Advances in protein structure prediction and design

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302

The Protein Data Bank

Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196

Equation of State Calculations by Fast Computing Machines

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192

Crystallography & NMR System: A New Software Suite for Macromolecular Structure Determination

Brünger, A. T., Adams, P. D., Clore, G. M., DeLano, W. L., Gros, P., Grosse‐Kunstleve, R. W., Jiang, J., Kuszewski, J. J., Nilges, M., Pannu, N., et al. (1998) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444998003254 · Cited by 15,723

SWISS-MODEL: homology modelling of protein structures and complexes

Waterhouse, A. M., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T., Rempfer, C., Bordoli, L., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky427 · Cited by 13,248

CHARMM36m: an improved force field for folded and intrinsically disordered proteins

Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmüller, H., & MacKerell, A. D. (2016) · Nature Methods · doi:10.1038/nmeth.4067 · Cited by 7,334

Principles that Govern the Folding of Protein Chains

Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096

The I-TASSER Suite: protein structure and function prediction

Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. (2015) · Nature Methods · doi:10.1038/nmeth.3213 · Cited by 5,596

Molecular flexibility in computational protein design: an algorithmic perspective

Bouchiba, Y., Cortés, J., Schiex, T., & Barbe, S. (2021) · Protein Engineering Design and Selection · doi:10.1093/protein/gzab011 · Cited by 14

Single-molecule resolution of macromolecules with nanopore devices

Ren, M., Liu, D., Qin, F., Chen, X., Ma, W., Tian, R., Weng, T., Wang, D., Astruc, D., & Liang, L. (2025) · Advances in Colloid and Interface Science · doi:10.1016/j.cis.2025.103417 · Cited by 13

Bioconjugation Strategies for Tobacco Mild Green Mosaic Virus

González‐Gamboa, I., Caparco, A. A., McCaskill, J. M., & Steinmetz, N. F. (2022) · ChemBioChem · doi:10.1002/cbic.202200323 · Cited by 13

Creating novel metabolic pathways by protein engineering for bioproduction

Zhou, Y., Liu, Y., Sun, H., & Lu, Y. (2024) · Trends in biotechnology · doi:10.1016/j.tibtech.2024.10.017 · Cited by 13

Iterative SE(3)-Transformers

Fuchs, F. B., Wagstaff, E., Dauparas, J., & Posner, I. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80209-7_63 · Cited by 16

An investigation of alkaline phosphatase enzymatic activity after electrospinning and electrospraying

Onyekuru, L., Moreira, A., Zhang, J., Angkawinitwong, U., Costa, P. F., Brocchini, S., & Williams, G. R. (2021) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2021.102592 · Cited by 13

A Unifying Framework for Understanding Biological Structures and Functions Across Levels of Biological Organization

Herman, M. A., Aiello, B. R., DeLong, J. P., García-Ruíz, H., González, A. L., Hwang, W., McBeth, C., Stojković, E. A., Trakselis, M. A., & Yakoby, N. (2021) · Integrative and Comparative Biology · doi:10.1093/icb/icab167 · Cited by 17

Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function

Madani, M., Behzadi, M. M., Song, D., Ilieş, H. T., & Tarakanova, A. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.020 · Cited by 20

A critical address to advancements and challenges in computational strategies for structural prediction of protein in recent past

Ali, N. F., Khan, S., & Zahid, S. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108430 · Cited by 13

Dynamics of Molecular Self‐Assembly of Short Peptides at Liquid–Solid Interfaces – Effect of Charged Amino Acid Point Mutations

Yurtsever, A., Hirata, K., Kojima, R., Miyazawa, K., Miyata, K., Kesornsit, S., Zareie, H. M., Sun, L., Maeda, K., Sarıkaya, M., et al. (2024) · Small · doi:10.1002/smll.202400653 · Cited by 13

RNA‐Templated Peptide Bond Formation Promotes L‐Homochirality

Węgrzyn, E., Mejdrová, I., Müller, F., Nainytė, M., Escobar, L., & Carell, T. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202319235 · Cited by 12

Predicting therapeutic and side effects from drug binding affinities to human proteome structures

Sawada, R., Sakajiri, Y., Shibata, T., & Yamanishi, Y. (2024) · iScience · doi:10.1016/j.isci.2024.110032 · Cited by 12

Resource analysis of quantum algorithms for coarse-grained protein folding models

Linn, H., Lyngfelt, I., García-Álvarez, L., & Johansson, G. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.033112 · Cited by 16

Protein design accelerates the development and application of optogenetic tools

Zhu, L., Wang, Y., Wu, X., Wu, G., Zhang, G., Liu, C., & Zhang, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.02.014 · Cited by 13

Computational drug repurposing study of antiviral drugs against main protease, RNA polymerase, and spike proteins of SARS-CoV-2 using molecular docking method

Jalalvand, A., Khatouni, S. B., Najafi, Z. B., Fatahinia, F., Ismailzadeh, N., & Farahmand, B. (2021) · Journal of Basic and Clinical Physiology and Pharmacology · doi:10.1515/jbcpp-2020-0369 · Cited by 12

Crystal Structure Analysis of Sarcoplasmic-Calcium-Binding Protein: An Allergen in Scylla paramamosain

Chen, Y., Jin, T., Li, M., Yun, X., Huan, F., Liu, Q., Hu, M., Wei, X., Zheng, P., & Liu, G. (2023) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.2c07267 · Cited by 12

In Silico Characterization of Withania coagulans Bioactive Compounds as Potential Inhibitors of Hydroxymethylglutaryl (HMG-CoA) Reductase of Mus musculus

Azmi, M. B., Khan, F., Asif, U., Khurshid, B., Wadood, A., Qureshi, S. A., Ahmed, S. D. H., Mudassir, H. A., Sheikh, S. I., & Feroz, N. (2023) · ACS Omega · doi:10.1021/acsomega.2c07893 · Cited by 12

Plant cell wall architecture guided design of CBM3-GH11 chimeras with enhanced xylanase activity using a tandem repeat left-handed β-3-prism scaffold

Pinheiro, M. P., Reis, R. A. G., Dupree, P., & Ward, R. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.011 · Cited by 9

Chitosan-Based Oral Drug Delivery System for Peptide, Protein and Vaccine Delivery

Zainuddin, S. Z., & Hamid, K. A. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.95771 · Cited by 12

Mutational Landscape of the Proglucagon-Derived Peptides

Lindquist, P., Madsen, J. S., Bräuner‐Osborne, H., ROSENKILDE, M. M., & Hauser, A. S. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.698511 · Cited by 13

Selective Adsorption and Separation of Proteins by Ligand-Modified Nanofiber Fabric

Liu, S., & Mukai, Y. (2021) · Polymers · doi:10.3390/polym13142313 · Cited by 12

A de novo protein structure prediction by iterative partition sampling, topology adjustment and residue-level distance deviation optimization

Liu, J., Zhao, K., He, G.-X., Wang, L., Zhou, X., & Zhang, G. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab620 · Cited by 12

Inhibition of a malaria host–pathogen interaction by a computationally designed inhibitor

Tobin, A. R., Crow, R., Urusova, D. V., Klima, J. C., Tolia, N. H., & Strauch, E. (2022) · Protein Science · doi:10.1002/pro.4507 · Cited by 10

Common framework mutations impact antibody interfacial dynamics and flexibility

Rhodes, E. R., Faris, J. G., Petersen, B. M., & Sprenger, K. G. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1120582 · Cited by 11

pH-Based Molecular Dynamics Simulation for Analysing Protein Structure and Folding

Sasidharan, S., Shukla, R., Tripathi, T., & Saudagar, P. (2023) · Springer eBooks · doi:10.1007/978-981-99-2079-2_11 · Cited by 11

TransConv: convolution-infused transformer for protein secondary structure prediction

Das, S., Ghosh, S., & Jana, N. D. (2025) · Journal of Molecular Modeling · doi:10.1007/s00894-024-06259-7 · Cited by 12

A physical informed deep learning model for learning the spatiotemporal dynamics of bubbles in microchannel phase change heat transfer

Yao, Q., Sun, Z., Jin, H., Xu, Y., Gu, J., Shi, L., Li, K., Shen, X., Bie, Q., & Li, J. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.127193 · Cited by 12

CSI-LSTM: a web server to predict protein secondary structure using bidirectional long short term memory and NMR chemical shifts

Miao, Z., Wang, Q., Xiao, X., Kamal, G. M., Song, L., Zhang, X., Li, C., Zhou, X., Jiang, B., & Liu, M. (2021) · Journal of Biomolecular NMR · doi:10.1007/s10858-021-00383-9 · Cited by 13

Prediction, validation, and analysis of protein structures: A beginner’s guide

Sasidharan, S., & Saudagar, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90264-9.00023-4 · Cited by 10

Computational protein design with data‐driven approaches: Recent developments and perspectives

Liu, H., & Chen, Q. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1646 · Cited by 13

The Sequence Characteristics and Binding Properties of the Odorant-Binding Protein 2 of Euplatypus parallelus to Semiochemicals

Cui, G., Zhou, X., Wang, Q., Zhang, K., Qin, L., & Guo, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24021714 · Cited by 11

Pareto Dominance Archive and Coordinated Selection Strategy-Based Many-Objective Optimizer for Protein Structure Prediction

Zhang, Z., Gao, S., Lei, Z., Xiong, R., & Cheng, J. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3247025 · Cited by 11

Exploring the structural acrobatics of fold-switching proteins using simplified structure-based models

Retamal-Farfán, I., González-Higueras, J., Galaz‐Davison, P., Rivera, M., & Ramírez‐Sarmiento, C. A. (2023) · Biophysical Reviews · doi:10.1007/s12551-023-01087-0 · Cited by 15

Scalable Coupling of Deep Learning with Logical Reasoning

Defresne, M., Barbe, S., & Schiex, T. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/402 · Cited by 17

On interaction of proteinoids with simulated neural networks

Mougkogiannis, P., & Adamatzky, A. (2024) · Biosystems · doi:10.1016/j.biosystems.2024.105175 · Cited by 11

High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In VivoPreprint

Tang, Y. J., Shuldiner, E. G., Karmakar, S., & Winslow, M. M. (2023) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a041382 · Cited by 11

Hydrolysis in food processing and its impact on allergenicity of food

Mishra, A., Sancho, A. I., & Bøgh, K. L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-96018-2.00143-7 · Cited by 12

HalluDesign: Protein Optimization and de novo Design via Iterative Structure Hallucination and Sequence DesignPreprint

Fang, M., Wang, C., Shi, J., Lian, F., Jin, Q., Wang, Z., Zhang, Y., Chen, P., Cui, Z., Wang, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.08.686881 · Cited by 18

What Can De Novo Protein Design Bring to the Treatment of Hematological Disorders?

Lu, H. J., Cheng, Z., Hu, Y., & Tang, L. V. (2023) · Biology · doi:10.3390/biology12020166 · Cited by 10

Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation

Ferreiro, D., Branco, C., & Arenas, M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae096 · Cited by 10

From sequence to mechanobiology? Promises and challenges for AlphaFold 3

Zonta, F., & Pantano, S. (2024) · Mechanobiology in Medicine · doi:10.1016/j.mbm.2024.100083 · Cited by 11

A rapid-convergent particle swarm optimization approach for multiscale design of high-permeance seawater reverse osmosis systems

Chen, K., Luo, J. L., Chen, J., Lu, Y., & Heng, Y. (2024) · Communications Engineering · doi:10.1038/s44172-024-00289-y · Cited by 10

Structural elucidation of four fungal hydrophobins belonging to classes I and II: Results from Alphafold and accelerated molecular dynamics simulations

Agwora, D., Gufu, B., Marik, T., Papp, T., Vágvölgyi, C., Kredics, L., & Tyagi, C. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.015 · Cited by 10

New Insights into the Diagnosis and Treatment of Hepatocellular Carcinoma

Li, C., Lu, B., & Deng, B. (2025) · Biomedicines · doi:10.3390/biomedicines13051244 · Cited by 12

Breaking Evolution’s Ceiling: AI-Powered Protein Engineering

Jin, S., Wu, Q., Fu, G., Lu, D. X., Wang, F., Deng, L. F., & Nie, K. (2025) · Catalysts · doi:10.3390/catal15090842 · Cited by 11

A panoramic view of proteomics and multiomics in precision health

Zilocchi, M., Wang, C., Babu, M., & Li, J. (2021) · iScience · doi:10.1016/j.isci.2021.102925 · Cited by 10

Emerging trends in developing biosensor techniques to undertake plant phosphoproteomic analysis

Farooq, A., Bhat, K. A., Mir, R. A., Mahajan, R., Nazir, M., Sharma, V., & Zargar, S. M. (2021) · Journal of Proteomics · doi:10.1016/j.jprot.2021.104458 · Cited by 10

Personalized structural biology reveals the molecular mechanisms underlying heterogeneous epileptic phenotypes caused by de novo KCNC2 variants

Mukherjee, S., Cassini, T. A., Hu, N., Yang, T., Li, B., Shen, W., Moth, C. W., Rinker, D. C., Sheehan, J. H., Cogan, J. D., et al. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100131 · Cited by 10

The cofactor-dependent folding mechanism of Drosophila cryptochrome revealed by single-molecule pulling experiments

(2023) · doi:10.1038/s41467-023-36701-y · Cited by 10

Topological data analysis of protein structure and inter/intra-molecular interaction changes attributable to amino acid mutations

Koseki, J., Hayashi, S., Kojima, Y., Hirose, H., & Shimamura, T. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.05.009 · Cited by 10

Benchmarking protein structure predictors to assist machine learning-guided peptide discovery

Aldas-Bulos, V. D., & Plisson, F. (2023) · Digital Discovery · doi:10.1039/d3dd00045a · Cited by 10

Effects of terminal tripeptide units on mechanical properties of collagen triple helices

Masrouri, M., & Zhao Qin, A. (2023) · Extreme Mechanics Letters · doi:10.1016/j.eml.2023.102075 · Cited by 10

In Silico Structural Modeling and Binding Site Analysis of Cerebroside Sulfotransferase (CST): A Therapeutic Target for Developing Substrate Reduction Therapy for Metachromatic Leukodystrophy

Singh, N., & Singh, A. K. (2024) · ACS Omega · doi:10.1021/acsomega.3c09462 · Cited by 10

Bilinear optimization of protein structure prediction: An exact approach via AB off-lattice model

Aquino, L. S., Seman, L. O., Camponogara, E., Mariani, V. C., & Coelho, L. D. S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108558 · Cited by 10

FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution

Feng, J., & Shukla, D. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.9b11869 · Cited by 9

A sequential niche multimodal conformational sampling algorithm for protein structure prediction

Xia, Y., Peng, C., Zhou, X., & Zhang, G. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab500 · Cited by 9

Structure-based interface engineering methodology in designing a thermostable amylose-forming transglucosylase

Tian, Y., Hou, X., Ni, D., Xu, W., Guang, C., Zhang, W., Chen, Q., Rao, Y., & Mu, W. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102074 · Cited by 10

Construction of a Deep Neural Network Energy Function for Protein Physics

Yang, H., Xiong, Z., & Zonta, F. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00069 · Cited by 13

Uncovering and engineering the mechanical properties of the adhesion GPCR ADGRG1 GAIN domainPreprint

Dumas, L., Marfoglia, M., Yang, B., Hijazi, M., Larabi, A., Lau, K., Pojer, F., Nash, M. A., & Barth, P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.05.535724 · Cited by 10

Bioinformatics-aided Protein Sequence Analysis and Engineering

Zhang, W., & Wang, T. (2023) · Current Protein and Peptide Science · doi:10.2174/1389203724666230509124300 · Cited by 9

Listening to life: Sonification for enhancing discovery in biological research

Braun, R., Tfirn, M., & Ford, R. M. (2024) · Biotechnology and Bioengineering · doi:10.1002/bit.28729 · Cited by 9

Allelic variation and haplotype diversity of Matrilineal (MTL) gene governing in vivo maternal haploid induction in maize

Dutta, S., Chhabra, R., Muthusamy, V., Gain, N., Rajkumar, S., Sarika, K., Devi, E. L., Madhavan, J., Zunjare, R. U., & Hossain, F. (2024) · Physiology and Molecular Biology of Plants · doi:10.1007/s12298-024-01456-3 · Cited by 9

A review on computational models for predicting protein solubility

Pimtawong, T., Ren, J., Lee, J., Lee, H., & Na, D. (2025) · The Journal of Microbiology · doi:10.71150/jm.2408001 · Cited by 9

Computational approaches and experimental investigation for identification of potential inhibitors targeting cysteine synthase in Leishmania donovani

Nawaz, A., Rai, G. P., Singh, K., Shanker, A., & Ali, V. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109753 · Cited by 9

OneProt: Towards multi-modal protein foundation models via latent space alignment of sequence, structure, binding sites and text encoders

Flöge, K., Udayakumar, S., Sommer, J., Piraud, M., Kesselheim, S., Fortuin, V., Günnemann, S., van der Weg, K. J., Gohlke, H., Merdivan, E., et al. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013679 · Cited by 13

OrgaNet: A Deep Learning Approach for Automated Evaluation of Organoids Viability in Drug Screening

Bian, X., Li, G., Wang, C., Shen, S., Liu, W., Lin, X., Chen, Z., Cheung, M., & Luó, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91415-8_35 · Cited by 9

Accurate positioning of functional residues with robotics-inspired computational protein design

Krivacic, C. T., Kundert, K., Pan, X., Pache, R. A., Liu, L., Conchúir, S. Ó., Jeliazkov, J. R., Gray, J. J., Thompson, M. C., Fraser, J. S., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115480119 · Cited by 8

Scalable Inhibitors of the Nsp3–Nsp4 Coupling in SARS-CoV-2

Azizogli, A., Pai, V., Coppola, F., Jafari, R., Dodd‐o, J., Harish, R., Balasubramanian, B., Kashyap, J., Acevedo‐Jake, A. M., Král, P., et al. (2023) · ACS Omega · doi:10.1021/acsomega.2c06384 · Cited by 9

Rational Design of Abasic Site-Containing DNA Triplexes To Bind Small Molecules with Low Nanomolar Dissociation Constants

Liu, J., Jiang, T., Zhao, J., Liu, M., Chen, F., Lin, L., He, H., Zuo, H., Huang, C. Z., & Mao, C. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.2c05777 · Cited by 9

Engagement of intrinsic disordered proteins in protein–protein interaction

Roterman, I., Stąpor, K., & Konieczny, L. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1230922 · Cited by 9

Enhancing Sensitivity and Selectivity: Morphological Modification and Chemical Functionalization within Confined Structures

Adhawiyah, R. E., & Lee, J. (2023) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-023-00935-z · Cited by 9

Identification of novel single nucleotide variants in the drug resistance mechanism of Mycobacterium tuberculosis isolates by whole-genome analysis

Qian, W., Ma, N., Zeng, X., Shi, M., Wang, M., Yang, Z., & Tsui, S. K. (2024) · BMC Genomics · doi:10.1186/s12864-024-10390-3 · Cited by 9

Multifunctional Nanomaterials: Recent Advancements in Cancer Therapeutics and Vaccines

Gupta, S. K., Kaur, R., Bhardwaj, A., & Parashar, D. (2024) · Indian Journal of Microbiology · doi:10.1007/s12088-024-01274-x · Cited by 10

Molecular modeling of lactoferrin for food and nutraceutical applications: insights from in silico techniques

Darmawan, K. K., Karagiannis, T. C., Hughes, J. G., Small, D. M., & Hung, A. (2022) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2022.2067824 · Cited by 9

PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids

Singh, V., Sharma, A. K., Dehzangi, A., & Tsunoda, T. (2020) · Genes · doi:10.3390/genes11121431 · Cited by 9

When can we trust structural models derived from pair distribution function measurements?

Maffettone, P. M., Fletcher, W. J. K., Nicholas, T. C., Deringer, V. L., Allison, J. R., Smith, L. J., & Goodwin, A. L. (2025) · Faraday Discussions · doi:10.1039/d4fd00106k · Cited by 8

Assessing the stereoselective bioactivity and biotoxicity of penthiopyrad in soil environment for efficacy improvement and hazard reduction

Fang, K., Liu, T., Tian, G., Sun, W., You, X., & Wang, X. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.136476 · Cited by 8

Synthesis, In Vitro Anti-Inflammatory Activity, Molecular Docking, Molecular Dynamics and DFT Calculations of Thiazoline-2-Thione Derivatives

Farial, B., Bourougaa, L., Belghit, T., Sameh, H., Cheraiet, Z., Mandumpal, J. B., Abdelfattah, M. A. O., Fattouche, M., & Gouasmia, A. (2025) · Applied Sciences · doi:10.3390/app15116095 · Cited by 8

Green biomanufacturing promoted by automatic retrobiosynthesis planning and computational enzyme design

Cui, Z., Zhang, S., Zhang, S., Chen, B., Zhu, Y., & Tan, T. (2021) · Chinese Journal of Chemical Engineering · doi:10.1016/j.cjche.2021.08.017 · Cited by 8

Identification and Functional Analysis of Cytokine-Like Protein CLEC-47 in Caenorhabditis elegans

Pan, W., Huang, X., Guo, Z., Nagarajan, R., & Mylonakis, E. E. (2021) · mBio · doi:10.1128/mbio.02579-21 · Cited by 8

The nucleotide excision repair proteins through the lens of molecular dynamics simulations

Pinto, É. S. M., Krause, M. J., Dorn, M., & Feltes, B. C. (2023) · DNA repair · doi:10.1016/j.dnarep.2023.103510 · Cited by 9

ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins

Lu, P., & Tian, J. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108115 · Cited by 8

Protein engineering using variational free energy approximation

Lobzaev, E., Herrera, M. A., Kasprzyk, M., & Stracquadanio, G. (2024) · Nature Communications · doi:10.1038/s41467-024-54814-w · Cited by 9

DockNmine, a Web Portal to Assemble and Analyse Virtual and Experimental Interaction Data

Gheyouche, E., Launay, R., Lethiec, J., Labeeuw, A., Roze, C., Amossé, A., & Télétchéa, S. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20205062 · Cited by 9

Composition-dependent energetic contribution of complex salt bridges to collagen stability

Sun, T., Qiang, S., Lu, C., & Xu, F. (2021) · Biophysical Journal · doi:10.1016/j.bpj.2021.05.028 · Cited by 8

C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features

Malik, A., Mahajan, N., Dar, T. A., & Kim, C.-B. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23179518 · Cited by 8

Computational Protein Design - Where it goes?

Xu, B., Chen, Y., & Xue, W. (2023) · Current Medicinal Chemistry · doi:10.2174/0929867330666230602143700 · Cited by 9

Cross-Gate MLP with Protein Complex Invariant Embedding Is a One-Shot Antibody Designer

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