A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches.

Area of Science:

• Computational chemistry and cheminformatics
• Drug discovery and development
• Bioinformatics and computational biology

Background:

• Drug Target Interaction (DTI) prediction is crucial for efficient drug discovery, reducing costly and time-consuming wet-lab experiments.
• Chemogenomics systematically studies the biological effects of small molecules on macromolecular targets.
• Increasing algorithmic complexity in DTI prediction may soon necessitate big data technologies like Spark.

Purpose of the Study:

• To provide a comprehensive overview and realistic evaluation of computational DTI prediction approaches.
• To serve as a guide and reference for researchers in the field of DTI prediction.
• To highlight future opportunities for improving DTI prediction performance.

Main Methods:

• Explanation of data utilized in computational DTI prediction.
• Categorization and explanation of modern DTI prediction techniques.
• Realistic assessment of the predictive performance of various DTI approaches.

Main Results:

• Detailed analysis of data sources relevant to DTI prediction.
• Comparative performance evaluation of different computational DTI prediction methods.
• Identification of strengths and weaknesses of current DTI prediction strategies.

Conclusions:

• Computational DTI prediction significantly streamlines the drug discovery pipeline.
• The study offers a valuable resource for researchers navigating DTI prediction methodologies.
• Future research should focus on enhancing DTI prediction accuracy and exploring new computational paradigms.