Cited by 193Computational Drug Discovery MethodsChemical Synthesis and AnalysisSynthesis and Characterization of Heterocyclic Compounds

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

Modeling the expansion of virtual screening libraries

Open access

Lyu, Jiankun, Irwin, John J., Shoichet, Brian K.·2023·Nature Chemical Biology

DOI 10.1038/s41589-022-01234-wPMC PMC10243288

Published version of a preprint

Open this paper in CoChat — free account Open PDF

Abstract

Recently, 'tangible' virtual libraries have made billions of molecules readily available. Prioritizing these molecules for synthesis and testing demands computational approaches, such as docking. Their success may depend on library diversity, their similarity to bio-like molecules and how receptor fit and artifacts change with library size. We compared a library of 3 million 'in-stock' molecules with billion-plus tangible libraries. The bias toward bio-like molecules in the tangible library decreases 19,000-fold versus those 'in-stock'. Similarly, thousands of high-ranking molecules, including experimental actives, from five ultra-large-library docking campaigns are also dissimilar to bio-like molecules. Meanwhile, better-fitting molecules are found as the library grows, with the score improving log-linearly with library size. Finally, as library size increases, so too do rare molecules that rank artifactually well. Although the nature of these artifacts changes from target to target, the expectation of their occurrence does not, and simple strategies can minimize their impact.

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

Crossref · 10.1038/s41589-022-01234-w

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(96)00423-1 · Cited by 11,484
Angewandte Chemie International Edition
Vazdar, K., Kunetskiy, R. A., Saame, J., Kaupmees, K., Leito, I., & Jahn, U. (2006) · Angewandte Chemie International Edition · doi:10.1002/(issn)1521-3773 · Cited by 5,242
The evolving role of natural products in drug discovery
Koehn, F. E., & Carter, G. T. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1657 · Cited by 2,579
The Properties of Known Drugs. 1. Molecular Frameworks
Bemis, G. W., & Murcko, M. A. (1996) · Journal of Medicinal Chemistry · doi:10.1021/jm9602928 · Cited by 2,501
ChEMBL: towards direct deposition of bioassay data
Mendez, D., Gaulton, A., Bento, A. P., Chambers, J., De Veij, M., Félix, E., Magariños, M. P., Mosquera, J. F., Mutowo, P., Nowotka, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1075 · Cited by 2,372
Discovery and development of sorafenib: a multikinase inhibitor for treating cancer
Wilhelm, S., Carter, C. A., Lynch, M., Lowinger, T., Dumas, J., Smith, R., Schwartz, B., Simantov, R., & Kelley, S. (2006) · Nature reviews. Drug discovery · doi:10.1038/nrd2130 · Cited by 1,651
Molecular signatures of G-protein-coupled receptors
Venkatakrishnan, A., Deupí, X., Lebon, G., Tate, C. G., Schertler, G. F. X., & Babu, M. M. (2013) · Nature · doi:10.1038/nature11896 · Cited by 1,534
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2005) · Chemical & Engineering News · doi:10.1021/cen-v083n019.p031 · Cited by 1,534
Impact of high-throughput screening in biomedical research
Macarron, R., Banks, M. N., Bojanic, D., Burns, D. J., Cirovic, D. A., Garyantes, T., Green, D. V. S., Hertzberg, R. P., Janzen, W. P., Paslay, J. W., et al. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3368 · Cited by 1,364

Show all 53 references

Research works printed but not linked: 1

Lipinski, C. A. Physicochemical properties and the discovery of orally active drugs: technical and people issues. In Molecular Informatics: Confronting Complexity, Proceedings of the Beilstein-Institut Workshop (Frankfurt, 2003).

Sorted by citations

Items 101–192 of 192

A goldilocks computational protocol for inhibitor discovery targeting DNA damage responses including replication-repair functions
Moiani, D., & Tainer, J. A. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2024.1442267 · Cited by 3
AI-assisted design of next-generation antibiotics against multidrug-resistant Staphylococcus aureus
Zheng, Y., Jin, F., Chang, Q., Yu, J., Zheng, Y., & Li, Y. (2026) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2026.109092 · Cited by 3
In silico induction of missense mutation in NNRTI protein: computational modelling and stability study of modelled proteins
Sule, L., Gupta, S., Jain, N., & Sapre, N. S. (2023) · Journal of Mathematical Chemistry · doi:10.1007/s10910-023-01545-w · Cited by 3
Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures
Shim, H., Allen, J., & Bennett, W. F. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6010030 · Cited by 3
Identification of A Potential Inhibitor for Anticancer Target MTHFD2 by Consensus Docking and Molecular DynamicsPreprint
Zhou, H., Hong, Y., & Beckford, F. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.09.566353 · Cited by 3
A Mechanism to Open Academic Chemistry to High-Throughput Virtual ScreeningPreprint
Bedart, C., Shimokura, G. K., West, F. G., Wood, T. E., Batey, R. A., Irwin, J. J., & Schapira, M. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-jgbgv · Cited by 3
Self-citation
Small vs. Large Library Docking for Positive Allosteric Modulators of the Calcium Sensing ReceptorPreprint
Liu, F., Wu, C.-G., Tu, C., Glenn, I., Meyerowitz, J., Kaplan, A. L., Lyu, J., Cheng, Z., Tarkhanova, O. O., Moroz, Y. S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.27.573448 · Cited by 3
Self-citation
Docking 14 million virtual isoquinuclidines against the mu and kappa opioid receptors reveals dual antagonists-inverse agonists with reduced withdrawal effectsPreprint
Vigneron, S. F., Ohno, S., Braz, J. M., Kim, J. Y., Kweon, O. S., Webb, C. M., Billesbølle, C. B., Bhardwaj, K., Irwin, J. J., Manglik, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.09.632033 · Cited by 3
Self-citation
Assessing the Robustness and Scalability of Machine Learning Methods to Accelerate Ultralarge High-Throughput Docking Campaigns
Filippo, J. I. D., Rómoli, S., & Cavasotto, C. N. (2025) · ACS Omega · doi:10.1021/acsomega.5c00829 · Cited by 2
Machine learning-based structural analysis of OATP1B1 interactors/non-interactors: Discriminating toxic and non-toxic alerts for transporter-mediated toxicity
Gayen, S., Dasgupta, I., Ghosh, B., Qureshi, I. A., & Roy, P. P. (2025) · Computational Toxicology · doi:10.1016/j.comtox.2025.100373 · Cited by 2
Developing inhibitors of the guanosine triphosphate hydrolysis accelerating activity of Regulator of G protein Signaling-14
Agogo-Mawuli, P. S., Sadiya, I., Abramyan, T. M., Bosch, D. E., Emmitte, K. A., Colón-Pérez, L. M., Kosloff, M., & Siderovski, D. P. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110611 · Cited by 2
Structure-based discovery of inhibitors of Mac1 domain of nonstructural protein-3 of SARS-CoV-2 by machine learning-augmented screening of chemical space
Ban, F., Ravichandran, R., Correy, G. J., Herasymenko, O., Silva, M., Ackloo, S. Z., Bolotokova, A., Chau, I., Gibson, E., Harding, R., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-65034-1 · Cited by 3
Identification of potent high-affinity secondary nucleation inhibitors of Aβ42 aggregation from an ultra-large chemical library using deep docking
Brezinova, M., Brotzakis, Z. F., Horne, R. I., Chowdhury, V. R., Gregory, R. C., Bian, Y., Díaz, A. G., Gentile, F., & Vendruscolo, M. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00159-5 · Cited by 3
Trillion Ligands per Day: Performance-Portable Virtual Screening via Compound Database Optimization and Multi-Target Docking
Duan, X., Shen, C., Chen, G., Wu, S., Wang, Y., Chen, Y., Chang, Q., Xia, Q., Yin, Z., Gan, L., et al. (2025) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3712285.3759833 · Cited by 2
Early AI-driven repurposing study of existing drugs towards the vasopressin V2 receptor
Trisciuzzi, D., Angelini, I., Gambacorta, N., Centrone, M., Ciriaco, F., Siragusa, L., Mise, A. D., Ranieri, M., Valenti, G., Guaragnella, N., et al. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111428 · 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
Docking of millions: accelerating a million-scale virtual screening using deep learning
Ha, J., Lee, J., Ko, J., & Shin, W. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag128 · Cited by 2
Docking-based virtual screening: Past, present, and future
Xu, X., & Zou, X. (2026) · Biophysical Journal · doi:10.1016/j.bpj.2026.04.011 · Cited by 3
Geometric Deep Learning‐Based Drug Design Models for Small‐Molecule Drug Discovery
Srivastav, A. K., Modi, U., Kumar, R., Bhatia, D. D., & Solanki, R. (2026) · Molecular Informatics · doi:10.1002/minf.70047 · Cited by 2
D3Rings: A Fast and Accurate Method for Ring System Identification and Deep Generation of Drug-Like Cyclic Compounds
Ma, M., Zhang, X., Zhou, L., Han, Z., Shi, Y., Li, J., Wu, L., Xu, Z., & Zhu, W. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01657 · Cited by 2
Ultra-Large-Scale Virtual Screening
Pöhner, I., Sivula, T., & Poso, A. (2024) · Computer-aided drug discovery and design · doi:10.1007/978-3-031-76718-0_11 · Cited by 2
Structure‐Based Ultra‐Large Virtual Screenings
Gorgulla, C. (2024) · Wiley eBooks · doi:10.1002/9783527840748.ch19 · Cited by 2
NGT: Generative AI with Synthesizability Guarantees Identifies Potent Inhibitors for a G-protein Associated Melanocortin Receptor in a Tera-scale vHTS ScreenPreprint
de Oliveira, S. H. P., Pedawi, A., Kenyon, V. A., & van den Bedem, H. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-fz37h · Cited by 1
Identification of Antituberculars with Favorable Potency and Pharmacokinetics through Structure-Based and Ligand-Based ModelingPreprint
Warapande, V., Meng, F., Bozan, A., Graff, D., Fromer, J. C., Mughal, K., Mohideen, F. K., Shivangi, S., Paruchuri, S., Johnston, M. L., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.03.636334 · Cited by 3
Application of computation in creating dereplicated phytochemical libraries
Nahar, L., & Sarker, S. D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-16102-5.00006-7 · Cited by 1
Autoparty: Machine Learning-Guided Visual Inspection of Molecular Docking Results
Shub, L., Korczynska, M., Muir, D. F., Lin, F., Hall, B. W., Mathiowetz, A. M., & Keiser, M. J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00850 · Cited by 1
Comparative analysis for optimal LSD1 inhibitors evaluation techniques: pros and cons
Yin, Q., Ma, C., Zhao, X., Wang, P., Shen, D., Ren, C., Wang, B., Li, F., Yang, Y. H., Liu, H., et al. (2025) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2025.101430 · Cited by 1
A reaction enumeration analysis of building blocks to target novel coupling methods
Mahjour, B. A., Zhang, R., Outlaw, A., Katzenburg, F., Abdelalim, M., Zhang, X., Harmata, A. S., & Cernak, T. (2025) · Tetrahedron Letters · doi:10.1016/j.tetlet.2025.155838 · Cited by 1
Controllable molecular generation with fine-tuned flow-matching model
Wang, K., Janet, J. P., & Tibo, A. (2026) · Communications Chemistry · doi:10.1038/s42004-026-02188-z · Cited by 2
Large Library Docking for Polypharmacology
Wu, Y., Vigneron, S. F., Bráz, J. M., Srinivasan, K., Fink, E. A., Huang, X.-P., Xu, X., Huebner, H., Kim, J. Y., Wang, J., et al. (2026) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c03810 · Cited by 1
Self-citation
UniDock-Pro: A Unified GPU-Accelerated Platform for High-Throughput Structure-Based, Ligand-Based, and Synergistic Hybrid Virtual Screening
Ni, B., & Houston, D. R. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02587 · Cited by 1
Computational Innovations in Cancer Research and How Computing is Transforming Drug Discovery and Development: A Review
Lytan, I., Singha, B., Boruah, N., Gogoi, P. P., Longkumer, P., Pucho, M., Dkhar, B., & Sinha, U. B. (2026) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/0113895575418750251204104229 · Cited by 1
Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform
Gu, S., Zhang, X., Xiao, M., Qian, Y., Liu, B., Luo, H., Du, H., Zhang, O., Mou, M., Fu, T., et al. (2026) · Nature Protocols · doi:10.1038/s41596-026-01389-z
Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition
Moesgaard, L., Shoichet, B. K., & Mailhot, O. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01066 · Cited by 1
Self-citation
The inhibition mechanism of quercetin targeting β-Lactamases OXA-98 based on molecular dynamics simulation
Yang, Y., Song, J., Gao, Y., Li, D., Li, X., & Liu, Y. (2023) · Chemical Physics Letters · doi:10.1016/j.cplett.2023.140596 · Cited by 1
Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK
Chakrabarti, M., Tan, Y. S., & Balius, T. E. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3822-4_6 · Cited by 1
Increase Docking Score Screening Power by Simple Fusion With CNNscore
Liang, H., Xie, A., Hou, N., Wei, F., Gao, T., Li, J., Gao, X., Shi, C., Xiao, G., & Xu, X. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70060 · Cited by 1
Parallelized Tools for the Preparation and Curation of Large Libraries for Virtual Screening
Vitalis, A., Parker, D., Radler, F., Vargas-Rosales, P. A., Zhang, Y., Marchand, J., & Caflisch, A. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70074 · Cited by 2
NGT: Generative AI with Synthesizability Guarantees Identifies Potent Inhibitors for a G-protein Associated Melanocortin Receptor in a Tera-scale vHTS ScreenPreprint
de Oliveira, S. H. P., Pedawi, A., Kenyon, V. A., & van den Bedem, H. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-fz37h-v2 · Cited by 1
NRGRank: Coarse-grained structurally-informed ultra-massive virtual screeningPreprint
DesCôteaux, T., Mailhot, O., & Najmanovich, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.17.638675 · Cited by 1
A community effort to discover small molecule SARS-CoV-2 inhibitorsPreprint
Schimunek, J., Seidl, P., Elez, K., Hempel, T., Le, T., Noé, F., Olsson, S., Raich, L., Winter, R., Gökcan, H., et al. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-1d5w8 · Cited by 1
Correlation of binding site properties with chemistries used for generation of ultra-large virtual libraries.Preprint
Song, R., Nicklaus, M. C., & Tarasova, N. I. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-hs7ql
ASHA:A Chemical Sapce Navigator app for Dual–Target-Directed-Inhibitor designPreprint
Kumar, A. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-kpx9w
Protein structure-based organic chemistry-driven ligand design from ultra-large chemical spacesPreprint
Rognan, D., Sindt, F., Seyller, A., & Eguida, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3687338/v1
NGT: Generative AI with Synthesizability Guarantees Identifies Potent Inhibitors for a G-protein Associated Melanocortin Receptor in a Tera-scale vHTS ScreenPreprint
de Oliveira, S. H. P., Pedawi, A., Kenyon, V. A., & van den Bedem, H. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-fz37h-v3
Algorithms for Structure Comparison and Analysis: Docking
Giordano, D., D’Arminio, N., Marabotti, A., & Facchiano, A. M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00149-4
Synthesis of Silyl Heterocycles Using Transborylation
Willcox, D. R. (2024) · Synlett · doi:10.1055/a-2500-6758
AI-driven antimicrobial peptides for drug development
Arora, Y., Lalwani, H., Kumar, A., Mathur, P., Chaturvedi, N., & Mishra, V. (2025) · Methods in microbiology · doi:10.1016/bs.mim.2024.08.003
Efficiency in Medicinal Chemistry via Computational Chemistry
Rajapriyan, M., Moydeen, A. M., & Padusha, M. S. A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-5598-5.ch010
Genetic-to-Chemical Perturbation Transfer Learning Through Unified Multimodal Molecular RepresentationsPreprint
Li, Y., Zeng, M., Zhu, J., Liu, L., Wang, F., Huang, L.-K., Yang, F., Li, M., & Yao, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.02.635055
The concept of ‘nature’ in chemistry in a digital and ecological age
Schnurr, C. (2025) · Foundations of Chemistry · doi:10.1007/s10698-025-09536-2
PayloadGenX, a multi-stage hybrid virtual screening approach for payload design: A microtubule inhibitor case study
Ahmed, F., Samantasinghar, A., Sunildutt, N., & Choi, K. H. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108439
SAIAME : Semi‐Parameter Adaptation Information‐Assisted Multi‐Objective Evolutionary for Protein‐Ligand Docking
Xiao, W., Shu, H., Chen, X., Li, W., & Ren, J. (2025) · Chemical Biology & Drug Design · doi:10.1111/cbdd.70094
Identification of pyrazole scaffold inhibitors targeting cyclin-dependent kinase 8 for potential use in pulmonary fibrosis
Lin, T. E., Hsu, K., Chou, C.-H., Tsai, E.-Y., Wu, Y., Sung, T.-Y., Hsu, J., Hsieh, J., Yen, S., Chang, Y.-W., et al. (2025) · Biochemical Pharmacology · doi:10.1016/j.bcp.2025.116959
Computational Screening of Natural Compounds Targeting VEGFR-2 for Anti-Angiogenic Therapy
Abbas, K., Alam, M., Raza, M. T., Shariq, M., Ahmad, H., Farooqi, F., Mustafa, M., Habib, S., & Usmani, N. (2025) · Current Science Engineering and Technology · doi:10.2174/0122102981375849250623091708
Discovery of Novel Isofunctional SARS-CoV-2 NSP14 RNA Cap Methyltransferase Inhibitors by Structure-Based Virtual Screening
Meyer, C., Michino, M., Huggins, D. J., Garzia, A., Davis, J. A., Miller, M. W., Liverton, N. J., Hoffmann, H.-H., Glickman, J. F., Nitsche, J., et al. (2025) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.5c00339
Accelerated Drug Discovery through In Silico Study and Multicomponent Reactions
Sutanto, F., Krisli, A. R., Wirawan, C., Cahya, R. J., Aliyansah, P. I., & Hartrianti, P. (2025) · Current Drug Therapy · doi:10.2174/0115748855399861250908113204
Phenotypic AI-based design of cell-specific small molecule cytotoxics
Rojas-Granado, G., Sánchez‐Soto, M., Calahorra, J., Caballero, M., Ramos, I., Bertoni, M., & Aloy, P. (2026) · Communications Chemistry · doi:10.1038/s42004-026-02071-x
LEVERAGING ARTIFICIAL INTELLIGENCE IN MEDICINAL CHEMISTRY: ADVANCES IN DRUG SYNTHESIS, DEVELOPMENT AND DESIGN
Shastri, M. M., RAUT, S., TAKALKAR, V., & Kshirsagar, S. (2025) · International Journal of Pharmacy and Pharmaceutical Sciences · doi:10.22159/ijpps.2025v17i11.56256
Acoustic Droplet Ejection High-Resolution Mass Spectrometry Enables High-Throughput Analysis of Proteins in Complex Biologic and Vaccine Matrices
Laro, J. A., Barrientos, R. C., Larson, E. J., Regalado, E. L., Appiah‐Amponsah, E., Liu, C., Gouker, J. R., Makey, D. M., Hu, H., & Ukaegbu, O. I. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.6c03005
In Silico identification of taxane analogs as potentially-safer, chemoresistance-evading candidates for breast cancer
Guidotti, I. L., & Kremer, F. S. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100179
Fragment-Guided New Therapeutic Molecule Discovery and Mapping of Clinically Relevant Interactomes
Kanapeckaitė, A., Okuyan, S., Wagg, D., Köster, J., Jančorienė, L., Sakalauskaitė, I., Brasiūnienė, B., & Townsend‐Nicholson, A. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02503
Strategies Rooted in Quantum Mechanics for Drug Discovery: Keeping Pace with Novel Hurdles in Pharmaceutical Design
Eriksson, J., & Holm, K. (2023) · Pharmaceutical Sciences and Drug Design · doi:10.51847/jhgy5h43t8
GNN-MA: Soft Molecular Alignment with Cross-Graph Attention for Ligand-Based Virtual Screening
Liu, K., Wei, D., Shi, R., & Zhou, Z. (2026) · Molecules · doi:10.3390/molecules31060991
Large-scale prospective evaluation of co-folding across hundreds of Mac1–ligand complexes and three virtual screensPreprint
Kim, J., Correy, G. J., Hall, B. W., Rachman, M. M., Mailhot, O., Togo, T., Gonciarz, R. L., Jaishankar, P., Neitz, R. J., Hantz, E. R., et al. (2026) · eLife · doi:10.7554/elife.110475
Self-citation
Small is big: growing impact of small molecule mass spectrometry in infectious disease drug development
McCall, L.-I. (2026) · mSphere · doi:10.1128/msphere.00860-25
The Selectivity Implications of Docking Libraries with Greater and Lesser Similarities to Bio-like Molecules
Hall, B. W., Sakamoto, K., Huang, X.-P., Irwin, J. J., Shoichet, B. K., & Roth, B. L. (2026) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6c00345
Self-citation
GalaxyVS: Exploring 100-Billion Compounds in SecondsPreprint
Hong, X., Li, P., Zhu, W., Wu, C., Guo, H., Tan, H., Wu, Q., Wu, K., Chen, L., Jia, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.29.728912
Cloud and HPC platforms for molecular docking and simulation
Biswas, D., Chakrabarty, S., Roy, S., Majumder, P., Ballove, T., Londhe, A., Swarup, V., Narayan, M., Tripathy, S., & Jana, M. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-49253-2.00015-5
Library Docking for Cannabinoid-2 Receptor Ligands
Rachman, M. M., Iliopoulos‐Tsoutsouvas, C., Sacco, M. D., Xu, X., Wu, C.-G., Santos, E., Glenn, I., Paris, L., Cahill, M. K., Ganapathy, S., et al. (2026) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6c00835
Self-citation
Automated Parallel Synthesis Accelerates Virtual Screening Hit Discovery
McKenna, S. M., Šícho, M., van der Horst, C., Maasland, J., van der Nol, E., Turco, G., Bernatavicius, A., van Westen, G. J. P., Heitman, L. H., & Pomplun, S. J. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.6c05055
Chemical Distance-Based Acceleration of Large Library Docking with ChemSTEP
Mailhot, O., Holland, K. L., Paris, L., Radchenko, D. S., Moroz, Y. S., Irwin, J. J., & Shoichet, B. K. (2026) · ACS Central Science · doi:10.1021/acscentsci.6c00396
Self-citation
Discovery of Novel AURKA Inhibitors for Triple-Negative Breast Cancer (TNBC) Therapy via a Hybrid Virtual Screening Pipeline, Biological Evaluation and Molecular Dynamics Simulation
Liu, P., Liu, Y., Zhang, X., Chen, R., Pan, Y., Xu, X., & Huang, W. (2026) · ACS Omega · doi:10.1021/acsomega.6c00415
Machine Learning Approaches to Chemical Space Exploration
Almeida, R. L., Tikkanen, T., dos Santos, V. S., Veríssimo, G. C., de Azevedo, D. Q., Sivula, T., Maltarollo, V. G., & Pöhner, I. (2026) · Royal Society of Chemistry eBooks · doi:10.1039/9781837070206-00098
Enabling Computational Exploration of an Expanding Academic Chemistry Space
Abu‐Saleh, A. A. A., Saliby, S., Beauchemin, A. M., Le, C. M., Lundgren, R. J., Batey, R. A., Gentile, F., Harding, R. J., Rousseaux, S. A. L., West, F. G., et al. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c02522
Lead Discovery
Silverman, R. B., Xue, F., & Juncosa, J. I. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-15862-9.00002-3
Machine Learning Integrated Designing and Screening of 8-Hydroxyquinoline Based Metallo-β-Lactamase Inhibitors
Baudino, A. M., & Stone, K. L. (2026) · AI Chemistry · doi:10.3390/aichem1030011
BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening
Qiao, A., Wang, Z., Li, Y., Rao, J., & Yang, Y. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3819040
AI-powered medicinal chemistry and translational drug development
Kaicheng, U., Zhang, S. M., Yu, Z., Zhang, Z., Zhang, J., He, C., Liu, A., Chen, R., Wang, S., Yan, L., et al. (2026) · Chemical Society Reviews · doi:10.1039/d5cs01469g
Pushing the boundaries of virtual screening scale of combinatorial spaces with the V-SYNTHES approach
Protopopov, M., Semenenko, O., Vasylchuk, M., Sadybekov, A., Sadybekov, A. A., Horbatok, K., Hrabovskyi, O., Kapeliukha, A., Nazarova, A. L., Radchenko, D. S., et al. (2026) · npj Drug Discovery. · doi:10.1038/s44386-026-00067-0
Reinvigorating the possibilities: Drug repurposing as a strategic access pathway for rare diseases
Jonker, A., O'Connor, D., Mejat, A., Gribbon, P., Crombie, C., Randall, L., Parker, S., & Lo, D. C. (2026) · Translational Science of Rare Diseases · doi:10.1177/22146490261486345
Computational prioritization of FSP1 inhibitor candidates targeting the ferroptosis-associated protein interactome
Aboagye, D., Sackitey, S., Bandoh, C. O., Buabeng, J. A., Dwomoh, N. K. A., Adam, Y., Oppong, J. F., Adanuty, D. E., Biniyam, P. D., Ohene‐Adu, V., et al. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100584
An extension of jamdock-suite for virtual screening from raw chemical datasets
Emon, S., Haque, M. M., Alam, I., Haque, M. A., & Alam, J. M. (2026) · STAR Protocols · doi:10.1016/j.xpro.2026.104822
Docking-score landscapes shape active-learning performance across Vina, Glide, and SILCS
Chung, J., Bhatt, A., Levine, J. E., Lin, Y., Zhao, M., MacKerell, A. D., Jo, S., Kosaraju, S., & Luo, Y. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00927-x
Exploring benzofuran derivatives as selective MAO-B inhibitors: a multilayered in silico study
Матеев, Е., Irfan, A., Kostov, S., Muhammed, M. T., & Zlatkov, A. (2026) · Pharmacia · doi:10.3897/pharmacia.73.e206275
Large scale prospective evaluation of co-folding across hundreds of Mac1-ligand complexes and three virtual screensPreprint
Kim, J., Correy, G. J., Hall, B. W., Rachman, M. M., Mailhot, O., Togo, T., Gonciarz, R. L., Jaishankar, P., Neitz, R. J., Hantz, E. R., et al. (2026) · eLife · doi:10.7554/elife.110475.2
Self-citation
Generating three-dimensional molecules with non-degenerate double-layer geometric equivariant graph network
Gao, M., & Zhu, F. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.116337
Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions
Hajal, A. A. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00946-8
A critical appraisal of structure-based virtual screening, binding free energy methods, and AI-driven molecular design across the evolving computational landscape of drug discovery
Agboola, O. E., Agboola, S. S., Adedeji, O. S., Aribigbola, T. C., Ilesanmi, O. S., & Aiyeku, B. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00955-7
From Sequence to Structure: AlphaFold and the AI Revolution in Aquaculture Drug Discovery and Vaccine Design
Üstüner, E., & Öz, M. (2026) · Biotechnology and Bioengineering · doi:10.1002/bit.70398
BindingDB Entry 50022912: Identification of Novel Human 15-Lipoxygenase-2 (h15-LOX-2) Inhibitors Using a Virtual Screening Approach.
BindingDB (2026) · UC San Diego · doi:10.7270/q2sx6k3q
BindingDB Entry 50019644: The Science and Art of Structure-Based Virtual Screening.
BindingDB (2024) · UC San Diego · doi:10.7270/q27p93sn

Authors: Lyu, J., Irwin, J. J., & Shoichet, B. K.
Affiliations: University of California, San Francisco (US)
Corresponding author: Irwin, John J.
Shoichet, Brian K.
ORCID: Lyu, Jiankun
Irwin, John J.
Shoichet, Brian K.
Venue: Nature Chemical Biology
Publisher: Springer Science and Business Media LLC
Published: 2023-01-16
Volume: 19
Issue: 6
Pages: 712-718
ISSN: 1552-4450 (ISSN-L)
1552-4469
DOI: 10.1038/s41589-022-01234-w
PMC: PMC10243288
OpenAlex: W4316507591
Cited by: 193
References: 53
Citations by year: 2026: 49 · 2025: 57 · 2024: 55 · 2023: 31
FWCI: 27.17
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Chemical Biology (publishedVersion)
ChemRxiv (acceptedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (GM133836, R35GM122481)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
Keywords: library size, molecular diversity, molecular similarity, protein-ligand docking
MeSH terms: Libraries, Digital, 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

Modeling the expansion of virtual screening libraries

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks