Drug Discovery using Machine Learning Approach
Predicting Inhibitor Concentration 50%(IC50) values of HDAC6 compounds & selecting top 250 compounds from 10000 unknown compounds.
Created on 9th April 2023
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Drug Discovery using Machine Learning Approach
Predicting Inhibitor Concentration 50%(IC50) values of HDAC6 compounds & selecting top 250 compounds from 10000 unknown compounds.
The problem Drug Discovery using Machine Learning Approach solves
This Project addresses a critical need in the pharmaceutical industry for more efficient drug discovery and development. HDAC6 inhibitors have been identified as a promising target for the treatment of various diseases, including cancer, neurological disorders, and autoimmune diseases. However, the development of HDAC6 inhibitors has been challenging due to their complex structure-activity relationships.
By using machine learning approaches to predict the activity of HDAC6 compounds, this project has the potential to accelerate the drug discovery process, reduce the cost of drug development, and ultimately lead to the discovery of more effective treatments for diseases that currently have limited therapeutic options. Additionally, the project may have broader implications for the development of machine learning models for predicting the activity of compounds in other therapeutic areas, thus advancing the field of computational drug discovery.
Challenges we ran into
Finding proper descriptors and fingerprints for particular compounds. Converting smiles into a suitable mole format. Time required for generating descriptors and fingerprints.
Technologies used