(Full project is available on my github-https://github.com/AKSHITHASINGAREDDY/Predicting-Bioactivity-of-Compounds-using-Machine-Learning-Bioinformatics-Approach)
Use of Predicting Bioactivity of Compounds
Predicting the bioactivity of compounds is crucial in drug discovery and development. Bioactivity prediction helps in identifying potential drug candidates by estimating their effectiveness in inhibiting specific biological targets. This can significantly reduce the time and cost associated with experimental testing by prioritizing compounds with higher predicted bioactivity for further experimental validation.
Target Value: pIC50
The target value in this project is pIC50, which stands for the negative logarithm of the IC50 value. IC50 is the concentration of a substance required to inhibit a particular biological or biochemical function by 50%. The pIC50 value is a standardized measure that makes it easier to compare the potencies of different compounds. A higher pIC50 value indicates a more potent compound, meaning it requires a lower concentration to achieve the same inhibitory effect. This makes pIC50 a crucial parameter in evaluating the effectiveness of potential drug candidates.
Project Outcome: What Did I Predict?
From this project, I successfully predicted the pIC50 values of compounds based on their molecular fingerprints. This prediction indicates how potent each compound is in inhibiting a specific target, allowing for the identification of highly effective compounds. The machine learning model developed in this project can be used to screen large libraries of compounds and prioritize those with the highest predicted bioactivity for further experimental validation. This contributes to a more efficient and cost-effective drug discovery process.
Disease Targeted in This Project
The project targets Acetylcholinesterase (AChE), an enzyme associated with neurological diseases, most notably Alzheimer's disease. Inhibitors of acetylcholinesterase are commonly studied for their potential therapeutic effects in Alzheimer's disease treatment.
Steps Followed in this Project
The project comprises Jupyter notebook that collectively address a bioinformatics problem through data analysis and machine learning model building. The goal is to predict the bioactivity of compounds, specifically their pIC50 values, which indicate the inhibitory concentration of a substance against a particular target.
01. Identify the Biological Target:
The target is acetylcholinesterase (AChE), which is an important enzyme in the nervous system that breaks down the neurotransmitter acetylcholine. Inhibition of AChE is a strategy for treating Alzheimer's disease.
02. Collect Bioactivity Data:
Bioactivity data for compounds tested against acetylcholinesterase was collected from the ChEMBL database.
Ensure the data includes both active (bioactive) and inactive compounds to train the model effectively.
03. Data Preprocessing:
Clean and preprocess the data to handle missing values, normalize features, and encode categorical variables if necessary.
Generate molecular fingerprints or other relevant descriptors to represent the chemical structures of the compounds to serve as features. [PubChem Fingerprints: These columns represent molecular fingerprints generated using PubChem's fingerprinting method. Each column (e.g., PubchemFP0, PubchemFP1, ...) is a binary feature indicating the presence or absence of certain molecular substructures or properties]
Molecular fingerprints, represented as binary features, were used to describe the compounds, while the pIC50 values served as the target variable.
04. Model Building:
The dataset was split into training and testing sets (80:20).
A Random Forest Regressor model was trained to predict pIC50 values based on the molecular fingerprints.
05. Model Evaluation and Validation:
The model's performance was evaluated using Mean Squared Error (MSE) and R² score.
Hyperparameter tuning and feature selection were conducted to optimize model performance.
06. Prediction and Screening:
The trained model was used to predict the pIC50 values of new compounds, identifying those with the highest predicted bioactivity for potential further experimental validation.
By targeting acetylcholinesterase, the project aims to contribute to the development of new treatments for Alzheimer's disease, demonstrating the practical application of machine learning in bioinformatics and drug discovery.
Future Scope on How This Project Helps in Alzheimer's Drug Discovery
01. Identifying Potent Inhibitors:
By predicting the pIC50 values of various compounds, the project helps in identifying potent inhibitors of acetylcholinesterase. Higher pIC50 values correspond to more effective inhibitors, which are crucial in slowing down the degradation of acetylcholine in the brain and thereby alleviating symptoms of Alzheimer's disease.
02. Efficient Screening:
The machine learning model allows for the rapid screening of large libraries of compounds. This means that researchers can quickly prioritize compounds that are most likely to be effective based on their predicted bioactivity, saving both time and resources in the drug discovery process.
03. Reducing Experimental Costs:
Experimental testing of every potential compound is expensive and time-consuming. By using the model to predict bioactivity, only the most promising compounds are selected for further experimental validation, significantly reducing the overall cost of the drug discovery process.
04. Guiding Chemical Modifications:
The insights gained from the model's predictions can guide chemists in modifying existing compounds to improve their efficacy. Understanding which molecular fingerprints are associated with higher bioactivity can help in designing more potent acetylcholinesterase inhibitors.
05. Accelerating Drug Development:
The predictive model accelerates the initial stages of drug development by quickly identifying lead compounds that have a higher chance of success in clinical trials. This can shorten the time frame for developing new Alzheimer's drugs.
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