bbaf635 · Cited by 5 · see citing works

Accurate enzyme specificity constant prediction with iESC

Zhang, Y., Liu, L.-H., Wang, S., & Jiang, A. (2026) · Bioresource Technology · doi:10.1016/j.biortech.2026.134067 · Cited by 3 · see citing works

Investigating Enzyme Biochemistry by Deep Learning: A Computational Tool for a New Era

Rayka, M., Mirzaei, M., Farnoosh, G., & Latifi, A. M. (2024) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416524400052 · Cited by 3 · see citing works

Transfer learning improves predictions of enzyme kinetic parameters

Kroll, A. (2024) · Chem Catalysis · doi:10.1016/j.checat.2024.101121 · Cited by 3 · see citing works

Predicting Taxonomic Identity and Genetic Composition of Codon Usage Bias Levels Using Deep Learning Models

Buhl, L. M., & Pattanayak, S. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020679 · Cited by 3 · see citing works

GELKcat: An Integration Learning of Substrate Graph with Enzyme Embedding for Kcat prediction

Du, B.-X., Yu, H., Zhu, B., Long, Y., Wu, M., & Shi, J. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385630 · Cited by 3 · see citing works

AI-driven parametrization of Michaelis–Menten maximal velocity: Advancing in silico new approach methodologies (NAMs)

Karakoltzidis, A., Karakitsios, S., & Sarigiannis, D. A. (2025) · NAM journal. · doi:10.1016/j.namjnl.2025.100012 · Cited by 3 · see citing works

LICEDB: light industrial core enzyme database for industrial applications and AI enzyme design

Gong, L., Liu, F., Zhang, C., Ming, Y., Mou, Y., Yuan, Z., Jiang, H., Gao, B., Lu, F., & Zhang, L. (2025) · Database · doi:10.1093/database/baaf001 · Cited by 3 · see citing works

Temperature adaptation in structure and function in lactate dehydrogenase-A reflects convergent evolution in a few key protein regions

Zhu, X.-L., Liao, M., Ma, L., Somero, G. N., & Dong, Y. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2517759122 · Cited by 2 · 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

Enzyme Catalytic Parameters and Evolution Across the Dissipation Plane

Juretić, D., & Mađarić, B. B. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27041709 · Cited by 2 · see citing works

Unlocking Enzyme Discovery: A High-Resolution Gene Cluster Database Powered by Phylogenetic Insights and Machine Learning

Zhang, S., He, J., Duan, X., Liu, Z., Lan, Z., Wang, Q., Wang, J., Liu, W., Zhai, Q., Cruz‐Morales, P., et al. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c06841 · Cited by 2 · see citing works

KinForm: kinetics-informed feature optimised representation models for enzyme kcat and KM prediction

Alwer, S., & Fleming, R. M. T. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00692-5 · Cited by 6 · see citing works

Metabolic reprogramming of myeloid cells in cancer: from lactate–NAMPT axis to AI-guided therapeutics

Lee, I.-G., & Jung, K. (2026) · Experimental & Molecular Medicine · doi:10.1038/s12276-026-01759-3 · Cited by 2 · see citing works

Modelling energy metabolism dysregulations in neuromuscular diseases: A case study of calpainopathy

Siharath, C., Biondi, O., & Pérès, S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e40918 · Cited by 1 · see citing works

Computational approach for identification and characterization of a glucose-tolerant thermostable β-glucosidase from marine metagenome

Pandey, A. K. (2025) · Molecular Diversity · doi:10.1007/s11030-025-11419-9 · Cited by 1 · see citing works

Dual-Site Cooperative Modification Strategy Empowers Cellulase: Performance Optimization and pH-Sensitive Recovery

Zhang, H., Zhou, M., Yu, S., Liu, X., Liu, Y., & Li, L. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c14178 · Cited by 1 · see citing works

Deep Learning Meets Synthetic Biology: Applications Across Molecular, Cellular, and Metabolic Scales

Zhao, Y., Yang, C., Xue, B., Liu, H., Wang, S., & Su, H. (2026) · Green Carbon · doi:10.1016/j.greenca.2025.11.003 · Cited by 2 · see citing works

KmPred: prediction of Michaelis constants (Km) using an integrative machine learning framework

Alazmi, M. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1711471 · Cited by 1 · see citing works

A standardized workflow for kinetic metabolic model curation and dissemination

Cook, M., Anastasakis, S., Heydarabadipour, A., Shin, J., Burbano, D. A., Carothers, J. M., & Sauro, H. M. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014227 · Cited by 1 · see citing works

Multidimensional Metabolic Engineering of Yarrowia lipolytica for Efficient Synthesis of Dihydrokaempferol

Liu, S., Tang, Y., Zhu, Q., Zeng, W., He, Q., Xu, S., & Zhou, J. (2026) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.5c13902 · Cited by 1 · see citing works

GraphKM: machine and deep learning for KM prediction of wildtype and mutant enzymes

He, X. D., & Yan, M. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05746-1 · Cited by 14 · see citing works

Leveraging active site information for deep learning prediction of enzyme–substrate Michaelis constants

Lepikhov, D., Sandner, L., & Nunes‐Alves, A. (2026) · Digital Discovery · doi:10.1039/d6dd00094k

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

MMISA-KM: a deep-learning method using multi-modal information and self-attention mechanisms for the prediction of Michaelis constants

Song, A., & Wang, K. (2025) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls66240.2025.11064981

KmPred: prediction of Michaelis constants (Km) using an integrative machine learning framework

Alazmi, M. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1711471 · Cited by 1 · see citing works