AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

Matplotlib: a 2D graphics environment

Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249

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

Array programming with NumPy

Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826

Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy

Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057

Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen

Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763

Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.

Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300687e · Cited by 2,412

A semiempirical free energy force field with charge‐based desolvation

Huey, R., Morris, G. M., Olson, A. J., & Goodsell, D. S. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20634 · Cited by 2,246

AutoDock4Zn: An Improved AutoDock Force Field for Small-Molecule Docking to Zinc Metalloproteins

Santos-Martins, D., Forli, S., Ramos, M. J., & Olson, A. J. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500209e · Cited by 318

Covalent docking using autodock: two-point attractor and flexible side chain methods.

Bianco, G., Forli, S., Goodsell, D. S., & Olson, A. J. (2016) · Protein Science · doi:10.1002/pro.2733 · Cited by 246

D3R grand challenge 2015: evaluation of protein-ligand pose and affinity predictions.

Gathiaka, S., Liu, S., Chiu, M., Yang, H., Stuckey, J. A., Kang, Y. N., Delproposto, J., Kubish, G., Dunbar, J. B., Carlson, H. A., et al. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9946-8 · Cited by 207

Generalized biomolecular modeling and design with RoseTTAFold All-Atom

Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2024) · Science · doi:10.1126/science.adl2528 · Cited by 978

Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery

Wong, F., Krishnan, A., Zheng, E. J., Stärk, H., Manson, A. L., Earl, A. M., Jaakkola, T., & Collins, J. J. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211081 · Cited by 284

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed

Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136

AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges

Schauperl, M., & Denny, R. A. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00026 · Cited by 122

Animal-derived products in science and current alternatives

Duarte, A. C., Costa, E. C., Filipe, H. A. L., Saraiva, S. M., Jacinto, T., Miguel, S. P., Ribeiro, M. P., & Coutinho, P. (2023) · Biomaterials Advances · doi:10.1016/j.bioadv.2023.213428 · Cited by 117

ABLs and TMKs are co-receptors for extracellular auxin

(2023) · doi:10.1016/j.cell.2023.10.017 · Cited by 112

Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom

Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2023) · doi:10.1101/2023.10.09.561603 · Cited by 106

SurfDock is a surface-informed diffusion generative model for reliable and accurate protein–ligand complex prediction

Cao, D., Chen, M., Zhang, R., Wang, Z., Huang, M., Yu, J., Jiang, X., Fan, Z., Zhang, W., Zhou, H., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02516-y · Cited by 106

RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design

Wang, M., Hsieh, C.-Y., Wang, J., Wang, D., Weng, G., Shen, C., Yao, X., Bing, Z., Li, H., Cao, D., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00732 · Cited by 99

Architecture of chloroplast TOC–TIC translocon supercomplex

(2023) · doi:10.1038/s41586-023-05744-y · Cited by 86

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

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · doi:10.26434/chemrxiv-2022-jjm0j · Cited by 81

Structure prediction of protein-ligand complexes from sequence information with Umol

Bryant, P., Kelkar, A., Guljas, A., Clementi, C., & Noé, F. (2024) · Nature Communications · doi:10.1038/s41467-024-48837-6 · Cited by 75

Cryo-EM structure of the cytosolic AhR complex.

Wen, Z., Zhang, Y., Zhang, B., Hang, Y., Xu, L., Chen, Y., Xie, Q., Zhao, Q., Zhang, L., Li, G., et al. (2023) · Structure · doi:10.1016/j.str.2022.12.013 · Cited by 51

Antagonistic interactions between odorants alter human odor perception

Fukutani, Y., Abe, M., Saito, H., Eguchi, R., Tazawa, T., de March, C. A., Yohda, M., & Matsunami, H. (2023) · Current Biology · doi:10.1016/j.cub.2023.04.072 · Cited by 41

CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome

Wang, S., Lin, H., Huang, Z., He, Y., Deng, X., Xu, Y., Pei, J., & Lai, L. (2022) · Biomolecules · doi:10.3390/biom12070967 · Cited by 39

Protein representations: Encoding biological information for machine learning in biocatalysis

Harding-Larsen, D., Funk, J., Madsen, N. G., Gharabli, H., Acevedo-Rocha, C. G., Mazurenko, S., & Welner, D. H. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108459 · Cited by 38

Cholangiocarcinoma PDHA1 succinylation suppresses macrophage antigen presentation via alpha-ketoglutaric acid accumulation

Zhang, N., Sun, L., Zhou, S., Ji, C., Cui, T., Chu, Q., Ye, J., Liang, S., Ma, K., Liu, Y., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58429-7 · Cited by 34

Binding and sequestration of poison frog alkaloids by a plasma globulin

Alvarez-Buylla, A., Fischer, M.-T., Moya Garzon, M. D., Rangel, A. E., Tapia, E. E., Tanzo, J. T., Soh, H. T., Coloma, L. A., Long, J. Z., & O'Connell, L. A. (2023) · eLife · doi:10.7554/elife.85096 · Cited by 32

Systemic evolutionary chemical space exploration for drug discovery

Lu, C., Liu, S., Shi, W., Yu, J., Zhou, Z., Zhang, X., Lu, X., Cai, F., Xia, N., & Wang, Y. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00598-4 · Cited by 30

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

(2023) · doi:10.3390/biom13010132 · Cited by 28

Mangiferin alleviates diabetic pulmonary fibrosis in mice via inhibiting endothelial-mesenchymal transition through AMPK/FoxO3/SIRT3 axis

Fu, T.-L., Li, G.-R., Li, D.-H., He, R.-Y., Liu, B.-H., Xiong, R., Xu, C.-Z., Lu, Z.-L., Song, C.-K., Qiu, H.-L., et al. (2024) · Acta Pharmacologica Sinica · doi:10.1038/s41401-023-01202-7 · Cited by 27

Bayesian optimization for ternary complex prediction (BOTCP)

Rao, A., Tunjic, T. M., Brunsteiner, M., Müller, M., Fooladi, H., Gasbarri, C., & Weber, N. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2023.100072 · Cited by 25

Unified modeling of 3D molecular generation via atomic interactions with PocketXMol

Peng, X., Guo, R., Guo, F., Wang, Z., Sun, J., Guan, J., Jia, Y., Xu, Y., Huang, Y., Zhang, M., et al. (2026) · Cell · doi:10.1016/j.cell.2026.01.003 · Cited by 22

A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction

Wu, M.-H., Xie, Z., & Zhi, D. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01506-1 · Cited by 19

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

Estevam, G. O., Linossi, E., Rao, J., Macdonald, C. B., Ravikumar, A., Chrispens, K. M., Capra, J. A., Coyote-Maestas, W., Pimentel, H., Collisson, E. A., et al. (2025) · eLife · doi:10.7554/elife.101882 · Cited by 17

Discovery of a heparan sulfate binding domain in monkeypox virus H3 as an anti-poxviral drug target combining AI and MD simulations

(2024) · doi:10.7554/elife.100545 · Cited by 16

Pattern-Based Genome Mining Guides Discovery of the Antibiotic Indanopyrrole A from a Marine Streptomycete

Sweeney, D., Bogdanov, A., Chase, A. B., Castro-Falcón, G., Trinidad-Javier, A., Dahesh, S., Nizet, V., & Jensen, P. R. (2024) · Journal of Natural Products · doi:10.1021/acs.jnatprod.4c00934 · Cited by 16

In-insect synthesis of oxygen-doped molecular nanocarbons

Usami, A., Kono, H., Austen, V., Phung, Q. M., Shudo, H., Kato, T., Yamada, H., Yagi, A., Amaike, K., Fujimoto, K. J., et al. (2025) · Science · doi:10.1126/science.adp9384 · Cited by 15

Quinone extraction drives atmospheric carbon monoxide oxidation in bacteria

(2025) · doi:10.1038/s41589-025-01836-0 · Cited by 15

Structural basis for triacylglyceride extraction from mycobacterial inner membrane by MFS transporter Rv1410

(2023) · doi:10.1038/s41467-023-42073-0 · Cited by 14

Photocobilins integrate B12 and bilin photochemistry for enzyme control

Zhang, S., Jeffreys, L. N., Poddar, H., Yu, Y., Liu, C., Patel, K., Johannissen, L. O., Zhu, L., Cliff, M. J., Yan, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46995-1 · Cited by 13

A Putative Binding Model of Nitazene Derivatives at the μ-Opioid Receptor

Clayton, J., Shi, L., Robertson, M. J., Skiniotis, G., Michaelides, M., Stavitskaya, L., & Shen, J. (2025) · Neuropharmacology · doi:10.1016/j.neuropharm.2025.110437 · Cited by 12

Specificity and mechanism of TonB-dependent ferric catecholate uptake by Fiu

Yang, T., Zou, Y., Ng, H. L., Kumar, A., Newton, S. M., & Klebba, P. E. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1355253 · Cited by 11

Fisetin as a Blueprint for Senotherapeutic Agents – Elucidating Geroprotective and Senolytic Properties with Molecular Modeling

Spiegel, M. (2025) · Chemistry – A European Journal · doi:10.1002/chem.202403755 · Cited by 12

3D-MINFLUX nanoscopy reveals distinct allosteric mechanisms for activation and modulation of PIEZO1 by Yoda1

Verkest, C., Roettger, L., Zeitzschel, N., & Lechner, S. G. (2025) · Nature Communications · doi:10.1038/s41467-025-67610-x · Cited by 12

Proteome profiling of enriched membrane-associated proteins unraveled a novel sophorose and cello-oligosaccharide transporter in Trichoderma reesei

Nogueira, K. M. V., Mendes, V., Kamath, K. S., Cheruku, A., Oshiquiri, L. H., de Paula, R. G., Carraro, C., Pedersoli, W. R., Pereira, L. M. S., Vieira, L. C., et al. (2024) · Microbial Cell Factories · doi:10.1186/s12934-023-02279-9 · Cited by 12

Computational prediction of high-risk non-synonymous SNPs in human ApoE and their structural impact on amyloid-β interaction in Alzheimer’s disease pathogenesis

Hossain, M. M., Adhikari, J., Dutta, A., Khandaker, A., Islam, S., Rahman, M. M., & Shibly, A. Z. (2025) · PLOS One · doi:10.1371/journal.pone.0331339 · Cited by 11

Comprehensive analysis of MAPK genes in the prognosis, immune characteristics, and drug treatment of renal clear cell carcinoma using bioinformatic analysis and Mendelian randomization

Zheng, X., Wang, Y., & Qiu, X. (2024) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2024.176840 · Cited by 10

Decoding substrate specificity determining factors in glycosyltransferase-B enzymes – insights from machine learning models

Hennen, S. G., Bomble, Y. J., Urbanowicz, B. R., & Bharadwaj, V. S. (2025) · Digital Discovery · doi:10.1039/d4dd00338a · Cited by 9

Reversible Control of Native GluN2B-Containing NMDA Receptors with Visible Light

Geoffroy, C., Berraud-Pache, R., Chéron, N., McCort-Tranchepain, I., Doria, J., Paoletti, P., & Mony, L. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.4c00247 · Cited by 9

Computational investigation unveils pathogenic LIG3 non-synonymous mutations and therapeutic targets in acute myeloid leukemia

Hossen, M. A., Jahan, U. M. S., Hossain, M. A., Asif, K. H., Rahman, A., Ahmed, S., Uddin, M. M., Amin, M. F., Barik, M. A., Islam, M. J., et al. (2025) · PLOS One · doi:10.1371/journal.pone.0320550 · Cited by 8

OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer

Xu, G., Luo, Z., Yan, Y., Wang, Q., & Ma, J. (2024) · Structure · doi:10.1016/j.str.2024.03.015 · Cited by 8

EquiCPI: SE(3)-Equivariant Geometric Deep Learning for Structure-Aware Prediction of Compound-Protein Interactions

Nguyen, N.-Q., & Kang, J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00773 · Cited by 7

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method

Stevenson, G. A., Kirshner, D., Bennion, B. J., Yang, Y., Zhang, X., Zemla, A., Torres, M. W., Epstein, A., Jones, D., Kim, H., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00722 · Cited by 7

Chalkophomycin Biosynthesis Revealing Unique Enzyme Architecture for a Hybrid Nonribosomal Peptide Synthetase and Polyketide Synthase

(2024) · doi:10.3390/molecules29091982 · Cited by 7

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

Estevam, G. O., Linossi, E., Rao, J., Macdonald, C. B., Ravikumar, A., Chrispens, K. M., Capra, J. A., Coyote-Maestas, W., Pimentel, H., Collisson, E. A., et al. (2025) · eLife · doi:10.7554/elife.101882.3 · Cited by 6

The carRS-ompV-virK operon of Vibrio cholerae senses antimicrobial peptides and activates the expression of multiple resistance systems

(2025) · doi:10.1038/s41598-025-98217-3 · Cited by 6

Discovery and mechanistic characterization of a probiotic‐origin 3β‐ OH ‐Δ 5‐6 ‐cholesterol‐5β‐reductase directly converting cholesterol to coprostanol

Netter, U., Bisht, V., Gaurav, A., Sharma, R., Ghosh, A., Bisht, V. S., Ambatipudi, K., Sharma, H. P., Mohanty, S., Loat, S., et al. (2025) · The FEBS Journal · doi:10.1111/febs.70131 · Cited by 6

Discovery and Heterologous Expression of Functional 4- O -Dimethylallyl- l -tyrosine Synthases from Lichen-Forming Fungi

Iacovelli, R., He, S., Sokolova, N., Lokhorst, I., Borg, M., Fodran, P., & Haslinger, K. (2024) · Journal of Natural Products · doi:10.1021/acs.jnatprod.4c00619 · Cited by 5

Uncovering the Elusive Structures and Mechanisms of Prevalent Antidepressants

Lin, J., Bu, G., Unge, J., & Gonen, T. (2024) · Advanced Therapeutics · doi:10.1002/adtp.202400117 · Cited by 5

Characterization and Diversification of AraC/XylS Family Regulators Guided by Transposon Sequencing

Pearson, A. N., Incha, M. R., Ho, C. N., Schmidt, M., Roberts, J. B., Nava, A. A., & Keasling, J. D. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00441 · Cited by 5

Voltage gating and 4-aminopyridine inhibition in the Shaker Kv channel revealed by a closed-state model

Pinto-Anwandter, B. I. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2025.06.029 · Cited by 5

Ion Transport and Inhibitor Binding by Human NHE1: Insights from Molecular Dynamics Simulations and Free Energy Calculations

(2024) · doi:10.1021/acs.jpcb.3c05863 · Cited by 4

Guiding discovery of protein sequence-structure-function modeling

(2024) · doi:10.1093/bioinformatics/btae002 · Cited by 4

Structures of Native Doublet Microtubules from Trichomonas vaginalis Reveal Parasite-Specific Proteins

Stevens, A., Kashyap, S., Crofut, E. H., Wang, S. E., Muratore, K. A., Johnson, P. J., & Zhou, Z. H. (2025) · Nature Communications · doi:10.1038/s41467-025-59369-y · Cited by 4

Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology

(2025) · doi:10.1371/journal.pcbi.1013216 · Cited by 4

Specific Mutations Reverse Regulatory Effects of Adenosine Phosphates and Increase Their Binding Stoichiometry in CBS Domain-Containing Pyrophosphatase

Anashkin, V. A., Kirillova, E. A., Orlov, V. N., & Baykov, A. A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25115768 · Cited by 3

DockFormer: Affinity Prediction and Flexible Docking with Pair Transformer

(2025) · doi:10.1103/1862-rs2k · Cited by 3

BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors

Dai, J., Zhang, Y., Shi, C., Liu, Y., Xiu, P., & Wang, Y. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c06729 · Cited by 3

Structure and post-translational modification of the prostaglandin transporter

Yu, P., Orlando, M. A., & Orlando, B. J. (2025) · Communications Biology · doi:10.1038/s42003-025-08510-0 · Cited by 3

Fucose as a nutrient ligand for Dikarya and a building block of early diverging lineages

(2023) · doi:10.1186/s43008-023-00123-8 · Cited by 3

Analysis of NUDIX enzymes across fungi reveals previously unrecognized diversity

Pasterny, Z., Barua, D., Mancera, E., & Muszewska, A. (2025) · BMC Genomics · doi:10.1186/s12864-025-11778-5 · Cited by 2

Harnessing Photosynthetic ATP for Whole-Cell Biocatalysis in the Cyanobacterium Synechocystis

Loprete, G., Traverso, E., Vascon, F., Botteri, M., Robescu, M. S., Ubiali, D., Cendron, L., Morosinotto, T., & Bergantino, E. (2025) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.5c07236 · Cited by 2

Visualizing the mechanism of quinol oxidation and inhibition of a bd -type oxidase using cryo-EM

van der Velden, T. T., Kayastha, K., Pelser, F., Brünle, S., & Jeuken, L. J. C. (2026) · Science Advances · doi:10.1126/sciadv.aec9946 · Cited by 2

The flexible stalk domain of sTREM2 modulates its interactions with brain-based phospholipids

(2025) · doi:10.7554/elife.102269.3 · Cited by 2

1,2,4-Triazole-Based First-in-Class Non-Nucleoside Inhibitors of the Bacterial Enzyme MraY

Berida, T., Huang, T.-Y., Weck, S. C., Lutz, M., McKee, S. R., Kagerah, N., Manning, D. L., Jahan, M. E., Mishra, S., Herrmann, J., et al. (2025) · ACS Bio & Med Chem Au · doi:10.1021/acsbiomedchemau.5c00158 · Cited by 2

The interplay between glucose and aromatic compound regulation by two IclR-type transcription factors, LigR1 and LigR2, in Pseudomonas putida KT2440

Kadriu, E., Qin, S., Prezioso, S. M., & Christendat, D. (2026) · Microbiological Research · doi:10.1016/j.micres.2025.128382 · Cited by 2

De novo protein–ligand design including protein flexibility and conformational adaptation

Agamia, J., & Zacharias, M. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag027 · Cited by 2

Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening

Uchikawa, K., Furui, K., & Ohue, M. (2025) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2025.102110 · Cited by 1

HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses

Kravchenko, A., de Vries, S. J., Smaïl-Tabbone, M., & Chauvot de Beauchene, I. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05733-6 · Cited by 1

Efficacy and mechanism of action of cipargamin as an antibabesial drug candidate

Li, H., Ji, S., Ariefta, N. R., Galon, E. M. S., El-Sayed, S. A., Do, T., Jia, L., Sakaguchi, M., Asada, M., Nishikawa, Y., et al. (2025) · eLife · doi:10.7554/elife.101128 · Cited by 1

Efficacy and mechanism of action of cipargamin as an antibabesial drug candidate

Li, H., Ji, S., Ariefta, N. R., Galon, E. M. S., El-Sayed, S. A., Do, T., Jia, L., Sakaguchi, M., Asada, M., Nishikawa, Y., et al. (2025) · eLife · doi:10.7554/elife.101128.4 · Cited by 1

DisDock : A Deep Learning Method for Metal Ion‐Protein Redocking

Lin, M., Li, K., Zhang, Y., Pan, F., Wu, W., & Zhang, J. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26791 · Cited by 1

Directed evolution of a cytochrome P450 monooxygenase for improved perillyl alcohol biosynthesis via a tailored genetically encoded biosensor

Odhiambo, C. A., Malico, A. A., & Williams, G. J. (2026) · RSC Advances · doi:10.1039/d6ra03129c · Cited by 1

Metagenome mining and functional analysis reveal oxidized guanine DNA repair at the Lost City Hydrothermal Field

(2024) · doi:10.1371/journal.pone.0284642 · Cited by 1

Regional Adipose Distribution Reveals Differential Genetic Risk Patterns for Cardiometabolic Diseases

Wu, Q., Yang, F., An, Q., Liu, Y., Tian, H., Du, Y., Cai, W., Lei, M., Xu, J., Deng, L., et al. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.126.049987

CR1(+) tumor-associated macrophages orchestrate an immunosuppressive niche in hepatocellular carcinoma: a genetic and multi-omics dissection

Wang, Z., Wang, Z., Ji, X., Zhao, L., Zheng, K., Yu, W., Zhang, H., Chang, H., & Liu, F. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-08301-z

SenolyticSynergy: An Attention-Based Network for Discovering Novel Senolytic Combinations via Human Aging Genomics

Ye, Y., Su, T., Gao, J., & Ming, D. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26189004

Investigating the ATP-binding pocket of CX3CL1-binding protein 2 using in silico approach

Kumari, R., Kaur, S., Hora, R., & Mishra, P. C. (2024) · Journal of Proteins and Proteomics · doi:10.1007/s42485-024-00133-z

Convergent evolution of distinct D-ribulose utilisation pathways in attaching and effacing pathogens

Cottam, C., Bowran, K., White, R. T., Baslé, A., Josts, I., & Connolly, J. P. R. (2025) · Nature Communications · doi:10.1038/s41467-025-62476-5

Integrative network-based construction of ecologically relevant computational adverse outcome pathways for organic mercury-induced toxicity

Madgaonkar, S. R., Chivukula, N., Garisetti, V., Marigoudar, S. R., Sharma, K. V., & Samal, A. (2026) · Environmental Toxicology and Chemistry · doi:10.1093/etojnl/vgag026

Trichoderma swollenin activates AtABCB5-dependent auxin efflux to promote plant development

Lu, J., Liu, J., Jiang, S., Xu, Y., Yan, Z., Li, L., Zhang, F., Chen, W., Xu, D., Li, Y., et al. (2026) · Cell · doi:10.1016/j.cell.2026.07.001

Analysis of NUDIX enzymes across fungi reveals previously unrecognized diversity

(2025) · doi:10.21203/rs.3.rs-6343747/v1