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GFscore:
A General Non-Linear Consensus Scoring Function for High-Throughput
Docking*
Stéphane Betzi**,
Karsten Suhre***,
Bernard Chétrit**, Françoise
Guerlesquin** and Xavier Morelli** |
Most
of the recent work published in the field of Docking and Scoring
Protein/Ligand complexes have focused in ranking true positives
resulting from a Virtual Library Screening (VLS) through
the use of a specified or consensus linear scoring function.
GFscore is a methodology to accelerate the process
of High Throughput Screening (HTS). We have extended the principle
of consensus scoring in a Non-Linear Neural
Network manner. This original global scoring function is
a combination of the five scoring functions found in the Cscore
package from Tripos Inc.
GFscore learned to discriminate true negatives from false
negatives in a dataset of diverse chemical compounds
and eliminates up to 75% of molecules with a confidence
rate of 90%. The final result is a Hit Enrichment
in the list of molecules to investigate during a research campaign
for biological active compounds. The resulting 25% of molecules
to be tested by in vitro screening make of GFscore a powerful
tool for the biologist, saving his time and money. |
Betzi, S.**, Suhre, K.***, Chetrit, B.**, Guerlesquin, F.**, Morelli, X**.
GFscore: A General Non-Linear Consensus Scoring Function for High-Throughput Docking
Journal of Chemical Information and Modeling. 2006 Jul-Aug;46(4):1704-12
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| This work was
partially supported by : |
- The
French National AIDS Research Agency (ANRS) AC14.3
- Marseille-Nice
Génopole and the French National Genomic Network (RNG).
KS thanks Prof. J-M Claverie (head of the IGS) for laboratory
space and support.
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** Bioénergétique
et Ingénierie des Protéines (BIP), CNRS UPR9036
Institute for Structural Biology and Microbiology (IBSM),
31 Chemin Joseph Aiguier 13402 Marseille Cedex 20, France |
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Contact
| 2006 | 
