AlphaFold heralds a data-driven revolution in biology and medicine

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

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 13,005 · see citing works

ColabFold - Making protein folding accessible to all

Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · 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

Accurate prediction of protein structures and interactions using a 3-track network

Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works

The druggable genome

Hopkins, A. L., & Groom, C. R. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd892 · Cited by 3,305 · see citing works

Highly accurate protein structure prediction for the human proteome

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · see citing works

PDBsum: Structural summaries of PDB entries

Laskowski, R., Jabłońska, J., Pravda, L., Vareková, R., & Thornton, J. (2017) · Protein Science · doi:10.1002/pro.3289 · Cited by 1,650 · see citing works

Protein Data Bank: the single global archive for 3D macromolecular structure data

wwPDB consortium, Burley, S. K., Berman, H. M., Bhikadiya, C., Bi, C., Chen, L., Costanzo, L. D., Christie, C., Duarte, J. M., Dutta, S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky949 · Cited by 1,163 · see citing works

ClinVar: improvements to accessing data

Landrum, M. J., Chitipiralla, S., Brown, G. R., Chen, C., Gu, B., Hart, J., Hoffman, D., Jang, W., Kaur, K., Liu, C., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkz972 · Cited by 994 · see citing works

Earth BioGenome Project: Sequencing life for the future of life

Lewin, H. A., Robinson, G. E., Kress, W. J., Baker, W. J., Coddington, J., Crandall, K. A., Durbin, R., Edwards, S. V., Forest, F., Gilbert, M. T. P., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720115115 · Cited by 962 · see citing works

A Structure-Based Drug Discovery Paradigm

Batool, M., Ahmad, S. B., & Choi, S. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112783 · Cited by 754 · see citing works

Molecular Mechanisms of Disease-Causing Missense Mutations

Stefl, S. K., Nishi, H., Petukh, M. G., Panchenko, A. R., & Alexov, E. G. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.014 · Cited by 334 · see citing works

Machine learning in protein structure prediction

AlQuraishi, M. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.005 · Cited by 252 · see citing works

Next Generation Protein Structure Predictions and Genetic Variant Interpretation

Diwan, G. D., Gonzalez-Sanchez, J. C., Apic, G., & Russell, R. B. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167180 · Cited by 30 · see citing works

Targeting p53 pathways: mechanisms, structures, and advances in therapy

Wang, H., Guo, M., Wei, H., & Chen, Y. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01347-1 · Cited by 1,016 · see citing works

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

Terwilliger, T. C., Liebschner, D., Croll, T. I., Williams, C. J., McCoy, A. J., Poon, B. K., Afonine, P. V., Oeffner, R. D., Richardson, J. S., Read, R. J., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02087-4 · Cited by 411 · see citing works

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

Ren, F., Ding, X., Zheng, M., Korzinkin, M., Cai, X., Zhu, W., Mantsyzov, A., Aliper, A., Aladinskiy, V., Cao, Z., et al. (2023) · Chemical Science · doi:10.1039/d2sc05709c · Cited by 365 · see citing works

The AlphaFold Database of Protein Structures: A Biologist’s Guide

David, A., Islam, S., Tankhilevich, E., & Sternberg, M. J. E. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167336 · Cited by 268 · see citing works

AlphaFold2 models indicate that protein sequence determines both structure and dynamics

Guo, H.-B., Perminov, A., Bekele, S., Kedziora, G., Farajollahi, S., Varaljay, V., Hinkle, K., Molinero, V., Meister, K., Hung, C., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-14382-9 · Cited by 248 · see citing works

AlphaFold2 structures guide prospective ligand discovery

Lyu, J., Kapolka, N. J., Gumpper, R. H., Alon, A., Wang, L., Jain, M. K., Barros-Álvarez, X., Sakamoto, K., Kim, Y., DiBerto, J. F., et al. (2024) · Science · doi:10.1126/science.adn6354 · Cited by 182 · see citing works

The impact of AlphaFold2 one year on

Jones, D. T., & Thornton, J. M. (2022) · Nature Methods · doi:10.1038/s41592-021-01365-3 · Cited by 152 · see citing works

The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins

Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155 · see citing works

AlphaFold2 protein structure prediction: Implications for drug discovery

Borkakoti, N., & Thornton, J. M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102526 · Cited by 137 · see citing works

Data‐Driven Materials Research and Development for Functional Coatings

Xu, K., Xiao, X., Wang, L., Lou, M., Wang, F., Li, C., Ren, H. P., Wang, X., & Chang, K. (2024) · Advanced Science · doi:10.1002/advs.202405262 · Cited by 125 · see citing works

Generative artificial intelligence in medicine

Teo, Z. L., Thirunavukarasu, A. J., Elangovan, K., Cheng, H., Moova, P., Soetikno, B., Nielsen, C., Pollreisz, A., Ting, D. S. J., Morris, R. J. T., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03983-2 · Cited by 119 · see citing works

Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers

Ng, F. Y. C., Thirunavukarasu, A. J., Cheng, H., Tan, T. F., Gutierrez, L., Lan, Y., Ong, J. C. L., Chong, Y. S., Ngiam, K. Y., Ho, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101230 · Cited by 113 · see citing works

How accurately can one predict drug binding modes using AlphaFold models?

(2023) · doi:10.7554/elife.89386.2 · Cited by 98 · see citing works

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103551 · Cited by 96 · see citing works

Deep learning for the PSIPRED Protein Analysis Workbench

Buchan, D. W. A., Moffat, L., Lau, A. M. C., Kandathil, S. M., & Jones, D. T. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae328 · Cited by 94 · see citing works

AlphaFold illuminates half of the dark human proteins

Binder, J. L., Berendzen, J., Stevens, A. O., He, Y., Wang, J., Dokholyan, N. V., & Oprea, T. I. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102372 · Cited by 81 · see citing works

Novel insights into RB1 mutation

Yao, Y., Gu, X., Xu, X., Ge, S., & Jia, R. (2022) · Cancer Letters · doi:10.1016/j.canlet.2022.215870 · Cited by 71 · see citing works

Predicting protein flexibility with AlphaFold

Ma, P., Li, D., & Brüschweiler, R. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26471 · Cited by 71 · see citing works

Structure‐Based Drug Discovery with Deep Learning**

Özçelik, R., van Tilborg, D., Jiménez-Luna, J., & Grisoni, F. (2023) · ChemBioChem · doi:10.1002/cbic.202200776 · Cited by 69 · see citing works

Recent advances in microbial production of medium chain fatty acid from renewable carbon resources: A comprehensive review

Ahn, J. H., Jung, K. H., Lim, E. S., Kim, S. M., Han, S. O., & Um, Y. (2023) · Bioresource Technology · doi:10.1016/j.biortech.2023.129147 · Cited by 69 · see citing works

Fungal biotechnology: From yesterday to tomorrow

Roth, M. G., Westrick, N. M., & Baldwin, T. T. (2023) · Frontiers in Fungal Biology · doi:10.3389/ffunb.2023.1135263 · Cited by 48 · see citing works

Accelerating crystal structure determination with iterative <i>AlphaFold</i> prediction

Terwilliger, T. C., Afonine, P. V., Liebschner, D., Croll, T. I., McCoy, A. J., Oeffner, R. D., Williams, C. J., Poon, B. K., Richardson, J. S., Read, R. J., et al. (2023) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s205979832300102x · Cited by 62 · see citing works

Towards a Definition of Generative Artificial Intelligence

Ronge, R., Maier, M., & Rathgeber, B. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00863-y · Cited by 75 · see citing works

How accurately can one predict drug binding modes using AlphaFold models?

Karelina, M., Noh, J. J., & Dror, R. O. (2023) · eLife · doi:10.7554/elife.89386 · Cited by 55 · see citing works

The X-ray crystallography phase problem solved thanks to <i>AlphaFold</i> and <i>RoseTTAFold</i> models: a case-study report

Barbarin-Bocahu, I., & Graille, M. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798322002157 · Cited by 52 · see citing works

DPAM : A domain parser for AlphaFold models

(2022) · doi:10.1002/pro.4548 · Cited by 49 · see citing works

Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader

Zhao, K., Xia, Y., Zhang, F., Zhou, X., Li, S. Z., & Zhang, G. (2023) · Communications Biology · doi:10.1038/s42003-023-04605-8 · Cited by 46 · see citing works

Assessing PDB macromolecular crystal structure confidence at the individual amino acid residue level

Shao, C., Bittrich, S., Wang, S., & Burley, S. K. (2022) · Structure · doi:10.1016/j.str.2022.08.004 · Cited by 47 · see citing works

The urgent challenge of ocean pollution: Impacts on marine biodiversity and human health

Chenniappan, T., & Devarajan, Y. (2024) · Regional Studies in Marine Science · doi:10.1016/j.rsma.2024.103995 · Cited by 54 · see citing works

Modeling protein–ligand interactions for drug discovery in the era of deep learning

Wang, Y. X., Li, Y., Chen, J., & Lai, L. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00415b · Cited by 46 · see citing works

Indole Alkaloids from Psychoactive Mushrooms: Chemical and Pharmacological Potential as Psychotherapeutic Agents

Plazas, E., & Faraone, N. (2023) · Biomedicines · doi:10.3390/biomedicines11020461 · Cited by 41 · see citing works

ParaFold: Paralleling AlphaFold for Large-Scale Predictions

Zhong, B., Su, X., Wen, M., Zuo, S.-C., Hong, L. Y., & Lin, J. (2022) · International Conference on High Performance Computing in Asia-Pacific Region Workshops (HPC Asia Workshops) · doi:10.1145/3503470.3503471 · Cited by 40 · see citing works

Membranome 3.0: Database of single‐pass membrane proteins with AlphaFold models

Lomize, A. L., Schnitzer, K. A., Todd, S. C., Cherepanov, S., Outeiral, C., Deane, C. M., & Pogozheva, I. D. (2022) · Protein Science · doi:10.1002/pro.4318 · Cited by 38 · see citing works

Classification of domains in predicted structures of the human proteome

Schaeffer, R. D., Zhang, J., Kinch, L. N., Pei, J., Cong, Q., & Grishin, N. V. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2214069120 · Cited by 34 · see citing works

Does AlphaFold2 model proteins’ intracellular conformations? An experimental test using cross-linking mass spectrometry of endogenous ciliary proteins

McCafferty, C. L., Pennington, E. L., Papoulas, O., Taylor, D. W., & Marcotte, E. M. (2023) · Communications Biology · doi:10.1038/s42003-023-04773-7 · Cited by 35 · see citing works

Mapping synthetic binding proteins epitopes on diverse protein targets by protein structure prediction and protein-protein docking

Mijit, A., Wang, X., Li, Y., Xu, H., Chen, Y., & Xue, W. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107183 · Cited by 35 · see citing works

Predicting multiple conformations of ligand binding sites in proteins suggests that AlphaFold2 may remember too much

Lazou, M., Khan, O., Nguyen, T., Padhorny, D., Kozakov, D., Joseph‐McCarthy, D., & Vajda, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412719121 · Cited by 31 · see citing works

The dominant logic of Big Tech in healthcare and pharma

Schuhmacher, A., Haefner, N., Honsberg, K., Goldhahn, J., & Gassmann, O. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.103457 · Cited by 31 · see citing works

Machine Learning Generation of Dynamic Protein Conformational Ensembles

Zheng, L.-E., Barethiya, S., Nordquist, E. B., & Chen, J. (2023) · Molecules · doi:10.3390/molecules28104047 · Cited by 32 · see citing works

Rational Prediction of PROTAC-Compatible Protein–Protein Interfaces by Molecular Docking

Pereira, G. P., Jiménez‐García, B., Pellarin, R., Launay, G., Wu, S., Martin, J., & Souza, P. C. T. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01154 · Cited by 32 · see citing works

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics

Zheng, L., Shi, S., Sun, X., Lu, M., Liao, Y., Zhu, S., Zhang, H., Pan, Z., Fang, P., Zeng, Z., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae006 · Cited by 32 · see citing works

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Sun, Y., Jiao, Y., Shi, C., & Zhang, Y. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.09.002 · Cited by 31 · see citing works

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

Varadi, M., Bordin, N., Orengo, C., & Velankar, S. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102543 · Cited by 29 · see citing works

Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects

Dou, Z., Sun, Y., Jiang, X., Wu, X., Li, Y., Gong, B., & Wang, L. (2023) · Acta Biochimica et Biophysica Sinica · doi:10.3724/abbs.2023033 · Cited by 30 · see citing works

Engineering therapeutic monoclonal antibodies

Stone, C. A., Spiller, B. W., & Smith, S. A. (2023) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2023.11.018 · Cited by 29 · see citing works

Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm

(2024) · doi:10.1007/s12539-023-00597-5 · Cited by 29 · see citing works

Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and structures

(2024) · doi:10.1038/s41592-024-02409-0 · Cited by 29 · see citing works

Protein multiple conformations prediction using multi-objective evolution algorithm

Hou, M.-H., Jin, S.-R., Cui, X.-Y., Peng, C.-X., Zhao, K.-L., Song, L., & Zhang, G.-J. (2023) · doi:10.1101/2023.04.21.537776 · Cited by 29 · see citing works

Technologies for design-build-test-learn automation and computational modelling across the synthetic biology workflow: A review

Matzko, R., & Konur, S. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00455-4 · Cited by 28 · see citing works

Dracorhodin targeting CMPK2 attenuates inflammation: A novel approach to sepsis therapy

Zhang, W., Jiang, H., Huang, P., Wu, G., Wang, Q., Luan, X., Zhang, H., Yu, D., Wang, H., Lu, D., et al. (2023) · Clinical and Translational Medicine · doi:10.1002/ctm2.1449 · Cited by 27 · see citing works

Has DeepMind’s AlphaFold Solved the Protein Folding Problem?

Al-Janabi, A. (2022) · BioTechniques · doi:10.2144/btn-2022-0007 · Cited by 26 · see citing works

SIGMA leverages protein structural information to predict the pathogenicity of missense variants

Zhao, H., Du, H., Zhao, S., Chen, Z., Li, Y., Xu, K., Liu, B., Cheng, X., Wen, W., Li, G., et al. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100687 · Cited by 24 · see citing works

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications

Andreini, C., & Rosato, A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23147684 · Cited by 25 · see citing works

The small nonstructural protein NP1 of human bocavirus 1 directly interacts with Ku70 and RPA70 and facilitates viral DNA replication

Ning, K., Wang, Z., Cheng, F., Yan, Z., & Qiu, J. (2022) · PLoS Pathogens · doi:10.1371/journal.ppat.1010578 · Cited by 22 · see citing works

Assessment of AlphaFold2 for Human Proteins via Residue Solvent Exposure

Bæk, K. T., & Kepp, K. P. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00243 · Cited by 22 · see citing works

Prediction of protein stability changes upon single-point variant using 3D structure profile

Gong, J., Wang, J., Zong, X., Ma, Z., & Xu, D. H. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.008 · Cited by 22 · see citing works

Precision cardiovascular medicine: shifting the innovation paradigm

Aikawa, M., Sonawane, A. R., Chelvanambi, S., Asano, T., Halu, A., Matamalas, J. T., Singh, S. A., Uchida, S., Aïkawa, E., Arenas, À., et al. (2025) · Frontiers in Science · doi:10.3389/fsci.2025.1474469 · Cited by 22 · see citing works

Accelerating the design and development of polymeric materials via deep learning: Current status and future challenges

Li, D., Ru, Y., Chen, Z., Dong, C., Dong, Y., & Liu, J. (2023) · APL Machine Learning · doi:10.1063/5.0131067 · Cited by 21 · see citing works

VHH Structural Modelling Approaches: A Critical Review

Vishwakarma, P., Vattekatte, A. M., Shinada, N. K., Diharce, J., Martins, C., Cadet, F., Gardebien, F., Etchebest, C., Nadaradjane, A. A., & de Brevern, A. G. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23073721 · Cited by 19 · see citing works

Critical assessment of AI-based protein structure prediction: Fundamental challenges and future directions in drug discovery

Niazi, S. (2025) · Computational and Structural Biotechnology Reports · doi:10.1016/j.csbr.2025.100064 · Cited by 20 · see citing works

Capturing the geometry, function, and evolution of enzymes with 3D templates

Riziotis, I. G., & Thornton, J. M. (2022) · Protein Science · doi:10.1002/pro.4363 · Cited by 18 · see citing works

Ten quick tips for sequence-based prediction of protein properties using machine learning

Hou, Q., Waury, K., Gogishvili, D., & Feenstra, K. A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010669 · Cited by 19 · see citing works

AlphaFold2 structures template ligand discoveryPreprint

Lyu, J., Kapolka, N. J., Gumpper, R. H., Alon, A., Wang, L., Jain, M. K., Barros-Álvarez, X., Sakamoto, K., Kim, Y., DiBerto, J. F., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.20.572662 · Cited by 18 · see citing works

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

Veríssimo, G. C., Serafim, M. S. M., Kronenberger, T., Ferreira, R. S., Honório, K. M., & Maltarollo, V. G. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2114451 · Cited by 17 · see citing works

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng, C., Xu, L., Zhao, L. J., Han, Y., & Hao, K. (2023) · Advanced Engineering Materials · doi:10.1002/adem.202201430 · Cited by 17 · see citing works

AlphaFold2 Update and Perspectives

Tourlet, S., Radjasandirane, R., Diharce, J., & de Brevern, A. G. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3020025 · Cited by 17 · see citing works

Toward the appropriate interpretation of Alphafold2

Xu, T., Xu, Q., & Li, J. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1149748 · Cited by 16 · see citing works

AI-Predicted Protein Deformation Encodes Energy Landscape Perturbation

McBride, J. M., & Tlusty, T. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.098401 · Cited by 17 · see citing works

The Implications of Artificial Intelligence on Infection Prevention and Control: Current Progress and Future Perspectives

Yang, L., Lu, S., & Zhou, L. (2024) · China CDC Weekly · doi:10.46234/ccdcw2024.192 · Cited by 17 · see citing works

AI and omics technologies in biobanking: Applications and challenges for public health

Venturini, P., de Faria, P. L., & Cordeiro, J. V. (2025) · Public Health · doi:10.1016/j.puhe.2025.105726 · Cited by 16 · see citing works

Delineation of functionally essential protein regions for 242 neurodevelopmental genes

Iqbal, S., Brünger, T., Pérez‐Palma, E., Macnee, M., Brunklaus, A., Daly, M. J., Campbell, A. J., Hoksza, D., May, P., & Lal, D. (2022) · Brain · doi:10.1093/brain/awac381 · Cited by 16 · see citing works

How accurately can one predict drug binding modes using AlphaFold models?

Karelina, M., Noh, J. J., & Dror, R. O. (2023) · doi:10.7554/elife.89386.1 · Cited by 16 · see citing works

Enhancing cell‐based therapies with synthetic gene circuits responsive to molecular stimuli

Galvan, S., Teixeira, A. P., & Fussenegger, M. (2024) · Biotechnology and Bioengineering · doi:10.1002/bit.28770 · Cited by 15 · see citing works

Machine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study

Alfonso‐Prieto, M., & Capelli, R. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00380 · Cited by 15 · see citing works

The 3D Modules of Enzyme Catalysis: Deconstructing Active Sites into Distinct Functional Entities

(2023) · doi:10.1016/j.jmb.2023.168254 · Cited by 15 · see citing works

In silico analysis and characterization of medicinal mushroom cystathionine beta-synthase as an angiotensin converting enzyme (ACE) inhibitory protein

Goh, N.-Y., Razif, M. F., Yap, Y. H., Ng, C., & Fung, S.-Y. (2021) · Comput. Biol. Chem. · doi:10.1016/j.compbiolchem.2021.107620 · Cited by 13 · see citing works

Molecular engineering tools for the development of vaccines against infectious diseases: current status and future directions

Xue, W., Li, T., Gu, Y., Li, S., & Xia, N. (2023) · Expert Review of Vaccines · doi:10.1080/14760584.2023.2227699 · Cited by 17 · see citing works

The Role of Structural Bioinformatics in Understanding Tumor Necrosis Factor α-Interacting Protein Mechanisms in Chronic Inflammatory Diseases: A Review

Bastos, L. L., Mariano, D., Lemos, R. P., Bialves, T. S., Oliveira, C. J. F., & de Melo-Minardi, R. C. (2024) · Immuno · doi:10.3390/immuno4010002 · Cited by 16 · see citing works

FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics

Zhang, G., Zhang, C., Cai, M., Luo, C., Zhu, F., & Liang, Z. T. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.131180 · Cited by 13 · see citing works

AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein

Guo, H.-B., Huntington, B., Perminov, A., Smith, K., Hastings, N., Dennis, P., Kelley-Loughnane, N., & Berry, R. (2024) · PLOS ONE · doi:10.1371/journal.pone.0301866 · Cited by 13 · see citing works

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

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FastFold: Optimizing AlphaFold Training and Inference on GPU Clusters

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PGRS domain structures: Doomed to sail the mycomembrane

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De novo design of transmembrane nanopores

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Analysis of conformational stability of interacting residues in protein binding interfaces

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Utilizing anomalous signals for element identification in macromolecular crystallography

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Changing the Culture on Data Management and Sharing: Getting Ready for the New NIH Data Sharing Policy

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Discovering value: women’s participation in university and commercial AI invention

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Sequence and structure alignments in post-AlphaFold era

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How is Artificial Intelligence Changing Science?

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In silico prediction of heme binding in proteins

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Using deep-learning predictions reveals a large number of register errors in PDB depositions

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Categorizing prediction modes within low-pLDDT regions of AlphaFold 2 structures: near-predictive, pseudostructure and barbed wire

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Cross-species molecular docking method to support predictions of species susceptibility to chemical effects

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Bias in the AlphaFold3 prediction of ligand-induced domain motion in enzymes

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AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

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AlphaFold: protein structure predictions that will revolutionise medicine

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AlphaFold: How AI Is Revolutionising Modern Medicine

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AlphaFold2 and its applications in the fields of biology and medicine

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Beyond AlphaFold: Charting the Future of AI in Protein Structure Prediction

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Predicting Proteome-Scale Protein Structure with Artificial Intelligence

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