Last modified by emacasali on 2023/01/11 16:05
From version 1.1
edited by emacasali
on 2023/01/11 10:48
Change comment: Collab created by emacasali
To version 7.1
edited by emacasali
on 2023/01/11 11:07
Change comment: Uploaded new attachment "image-20230111110707-1.png", version {1}

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2 2  (((
3 3  (% class="container" %)
4 4  (((
5 -= My Collab's Extended Title =
5 += MEDIUM - Machine lEarning Drug dIscovery throUgh dynaMics =
6 6  
7 -My collab's subtitle
7 += =
8 +
9 +Giorgio Colombo Group (UNIPV)
8 8  )))
9 9  )))
10 10  
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12 12  (((
13 13  (% class="col-xs-12 col-sm-8" %)
14 14  (((
15 -= What can I find here? =
17 += Why this tool is useful? =
16 16  
17 -* Notice how the table of contents on the right
18 -* is automatically updated
19 -* to hold this page's headers
19 +The prediction of the best ligand for a specific protein could be a huge challenge using the classical approaches like molecular docking and stabilisation energy calculations.
20 20  
21 +Here we report on a fast and solid workflow which starts from our DF-matrix method to analyse how the protein globally behaves in the presence of a ligand. Machine Learning (ML) trains a Convolutional Neural Network (CNN) model directly on the pixel images of DF: train is preformed using a known ligand and the different behaviour of the protein is evaluated in the presence and in absence of it.
22 +
23 +With the so trained model further predictions can be performed using different ligands.
24 +
21 21  = Who has access? =
22 22  
23 23  Describe the audience of this collab.
image-20230111110707-1.png
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