Drug Discovery Using ML for Breast Cancer

Description

• An ML-driven application for drug discovery in breast cancer utilizes machine learning algorithms to predict the pIC50 values of chemical compounds. By analyzing the relationships between compound structures and their biological activities against breast cancer, the application can provide valuable insights into the efficacy of potential drug candidates.

• By leveraging molecular features and descriptors, the application assists researchers in identifying promising compounds, accelerating the development of effective treatments for breast cancer.

About the Project

No.	Files	Types	Contents	Description
1	assets	Folder	assets/images/bio_logo.png, assets/images/logo.png, assets/aromatase.jpg, assets/landing.png	Logos and other images found on.
2	auth	Folder	auth/forget.html, auth/login.html, auth/register.html	HTML code of login and register pages.
3	css	Folder	css/animate.css, css/forget.css, css/login.css, css/register.css	Style for HTML pages.
4	diagrams	Folder	diagrams/activity/, diagrams/architecture/, diagrams/Sequence/, diagrams/usecase/	Diagrams code for documentation.
5	docs	Folder	docs/diagrams/, docs/Drug-Discovery-Docs.odt, docs/Drug-Discovery-Docs.pdf	All documentation of the App found on.
6	fonts	Folder	fonts/	Google fonts and others for HTML pages.
7	js	Folder	js/app.js, js/eye.js, js/firebase-config.js, js/forget.js, js/jquery-1.11.1.min.js, js/login.js, js/plugins.js, js/register.js	The JavaScript part for HTML pages.
8	ML	Folder	ML/aromatase_exp.txt, ML/aromatase.pkl, ML/descriptor_list.csv, ML/descriptors_output.csv, ML/Drug_Discovery.ipynb, ML/molecule.smi, ML/padel.sh, ML/padel.zip	ALL about the AI codes and resources found on.
9	PaDEL-Descriptor	Folder	PaDEL-Descriptor/, PaDEL-Descriptor/PaDEL-Descriptor.jar, PaDEL-Descriptor/PubchemFingerprinter.xml	Files that used for Finger Prints for Chemicals.
10	source	Folder	source/descriptors.py, source/feedback.py, source/file_download.py, source/history.py, source/home.py, source/logout.py, source/main.py, source/model.py, source/new.py, source/prediction.py, source/settings.py, source/XYZ_view.py	All Streamlit codes found on.
11	app.py	File	---	The main part of the streamlit App.
12	index.html	File	---	The landing page for the Application.
13	LICENSE	File	---	Is rule and regualtion to use the app, patent
14	packages.txt	File	---	The files of software packages(e.g. java jre) used to run the application.
15	README.md	File	---	The file of the description of the application
16	requirements.txt	File	---	Streamlit packages that is used for the application to run
17	style.css	File	---	Styles the whole HTML pages.

Importance of the Project

✔️ Accelerating Progress through Machine Learning
✔️ Machine learning-driven application predicts pIC50 values of chemical compounds.
✔️ Identification of potential drug candidates for breast cancer.
✔️ Analysis of compound structures and their relationships with biological activities.
✔️ Valuable insights into the efficacy of drug candidates.
✔️ Accelerates the identification and development of promising compounds.
✔️ Significant contribution to the battle against breast cancer.
✔️ Potential to transform patient outcomes and bring new hope for the future.

• After obtaining higher pIC50 values for the chemicals, there are must be prioritize and experimental validation of the chemicals will be followed.
  ✳️ Here are some key reasons why the pIC50 value is significant:
  ✔️ Quantitative Measure of Potency
  ✔️ Comparative Analysis
  ✔️ Predictive Modeling
  ✔️ Structure-Activity Relationship (SAR) Analysis
  ✔️ Lead Optimization
  ✔️ Decision-Making in Preclinical Studies
  

Usage

• To use the deployed system of the project, follow the following steps.

Step 1️⃣:

👉 Access the website with Drug Discovery Using ML for Breast Cancer

Step 2️⃣:

👉 Navigate to the Register button that is found on the right side of the header of the landing page.

Step 3️⃣:

👉 Fill the necessary form of input data and press the register button.

Step 4️⃣:

👉 If registered successfully, it will redirect you to the main page of the website.

Step 5️⃣:

👉 Upload a chemical SMILES file. The file should have two columns: the first column containing the SMILES notation of the chemicals and the second column containing the ChEMBL ID. Here's an example:

  CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1 CHEMBL133897
  O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1 CHEMBL336398
  CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1 CHEMBL131588
  O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F CHEMBL130628
  CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C CHEMBL130478

NB: Make sure your file is in a valid .txt format and CSV file content.

Step 6️⃣:

👉 Press the Predict button to see the output of the pIC50 value of the chemicals. You can find more details here.

Others

👉 After having an acount the user can login and use forget password functionality using the registered account.

About Us

• getdaniel - Gmail
• Endale1221 - Gmail

LICENSE

• The license of this project is accessed through LICENSE