What is it about?
Drug Target Interactions (DTIs) are crucial in drug discovery as it reduces the range of candidate searches for medicines, speeding up the drug screening process. Since, traditional experiments are time and money expensive, we aim to develop a computational technique to reduce predicting DTIs which can act as a good decision drivers during the early stages of drug discovery .
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Why is it important?
In our method we aim at predicting DTIs using Molecular substructure fingerprints and Position Specific Scoring Matrix(PSSM) of drugs and targets respectively. We proposed a technique to handle imbalance in our datasets which retrieves informative samples, helping in the boosting the performance of DTI prediction
Read the Original
This page is a summary of: A machine learning strategy with clustering under sampling of majority instances for predicting Drug Target Interactions, Molecular Informatics, November 2022, Wiley, DOI: 10.1002/minf.202200102.
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