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Computer-based de novo design of drug-like molecules
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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
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Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
Ruddigkeit, L., van Deursen, R., Blum, L. C., & Reymond, J.-L. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300415d · Cited by 1,505
Schneider, G. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.232 · Cited by 910
Self-citationRethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894
Self-citationCombining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Reymond, J.-L. (2015) · Accounts of Chemical Research · doi:10.1021/ar500432k · Cited by 720
Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach
Kapetanovic, I. M. (2008) · Chemico-Biological Interactions · doi:10.1016/j.cbi.2006.12.006 · Cited by 662
Forging the basis for developing protein-ligand interaction scoring functions.
Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., & Wang, R. (2017) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00491 · Cited by 493
Artificial intelligence in drug discovery: recent advances and future perspectives
Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1909567 · Cited by 464
Self-citationMolGAN: An implicit generative model for small molecular graphs
De Cao, N., & Kipf, T. (2018) · arXiv · doi:10.48550/arxiv.1805.11973 · Cited by 442
De Novo Design of Bioactive Small Molecules by Artificial Intelligence
Merk, D., Friedrich, L., Grisoni, F., & Schneider, G. (2018) · Molecular Informatics · doi:10.1002/minf.201700153 · Cited by 373
Self-citationGuedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331
Multiobjective Optimization in Bioinformatics and Computational Biology
Handl, J., Kell, D. B., & Knowles, J. (2007) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.070203 · Cited by 308
Assessing the impact of generative AI on medicinal chemistry
Walters, W. P., & Murcko, M. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0418-2 · Cited by 262
DOGS: Reaction-Driven de novo Design of Bioactive Compounds
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Self-citationeToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
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“Foremost SBDD methods include molecular docking [15], molecular dynamics [16], receptor-based VS [17], and the de novo design of active compounds [18].”
MethodHIV‐1 NNRTIs: structural diversity, pharmacophore similarity, and impliations for drug design
Zhan, P., Chen, X., Li, D., Fang, Z., De Clercq, E., & Liu, X. (2013) · Medicinal Research Reviews · doi:10.1002/med.20241 · Cited by 192
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Self-citationBai, Q., Liu, S., Tian, Y., Xu, T., Banegas‐Luna, A. J., Pérez‐Sánchez, H., Huang, J., Liu, H., & Yao, X. (2022) · WIREs Computational Molecular Science · doi:10.1002/wcms.1581 · Cited by 156
Fragment-Based Lead Discovery and Design
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Fragment Informatics and Computational Fragment‐Based Drug Design: An Overview and Update
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Knowledge-Based Approach to de Novo Design Using Reaction Vectors
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Artificial intelligence facilitates drug design in the big data era
Wang, L., Ding, J., Pan, L., Cao, D., Jiang, H., & Ding, X. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.103850 · Cited by 80
Niazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18091271 · Cited by 61
Wang, J., Ishchenko, A., Zhang, W., Razavi, A., & Langley, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-05875-8 · Cited by 55
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
Applications and potential of in silico approaches for psychedelic chemistry
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Metadata
- Authors
- Schneider, G., & Fechner, U.
- Venue
- Nature Reviews Drug Discovery
- Published
- 2005-08-01
- Volume
- 4
- Issue
- 8
- Pages
- 649-663
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W2041686943
- Cited by
- 948
- References
- 1
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
