The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.
Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery
Open accessAbstract
Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli 's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Within the chemoinformatics space, there are advances in computationally modeling how small molecules and proteins interact via docking.157–159”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [158]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
UCSF Chimera—A visualization system for exploratory research and analysis
Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20084 · Cited by 45,882 · cited 3×
“We used a Python script that employs the DockPrep function from UCSF Chimera (Pettersen et al , 2004; Allen, 2018) to add hydrogens and partial charges.”
MethodHighly 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 · cited 8×
“This is consistent with previous assessments of AlphaFold’s fidelity to experimentally determined protein structures (Jumper et al , 2021) and suggests that the weak performance of our model arises from the docking method, and not the quality of protein structures.”
BackgroundMethodResultInfluentialBerman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · cited 9×
“As positive controls for our docking simulations, we re-docked 11 experimentally evidenced protein-ligand complexes from the RCSB Protein Data Bank (Dataset EV2).”
MethodInfluentialAutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings
Eberhardt, J., Santos‐Martins, D., Tillack, A. F., & Forli, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00203 · Cited by 8,292 · cited 5×
“Different software for performing molecular docking are widely available and commonly used platforms include AutoDock Vina (Eberhardt et al , 2021 ) and DOCK (Allen et al , 2015 ).”
MethodInfluentialThe complete genome sequence of Escherichia coli K-12.
Blattner, F., Plunkett, G., Bloch, C., Perna, N., Burland, V., Riley, M., Collado-Vides, J., Glasner, J. D., Rode, C., Mayhew, G., et al. (1997) · Science · doi:10.1126/science.277.5331.1453 · Cited by 7,996 · cited 2×
“We shortlisted genes identified in at least two of these three studies, resulting in a total of 295 genes out of ∼ 4,000 total genes in E. coli (Blattner et al , 1997).”
MethodVaradi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · cited 3×
“As shown in the workflow here, in order to leverage AlphaFold 2 for docking, we first downloaded all 296 AlphaFold 2 -predicted E. coli essential protein structures from the AlphaFold Protein Structure Database (Jumper et al , 2021 ; Varadi et al , 2022 ).”
InfluentialAccurate 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 5,549 · cited 2×
“The advent of AlphaFold2 and other deep neural networks for protein folding, such as RoseTTAFold (Baek et al , 2021; Humphreys et al , 2021), has been widely anticipated and celebrated in structural biology.”
BackgroundDocking and scoring in virtual screening for drug discovery: methods and applications
Kitchen, D. B., Decornez, H. Y., Furr, J. R., & Bajorath, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1549 · Cited by 4,354 · cited 1×
“Various approaches to identifying molecular drug targets have been developed, including those based on biochemical assays, genetic interactions, and molecular docking (Kitchen et al , 2004; Schenone et al , 2013).”
BackgroundMolecular Docking: A Powerful Approach for Structure-Based Drug Discovery
Meng, X.-Y., Zhang, H.-X., Mezei, M., & Cui, M. (2011) · Current Computer Aided-Drug Design · doi:10.2174/157340911795677602 · Cited by 3,771 · cited 1×
“Model predictions based on the following rescoring functions were ensembled in sequence: RF-Score, NNScore, PLEC score, and RF-Score-VS.”
MethodHighly 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
Flagged first, then by citations
1 of the 52 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Computational approaches streamlining drug discovery
Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,424
“…targets lacking experimental structures, especially for target classes with less obvious structural homologies in the ligand-binding pocket, the performance of AlphaFold2 models in small-molecule docking showed disappointing results in recent assessments for GPCR and antibacterial targets 107,108 .”
BackgroundAlphaFold2 and its applications in the fields of biology and medicine
Yang, Z., Zeng, X., Zhao, Y. C., & Chen, R. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01381-z · Cited by 655
“Wong et al. 102 combined molecular docking simulations with AF2 for protein-ligand interactions prediction.”
BackgroundDiscovery of a structural class of antibiotics with explainable deep learning
Wong, F. J., Zheng, E. J., Valeri, J. A., Donghia, N. M., Anahtar, M. N., Omori, S., Li, A., Cubillos-Ruiz, A., Krishnan, A., Jin, W., et al. (2023) · Nature · doi:10.1038/s41586-023-06887-8 · Cited by 530
Self-citationClustering predicted structures at the scale of the known protein universe
Barrio-Hernandez, I., Yeo, J., Jänes, J., Mirdita, M., Gilchrist, C. L. M., Wein, T., Varadi, M., Velankar, S., Beltrao, P., & Steinegger, M. (2023) · Nature · doi:10.1038/s41586-023-06510-w · Cited by 362
Leveraging artificial intelligence in the fight against infectious diseases
Wong, F. J., de la Fuente‐Núñez, C., & Collins, J. J. (2023) · Science · doi:10.1126/science.adh1114 · Cited by 331
Self-citationBefore and after AlphaFold2: An overview of protein structure prediction
Bertoline, L. M. F., Lima, A. N., Krieger, J. E., & Teixeira, S. K. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1120370 · Cited by 330
Machine learning for antimicrobial peptide identification and design
Wan, F., Wong, F. J., Collins, J. J., & de la Fuente‐Núñez, C. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00152-x · Cited by 317
Self-citationHow good are AlphaFold models for docking-based virtual screening?
Scardino, V., Di Filippo, J. I., & Cavasotto, C. N. (2023) · iScience · doi:10.1016/j.isci.2022.105920 · Cited by 207
“On the other hand, Wong et al.(57) developed a model to predict protein-ligand interactions based on AF structures and molecular docking and indicated, contrary to our results, that "molecular docking using AlphaFold2-predicted structures is similar to using experimentally determined ones.”
ResultAlphaFold2 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 192
State-specific protein–ligand complex structure prediction with a multiscale deep generative model
Qiao, Z., Nie, W., Vahdat, A., Miller, T. F., & Anandkumar, A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00792-z · Cited by 210
Antimicrobial resistance crisis: could artificial intelligence be the solution?
Liu, G., Yu, D., Fan, M.-M., Zhang, X., Jin, Z., Tang, C. M., & Liu, X. (2024) · Military Medical Research · doi:10.1186/s40779-024-00510-1 · Cited by 138
“Unfortunately, the model showed, on average, weak performance across 12 essential targets with an area under the receiver characteristic curve (AUROC) of 0.48, and this was attributed to the docking method rather than AlphaFold2 predictions [93].”
BackgroundMethodInfluentialUni-Dock: GPU-Accelerated Docking Enables Ultralarge Virtual Screening
Yu, Y., Cai, C., Wang, J., Bo, Z., Zhu, Z., & Zheng, H. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01145 · Cited by 129
Modern Alchemical Free Energy Methods for Drug Discovery Explained
York, D. M. (2023) · ACS Physical Chemistry Au · doi:10.1021/acsphyschemau.3c00033 · Cited by 114
A biocompatible tea polyphenol nanoplatform for efficient cytosolic delivery of protein therapeutics
Wang, H., Tang, C., Zhang, H., Guo, L., Zou, C., Zhang, S., Liu, J., Huang, Q., Liu, Y., Zhou, W., et al. (2025) · Journal of Controlled Release · doi:10.1016/j.jconrel.2025.114347 · Cited by 109
How accurately can one predict drug binding modes using AlphaFold models?
(2023) · doi:10.7554/elife.89386.2 · Cited by 98
“, 2021)—for tasks related to drug design, with mixed results (Díaz-Rovira et al., 2023; He et al., 2022; Heo & Feig, 2022; C. Lee et al., 2022; S. Lee et al., 2022; Liang et al., 2022; Qiao et al., 2022; Scardino et al., 2022; Wong et al., 2022).”
Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening
Blanes‐Mira, C., Fernández-Aguado, P., de Andrés-López, J., Fernández-Carvajal, A. M., Ferrer-Montiel, A. V., & Fernandez-Ballester, G. J. (2022) · Molecules · doi:10.3390/molecules28010175 · Cited by 113
“They concluded that the limitations in benchmarking are not due to the AlphaFold structures itself, but to the methods to accurately model the protein–ligand interactions [27].”
BackgroundMethodA practical guide to machine-learning scoring for structure-based virtual screening
Tran‐Nguyen, V., Junaid, M., Simeon, S., & Ballester, P. J. (2023) · Nature Protocols · doi:10.1038/s41596-023-00885-w · Cited by 106
Deep learning in image-based phenotypic drug discovery
Krentzel, D., Shorte, S. L., & Zimmer, C. (2023) · Trends in Cell Biology · doi:10.1016/j.tcb.2022.11.011 · Cited by 104
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
Masters, M. R., Mahmoud, A. H., & Lill, M. A. (2025) · Nature Communications · doi:10.1038/s41467-025-63947-5 · Cited by 141
Wang, Q., Xu, K., Cai, X., Wang, C., Cao, Y., & Xiao, J. (2023) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.2c08444 · Cited by 90
Deep generative design of RNA aptamers using structural predictions
Wong, F. J., He, D., Krishnan, A., Hong, L., Wang, A. Z., Wang, J., Hu, Z., Omori, S., Li, A., Rao, J., et al. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00720-6 · Cited by 90
Self-citationSong, Y., Yuan, Q., Chen, S., Zeng, Y., Zhao, H., & Yang, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-52533-w · Cited by 89
“AlphaFold2 25 has made a breakthrough in protein structure prediction, with the predicted structures con fi rmed to be useful in DNA-binding site prediction 26,27 , antibiotic discovery 28 , and the study of intrinsically disordered proteins 29 .”
BackgroundZhu, H., Zhang, Y., Li, W., & Huang, N. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232415961 · Cited by 92
“With the facilitation of the protein structure prediction by AlphaFold 2 [20,21], more modeled structures will undoubtedly be employed in SBVS before the target structures are solved experimentally [22,23].”
MethodStructure-based virtual screening of vast chemical space as a starting point for drug discovery
Carlsson, J., & Luttens, A. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102829 · Cited by 90
Assessing molecular docking tools: understanding drug discovery and design
Nivatya, H. K., Singh, A., Kumar, N., Sonam, Sharma, L., Singh, V., Mishra, R., Gaur, N., & Mishra, A. K. (2025) · Future Journal of Pharmaceutical Sciences · doi:10.1186/s43094-025-00862-y · Cited by 96
Discovering small-molecule senolytics with deep neural networks
Wong, F., Omori, S., Donghia, N. M., Zheng, E. J., & Collins, J. J. (2023) · Nature Aging · doi:10.1038/s43587-023-00415-z · Cited by 82
“62), as we have done in previous work 79 .”
MethodSelf-citationAncajas, C. M. F., Oyedele, A. S., Butt, C. M., & Walker, A. S. (2024) · Natural Product Reports · doi:10.1039/d4np00009a · Cited by 75
Using AlphaFold Predictions in Viral Research
Gutnik, D., Evseev, P. V., Анатольевич, М. К., & Shneider, M. M. (2023) · Current Issues in Molecular Biology · doi:10.3390/cimb45040240 · Cited by 65
“[74] to assess AlphaFold-enabled molecular docking predictions for drug discovery The use of AF2 together with molecular docking simulations to predict protein-ligand bindings demonstrated poor performance.”
BackgroundVitale, G. A., Geibel, C., Minda, V., Wang, M., Aron, A. T., & Petras, D. (2024) · Natural Product Reports · doi:10.1039/d3np00050h · Cited by 66
Zhang, Y., Huang, J., Liu, Y.-H., Zhang, J., Huang, Z. W., Liu, Y., Zhao, J., & Ying, G. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.123514 · Cited by 62
Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry
Mroz, A. M., Basford, A. R., Hastedt, F., Jayasekera, I. S., Mosquera‐Lois, I., Sedgwick, R., Ballester, P. J., Bocarsly, J. D., del Rio‐Chanona, E. A., Evans, M. L., et al. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00146c · Cited by 61
Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50
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 53
NP-hard but no longer hard to solve? Using quantum computing to tackle optimization problems
Au-Yeung, R., Chancellor, N., & Halffmann, P. (2023) · Frontiers in Quantum Science and Technology · doi:10.3389/frqst.2023.1128576 · Cited by 60
“Quantum computing can overcome AlphaFold’s shortcomings (Mulligan et al., 2019; Wong et al., 2022) and dramatically change both landscape and revenue in the next few decades (Bobier et al.”
BackgroundGabaldón-Figueira, J. C., Martínez-Peinado, N., Escabia, E., Ros-Lucas, A., Chatelain, E., Scandale, I., Gascón, J., Pinazo, M., & Alonso-Padilla, J. (2023) · Research and Reports in Tropical Medicine · doi:10.2147/rrtm.s415273 · Cited by 46
Malhotra, Y., John, J., Yadav, D., Sharma, D., Vanshika, Rawal, K., Mishra, V., & Chaturvedi, N. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109842 · Cited by 42
Advancing structural biology through breakthroughs in AI
Aithani, L., Alcaide, E., Bartunov, S., Cooper, C. D. O., Dore, A. S., Lane, T. J., Maclean, F., Rucktooa, P., Shaw, R. A., & Skerratt, S. E. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102601 · Cited by 35
Vaccine development using artificial intelligence and machine learning: A review
Asediya, V., Anjaria, P. A., Mathakiya, R. A., KORINGA, P. G., Nayak, J. B., Bisht, D., Fulmali, D., Patel, V. A., & Desai, D. N. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.136643 · Cited by 35
Yan, Z., Wei, D., Li, X., & Chung, L. W. (2024) · Nature Communications · doi:10.1038/s41467-024-48453-4 · Cited by 35
Beyond sequence: Structure-based machine learning
Durairaj, J., de Ridder, D., & van Dijk, A. D. J. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.039 · Cited by 32
An explainable deep learning platform for molecular discovery
Wong, F. J., Omori, S., Li, A., Krishnan, A., Lach, R. S., Rufo, J., Wilson, M. Z., & Collins, J. J. (2024) · Nature Protocols · doi:10.1038/s41596-024-01084-x · Cited by 28
“In general, Chemprop and other chemotype-based approaches are also target independent and different from target-specific approaches, such as molecular docking 56,57 .”
Self-citationRobustly interrogating machine learning-based scoring functions: what are they learning?
Durant, G., Boyles, F., Birchall, K., Marsden, B. D., & Deane, C. M. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf040 · Cited by 27
“…are likely to be less accurately predicted compared to the crystal structure, yet this impact has been explored in a limited manner for a few scoring functions by us and others (Francoeur et al. 2020, McNutt et al. 2021, Shen et al. 2021, Boyles et al. 2022, Wong et al. 2022, Scardino et al. 2023).”
Rout, A. K., Dehury, B., Parida, S., Rout, S. S., Jena, R., Kaushik, N., Kaushik, N., Pradhan, S., Sahoo, C. R., Singh, A. K., et al. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.132030 · Cited by 29
Meller, A., de Oliveira, S. H. P., Davtyan, A., Abramyan, T. M., Bowman, G. R., & van den Bedem, H. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1171143 · Cited by 28
“(Díaz-Rovira et al., 2022; Wong et al., 2022) Here, we show that this can be mitigated by considering conformational heterogeneity using MSMs.”
Homma, M., Wakabayashi, T., Moriwaki, Y., Shiotani, N., Shigeta, T., Isobe, K., Okazawa, A., Ohta, D., Terada, T., Shimizu, K., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313683121 · Cited by 26
Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs
Alhumaid, N. K., & Tawfik, E. A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251810139 · Cited by 25
Addressing antibiotic resistance: computational answers to a biological problem?
Behling, A. H., Wilson, B. C., Ho, D., Virta, M., O'Sullivan, J. M., & Vatanen, T. (2023) · Current Opinion in Microbiology · doi:10.1016/j.mib.2023.102305 · Cited by 25
AlphaFold and what is next: bridging functional, systems and structural biology
Szczepski, K., & Jaremko, Ł. (2025) · Expert Review of Proteomics · doi:10.1080/14789450.2025.2456046 · Cited by 25
“Another example is study done by Wong et al. [68] where the combined application of AF2 predicted structures with molecular docking was used to evaluate protein – ligand interactions between nearly 300 proteins from E. coli and 318 (218 active and 100 inactive) antibacterial compounds, further…”
Truong, V., Bae, Y.-J., Rarison, R. H. G., Bang, J.-H., Park, S.-Y., & Jeong, W. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241914958 · Cited by 24
Shen, T., Guo, J., Han, Z., Zhang, G., Liu, Q., Si, X., Wang, D., Wu, S., & Xia, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01562 · Cited by 25
Bjerrum, E. J., Margreitter, C., Blaschke, T., Kolarova, S., & de Castro, R. L.-R. (2023) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-023-00512-6 · Cited by 23
Novel antidepressant mechanism of hypericin: Role of connexin 43-based gap junctions
Wang, H., Yang, X., Lai, H., Sun, Y., Xu, Y., Ai, Q., Lin, M., Yang, S., Yang, Y., Chu, S., et al. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.115545 · Cited by 22
Protein engineering of amine transaminases
Meng, Q., Ramírez-Palacios, C., Wijma, H. J., & Janssen, D. B. (2022) · Frontiers in Catalysis · doi:10.3389/fctls.2022.1049179 · Cited by 21
Artificial intelligence for radiopharmaceutical and molecular imaging
Tao, J., Liang, L., Hao, S., Chen, Y., Yang, Z., Cai, Y., & Zhu, H. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.09.039 · Cited by 23
“For instance, Wong et al. 36 combined AutoDock Vina 37 with AlphaFold2 to predict protein — ligand interactions.”
Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling
Ding, X., Chen, X., Sullivan, E. E., Shay, T. F., & Gradinaru, V. (2024) · Molecular Therapy · doi:10.1016/j.ymthe.2024.04.003 · Cited by 20
Zhao, Q., Zhao, H., Guo, L., Zheng, K., Li, Y., Ling, Q., Tang, J. P., Li, Y., & Wang, J. (2025) · Nature Communications · doi:10.1038/s41467-025-61745-7 · Cited by 21
Guo, J. R. (2024) · PLoS ONE · doi:10.1371/journal.pone.0296676 · Cited by 18
“However, the effectiveness of the virtual screening process has been questioned, with researchers suggesting that weaknesses in the modeling of protein-ligand interactions are to blame for less favorable results [4].”
BackgroundPerspectives on current approaches to virtual screening in drug discovery
Muegge, I., Bentzien, J., & Ge, Y. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2390511 · Cited by 20
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
Generation of focused drug molecule library using recurrent neural network
Zou, J., Zhao, L., & Shi, S. (2023) · Journal of Molecular Modeling · doi:10.1007/s00894-023-05772-5 · Cited by 20
AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility
Martin, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01805 · Cited by 20
Aizezi, Y., Zhao, H., Zhang, Z., Bi, Y., Yang, Q., Guo, G., Zhang, H., Guo, H., Jiang, K., & Wang, Z.-Y. (2024) · The Plant Cell · doi:10.1093/plcell/koad299 · Cited by 16
Kersten, C., Clower, S., & Barthels, F. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01400 · Cited by 15
Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning
Gadiya, Y., Genilloud, O., Bilitewski, U., Brönstrup, M., von Berlin, L., Attwood, M., Gribbon, P. M., & Zaliani, A. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02347 · Cited by 16
Optogenetics-enabled discovery of integrated stress response modulators
Wong, F. J., Li, A., Omori, S., Lach, R. S., Núñez, J. R., Ren, Y., Brown, S. P., Singhal, V., Lyda, B. R., Batjargal, T., et al. (2025) · Cell · doi:10.1016/j.cell.2025.06.024 · Cited by 16
“The screening library of 370,830 compounds was custom assembled at Integrated Biosciences, Inc., and includes previously described libraries 32–34 , 91”
Self-citationHow 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
Borrego, J. G., & Torrent, M. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01067-4 · Cited by 21
“These results align with studies advocating refinement techniques to improve enrichment factors but differences in force fields or scoring functions among programs may also influence performance [13, 14].”
Manen-Freixa, L., & Antolín, A. A. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2376643 · Cited by 15
“Scientists at MIT have recently shown that they can predict the target of antibiotics across the proteome of E. coli using inverse docking [308] with reasonable performance (auROCs = 0.63) – although this performance was only achieved when rescoring using machine learning.”
InfluentialAn in-depth review of AI-powered advancements in cancer drug discovery
Le, M. H. N., Nguyen, P. K., Nguyen, T. P. T., Nguyen, H. Q., Tam, D. N. H., Huynh, H. H., Huynh, P. K., & Le, N. Q. K. (2025) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2025.167680 · Cited by 15
Do Deep Learning Models for Co-Folding Learn the Physics of Protein-Ligand Interactions?Preprint
Masters, M., Mahmoud, A., & Lill, M. A. (2024) · bioRxiv · doi:10.1101/2024.06.03.597219 · Cited by 11
“…spawning hundreds of subsequent papers including high-throughput structure prediction [3, 4, 5], ensemble sampling [6, 7, 8], molecular docking [9, 10, 11, 12], stability prediction [13, 14, 15, 16], further method development [17, 18, 19, 20], and a host of other applied studies [21, 22, 23, 24].”
BackgroundDong, X., Sun, C., Guo, J., Ma, X., Xian, M., & Zhang, R. (2023) · Green Chemistry · doi:10.1039/d3gc02083e · Cited by 14
Fatoki, T. H., Balogun, T. C., Ojewuyi, A. E., Omole, A. C., Olukayode, O. V., Adewumi, A. P., Umesi, A. J., Ijeoma, N. P., Apooyin, A. E., Chinedu, C. P., et al. (2024) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-024-00804-z · Cited by 14
Metastable alpha‐rich and beta‐rich conformations of small Aβ42 peptide oligomers
Nguyen, P. H., Sterpone, F., & Derreumaux, P. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26495 · Cited by 13
“56 Alpha-Fold2 limitations to predict very accurately the structures of protein – protein (peptide) complexes 57,58 and generate conformational heterogeneity 59 were reported.”
Integrating Large-Scale Protein Structure Prediction into Human Genetics Research
Marrero, M. C., Jänes, J., Baptista, D., & Beltrão, P. (2024) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120622-020615 · Cited by 13
Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios
Gu, S., Yang, Y., Zhao, Y., Qiu, J., Wang, X., Tong, H. H. Y., Liu, L., Wan, X., Liu, H., Hou, T., et al. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01976 · Cited by 14
Khan, S. S., & Khatri, D. K. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108285 · Cited by 13
He, W., Ding, C., Lin, T., Wang, B., Wang, W., Deng, Z., Jin, T., Shang, Y., Zheng, D., Bai, T., et al. (2024) · Theranostics · doi:10.7150/thno.101229 · Cited by 13
“Moreover, the hydrophobic pockets of Pdk4 were occupied successfully by CF@P with a low binding energy of -9.047 kcal/mol, satisfying the strict criterion of binding energy less than -7 kcal/mol [57].”
InfluentialEssay: Using Machine Learning for Antibiotic Discovery
de la Fuente‐Núñez, C., & Collins, J. J. (2025) · Physical Review Letters · doi:10.1103/y3fg-s9vg · Cited by 13
Self-citationPutting Chemical Knowledge to Work in Machine Learning for Reactivity
Jorner, K. (2023) · CHIMIA International Journal for Chemistry · doi:10.2533/chimia.2023.22 · Cited by 10
Pan, T., Zhao, Z., Lu, J., Wen, H., Zhang, J., Xu, Y., Chen, Y., & Jin, X. (2023) · Life Sciences · doi:10.1016/j.lfs.2023.122321 · Cited by 12
Krokidis, M. G., Dimitrakopoulos, G. Ν., Vrahatis, A. G., Exarchos, T. P., & Vlamos, P. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1323182 · Cited by 13
Malatesta, M., Fornasier, E., di Salvo, M. L., Tramonti, A., Zangelmi, E., Peracchi, A., Secchi, A., Polverini, E., Giachin, G., Battistutta, R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47466-3 · Cited by 13
“Computational models can be used in these screenings in the absence of experimental structures 15,16 .”
Zhao, H.-F., Zhang, H., She, Z., Gao, Z., Wang, Q., Geng, Z. Z., & Dong, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032740 · Cited by 12
“Additionally, it also holds great promise for facilitating structure-based drug discoveries [42,46,47] and directing sitespecific mutagenesis [48,49].”
BackgroundCyanobacterial Phycobilisome Allostery as Revealed by Quantitative Mass Spectrometry
Liu, H. (2023) · Biochemistry · doi:10.1021/acs.biochem.3c00047 · Cited by 11
How accurately can one predict drug binding modes using AlphaFold models?Preprint
Karelina, M., Noh, J. J., & Dror, R. O. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.18.541346 · Cited by 12
Structural biology in the age of AI
Subramaniam, S. (2023) · Nature Methods · doi:10.1038/s41592-023-02123-3 · Cited by 12
Ekhtiari-Sadegh, S., Samani, S., Barneh, F., Dashtbin, S., Shokrgozar, M. A., & Bagheri, K. P. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2024.1385001 · Cited by 11
Ferdushi, R., Kim, D., Sriramulu, D. K., Hwang, Y., Park, K., & Key, J. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104315 · Cited by 11
AlphaFold3 for Structure-guided Ligand DiscoveryPreprint
Menon, K. M., Davasam, A., Chen, G., Bryant, C., Deng, Z., Lam, B., Yang, C., Barcelos, D., Liu, F., Alon, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.04.692352 · Cited by 15
Liu, Y., Luo, J., & Xu, B. (2024) · Life · doi:10.3390/life14060656 · Cited by 9
Yao, M., Cheng, Z., Xu, Y., Yue, Z., Pei, M., Liu, W., Zhang, L., Chen, X., Lei, S., Zhu, L., et al. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.176972 · Cited by 9
Wang, F., Cheng, B., Wu, W. D., Chen, Y., Dai, J., & Li, Y. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002638 · Cited by 9
Azinas, S., & Carroni, M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102621 · Cited by 12
Quinone-mediated extracellular electron transfer processes in ex situ biomethanation reactors
Palacios, P. A., Kleikamp, H. B. C., Nielsen, J. L., Jensen, M. B., Bentien, A., & Kofoed, M. V. W. (2023) · Bioresource Technology Reports · doi:10.1016/j.biteb.2023.101671 · Cited by 10
Advances and Challenges in Molecular Docking Applied to Neglected Tropical Diseases
de Angelo, R. M., Nascimento, L. A., Encide, J. P. P., Barbosa, H., Lago, J. H. G., Emery, F. D. S., & Honório, K. M. (2025) · Current Medicinal Chemistry · doi:10.2174/0109298673327352240930040103 · Cited by 11
Wang, J., Zhang, R., Zhao, X., Zhang, J., Tong, Y., Abbas, Z., Li, Z., Zhang, H., Si, D., & Wei, X. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26020505 · Cited by 8
Randeni, N., Luo, J., Wu, Y., & Xu, B. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26115202 · Cited by 8
Metadata
- Authors
- Wong, F., Krishnan, A., Zheng, E. J., Stärk, H., Manson, A. L., Earl, A. M., Jaakkola, T., & Collins, J. J.
- Affiliations
- Broad Institute (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Corresponding author
- Collins, James J
- ORCID
- Venue
- Molecular Systems Biology
- Published
- 2022-09-01
- Volume
- 18
- Issue
- 9
- Pages
- e11081-e11081
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC9446081
- OpenAlex
- W4294719209
- Cited by
- 284
- References
- 52
- Citations by year
- 2026: 61 · 2025: 85 · 2024: 70 · 2023: 60 · 2022: 8
- FWCI
- 30.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- Broad Institute of MIT and Harvard
- Flu Lab, LLC
- HHS¦NIH¦National Institute of Allergy and Infectious Diseases (U19AI110818, R01‐AI146194)
- HHS¦NIH¦National Institute of General Medical Sciences (P41‐GM103311)
- James S. McDonnell Foundation
- Rosamund Zander and Hansjorg Wyss
- Sea Grape Foundation
- Swiss National Science Foundation (SNSF_ 203071)
- The Audacious Project
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- AlphaFold2, molecular docking, protein-ligand interactions, antibiotic discovery, antibacterial agents, machine learning, drug target prediction, benchmarking
- MeSH terms
- Anti-Bacterial Agents, Ligands, Protein Binding, Proteins, Benchmarking, Molecular Docking Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
