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2 2  (((
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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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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.
21 += Who has access? =
22 22  
23 -[[image:image-20230111110707-1.png]]
24 -
25 -With the so trained model further predictions can be performed using different ligands.
26 -
27 -= How to use the script =
28 -
29 -• __Requisites__
30 -
31 - - Python 3.0 (or newer version)
32 -
33 - - Numpy
34 -
35 - - Tensorflow
36 -
37 - - Pandas
38 -
39 - - Sklearn
40 -
41 - - cv2
42 -
43 - - Matplotlib
44 -
45 -• __Usage__
46 -
47 -- CNN-training-script.py constitutes the main code of the tool: here different models of CNN can be customized, by changing also activation function and classification mode. In its final part it operates also a test using unseen data and save the trained model as a .h5 file.
48 -
49 -The first operation that is required by the user regards the very initial prepartion of the DF-images from the DF-matrices [see the following link for the DF preparation [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/distance-fluctuations>>https://wiki.ebrains.eu/bin/view/Collabs/distance-fluctuations]]]. This can be done using the gnuplot.in file and the exectute-DF.sh file, which renames the .png accroding with the nanoseconds used to extract the image.
50 -
51 -The images required for the training of the model has to be selected and classified by the user between the two states of interest and by using the random-selector files to divide them between test, trainig and validation datasets. Here we usually preformed a random separation between test (20%), train (64%) and validation (16%) sets using the last 200ns of the equilibrated dynamics.
52 -
53 -
54 -- CNN-external-data-test.py is a script which aims to use the trained model (.h5 file) and test it on data belonging to different proteins from the ones used during the build of the model.
23 +Describe the audience of this collab.
55 55  )))
56 56  
57 57  
image-20230111110707-1.png
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