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I wanted to try the framework and assess its efficacy for hit enrichment.
What I did is follows:
choose HDAC6 as target and download the structure PDB id: 5EEN
generate pharmacophore model using Pharmaconet
download 50 active compounds against HDAC6 from pubchem, generate decoys (50/active) using DUDE.
convert structures into 3D using open babel.
Perform screening using the generated Pharmacophore.
The results were really disappointing. Decoys get scores higher than the active and EF1% was very low.
what did I do wrong? should I generate multiple conformer for each compound ?
should I do docking and then perform screening using the model or what ?
The text was updated successfully, but these errors were encountered:
First, since PharmacoNet is a rigid-body docking approach, increasing the number of conformers improves performance. Since this program is developed for the purpose of pre-screening before docking as fine-screening, you don't have to perform docking.
Second, like many other docking programs and deep learning-based methods, the performance of PharmacoNet is target system-dependent. I recommend analyzing the correlation between the docking tool you plan to use in virtual screening against target proteins before doing a large-scale screening.
I note that I am not familiar with the characteristics of molecules generated using DUDE.
Hi
I wanted to try the framework and assess its efficacy for hit enrichment.
What I did is follows:
The results were really disappointing. Decoys get scores higher than the active and EF1% was very low.
what did I do wrong? should I generate multiple conformer for each compound ?
should I do docking and then perform screening using the model or what ?
The text was updated successfully, but these errors were encountered: