The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
NIH Molecular Libraries Initiative
Abstract
The Molecular Libraries Initiative (MLI) of the NIH Roadmap for Medical Research is a bold set of programs to facilitate the use of small molecules in the public sector and expand the reach of small-molecule technologies to define gene, cell, and organism function in health and disease. The initiative described in this Policy Forum has three components: the Molecular Libraries Screening Centers Network; cheminformatics initiatives including a new public compound database (PubChem); and technology development initiatives in chemical diversity, cheminformatics, assay development, screening instrumentation, and predictive ADMET. All research tools and results will be made publicly available without encumbrance. The small-molecule tools and technologies produced by the MLI will accelerate gene function determination, target validation, and downstream drug development.
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Citations
Cites
Hopkins, A. L., & Groom, C. R. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd892 · Cited by 3,305
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Unexplored therapeutic opportunities in the human genome
Oprea, T. I., Bologa, C. G., Brunak, S., Campbell, A., Gan, G. N., Gaulton, A., Gomez, S. M., Guha, R., Hersey, A., Holmes, J., et al. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.14 · Cited by 378
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Metadata
- Authors
- Austin, C. P., Brady, L. S., Insel, T. R., & Collins, F. S.
- Venue
- Science
- Published
- 2004-11-12
- Volume
- 306
- Issue
- 5699
- Pages
- 1138-1139
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W1640748652
- Cited by
- 348
- References
- 1
- Type
- journal article
- Language
- en
- Open access
- closed
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- Austin, C. P., Brady, L. S., Insel, T. R., & Collins, F. S. (2004). NIH Molecular Libraries Initiative. Science. https://doi.org/10.1126/science.1105511
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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.
