Version 10.1 by emacasali on 2023/01/11 16:05
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5 = MEDIUM - Machine lEarning Drug dIscovery throUgh dynaMics =
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9 Giorgio Colombo Group (UNIPV)
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17 = Why this tool is useful? =
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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.
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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.
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23 [[image:image-20230111110707-1.png]]
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25 With the so trained model further predictions can be performed using different ligands.
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27 = How to use the script =
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29 • __Requisites__
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31 - Python 3.0 (or newer version)
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33 - Numpy
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35 - Tensorflow
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37 - Pandas
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39 - Sklearn
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41 - cv2
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43 - Matplotlib
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45 • __Usage__
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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.
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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.
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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.
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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.
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60 {{box title="**Contents**"}}
61 {{toc/}}
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