Computational chemistry, data mining, high-throughput synthesis and screening--informatics and integration in drug discovery
2001
Integrating Technologies in Drug Discovery
publication
Evidence: moderate
Author Information
Author(s): Charles J. Manly
Primary Institution: Neurogen Corporation
Hypothesis
Can the integration of combinatorial chemistry, high-throughput pharmacology, and computational chemistry enhance the drug discovery process?
Conclusion
The AIDDsm methodology significantly improves the efficiency and effectiveness of drug discovery by integrating various disciplines.
Supporting Evidence
- AIDDsm can synthesize 400,000 samples per year and generate biological data for 300,000 samples per month.
- The integration of disciplines allows for significant efficiency gains in drug discovery.
- OLCM models provide guidance for optimizing drug-like properties.
Takeaway
This study shows that by combining different scientific methods, we can find new medicines faster and better.
Methodology
The AIDDsm methodology integrates combinatorial chemistry, high-throughput pharmacology, and computational chemistry to streamline drug discovery.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website