Journal
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
Volume 21, Issue 1-3, Pages 97-112Publisher
SPRINGER
DOI: 10.1007/s10822-007-9102-6
Keywords
QSAR modeling; phenanthrine-based tylophrine derivatives (PBTs); anti-cancer agents; virtual screening
Categories
Funding
- NATIONAL CANCER INSTITUTE [R01CA017625] Funding Source: NIH RePORTER
- NATIONAL CENTER FOR RESEARCH RESOURCES [P20RR020751] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [R01AI077417] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM066940] Funding Source: NIH RePORTER
- NCI NIH HHS [R01 CA017625-22, CA17625, R01 CA017625] Funding Source: Medline
- NCRR NIH HHS [P20-RR20751, P20 RR020751] Funding Source: Medline
- NIAID NIH HHS [R01 AI077417] Funding Source: Medline
- NIGMS NIH HHS [GM066940, R01 GM066940, R01 GM066940-04] Funding Source: Medline
Ask authors/readers for more resources
A combined approach of validated QSAR modeling and virtual screening was successfully applied to the discovery of novel tylophrine derivatives as anticancer agents. QSAR models have been initially developed for 52 chemically diverse phenanthrine-based tylophrine derivatives (PBTs) with known experimental EC50 using chemical topological descriptors (calculated with the MolConnZ program) and variable selection k nearest neighbor (kNN) method. Several validation protocols have been applied to achieve robust QSAR models. The original dataset was divided into multiple training and test sets, and the models were considered acceptable only if the leave-one-out cross-validated R-2 (q(2)) values were greater than 0.5 for the training sets and the correlation coefficient R-2 values were greater than 0.6 for the test sets. Furthermore, the q(2) values for the actual dataset were shown to be significantly higher than those obtained for the same dataset with randomized target properties (Y-randomization test), indicating that models were statistically significant. Ten best models were then employed to mine a commercially available ChemDiv Database (ca. 500 K compounds) resulting in 34 consensus hits with moderate to high predicted activities. Ten structurally diverse hits were experimentally tested and eight were confirmed active with the highest experimental EC50 of 1.8 mu M implying an exceptionally high hit rate (80%). The same ten models were further applied to predict EC50 for four new PBTs, and the correlation coefficient (R-2) between the experimental and predicted EC50 for these compounds plus eight active consensus hits was shown to be as high as 0.57. Our studies suggest that the approach combining validated QSAR modeling and virtual screening could be successfully used as a general tool for the discovery of novel biologically active compounds.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available