Project ~ Related Project ~ Guide
The present study is finalised to determine the most advanced models in the literature capable of producing new high-quality molecules starting from well-known datasets. The selection is carried out through a series of evaluation processes. At first, the output samples of each method are evaluated according to certain physico-chemical properties such as Quantitative Estimation of Drug-likeness (QED) and Synthetic Accessibility (SA). Then, in a successive step, the assessment also includes the predicted activity towards one target protein. The final aim of the project actually is to better understand whether and how the performance of each model varies when the typology of the target protein is changed. More precisely, the proteins involved in this work are: Beta-secretase 1 (BACE1), Peroxisome proliferator-activated receptor alpha (PPAR-α), Cyclin Dependent Kinase 2 (CDK2) and the Dopamine Receptor subtype D3 ( DRD3).
The modified code used to run the models is provided in the GitHub repo accessible at the following link. Specifically, the code at the provided link is a slightly updated version of that published by the authors in their projects. In case an error is encountered, it means the page has not been already published.
Note: the content of the folder Models/chembl_mcp_models is retrieved from the official GitHub page of the ChEMBL group.
This project includes a series of experiments conducted at the beginning of the learning phase with the objective to acquire a practical knowledge of the inner workings of the models aimed to produce molecules.
Nevertheless, the main contribution is the test_script.py file that enables to have a comprehensive overview of the quality of the molecules generated. The benchmark exploited by the script is created adding some brand-new metrics to those presented in the GuacaMol and MOSES benchmarking platforms.
A comprehensive guide concerning how to run the code along with additional information is provided in the file Instruction.md .
The packages required for the execution of the code along with the role of each file and the software used are described in the Sections below.
Althoug the following list gather all the most important packages needed to run the project code, a more comprehensive overview is provided in the file requirements.txt . The latter can also be directly used to install the packages by typing the specific command on the terminal. Please note that the descriptions provided in this subsection are taken directly from the package source pages. For more details it is reccomended to directly reference to the related official websites.
Compulsory :
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Pandas provides fast, flexible, and expressive data structures designed to make working with structured and time series data both easy and intuitive.
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Numpy is the fundamental package for array computing with Python.
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Tensorflow is an open source software library for high performance numerical computation. Its allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs). Important: Recently Keras has been completely wrapped within Tensorflow.
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Os provides a portable way of using operating system dependent functionality.
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Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
main.py it is the starting point of all the experiments conducted to understand the mechanisms of the de novo drug design generative models.
test_script.py contains a wide set of metrics useful to understand the quality of a collection of molecules.
PyCharm is an integrated development environment (IDE) for Python programmers: it was chosen because it is one of the most advanced working environments and for its ease of use.
Google Colab is an environment that enables to run python notebook entirely in the cloud. It supports many popular machine learning libraries and it offers GPUs where you can execute the code as well.
Visual Studio Code is a code editor optimized for building and debugging modern web and cloud applications.