Wiki source code of MEDIUM - Machine lEarning Drug dIscovery throUgh dynaMics
Show last authors
| author | version | line-number | content |
|---|---|---|---|
| 1 | (% class="jumbotron" %) | ||
| 2 | ((( | ||
| 3 | (% class="container" %) | ||
| 4 | ((( | ||
| 5 | = MEDIUM - Machine lEarning Drug dIscovery throUgh dynaMics = | ||
| 6 | |||
| 7 | = = | ||
| 8 | |||
| 9 | Giorgio Colombo Group (UNIPV) | ||
| 10 | ))) | ||
| 11 | ))) | ||
| 12 | |||
| 13 | (% class="row" %) | ||
| 14 | ((( | ||
| 15 | (% class="col-xs-12 col-sm-8" %) | ||
| 16 | ((( | ||
| 17 | = Why this tool is useful? = | ||
| 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. | ||
| 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 | = Who has access? = | ||
| 26 | |||
| 27 | Describe the audience of this collab. | ||
| 28 | ))) | ||
| 29 | |||
| 30 | |||
| 31 | (% class="col-xs-12 col-sm-4" %) | ||
| 32 | ((( | ||
| 33 | {{box title="**Contents**"}} | ||
| 34 | {{toc/}} | ||
| 35 | {{/box}} | ||
| 36 | |||
| 37 | |||
| 38 | ))) | ||
| 39 | ))) |