Last modified by emacasali on 2023/01/11 16:05
From version 7.1
edited by emacasali
on 2023/01/11 11:07
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To version 2.1
edited by emacasali
on 2023/01/11 10:48
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2 2  (((
3 3  (% class="container" %)
4 4  (((
5 -= MEDIUM - Machine lEarning Drug dIscovery throUgh dynaMics =
5 += My Collab's Extended Title =
6 6  
7 -= =
8 -
9 -Giorgio Colombo Group (UNIPV)
7 +My collab's subtitle
10 10  )))
11 11  )))
12 12  
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14 14  (((
15 15  (% class="col-xs-12 col-sm-8" %)
16 16  (((
17 -= Why this tool is useful? =
15 += What can I find here? =
18 18  
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.
17 +* Notice how the table of contents on the right
18 +* is automatically updated
19 +* to hold this page's headers
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 -
25 25  = Who has access? =
26 26  
27 27  Describe the audience of this collab.
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1 -XWiki.emacasali
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