How to approach machine learning-based prediction of drug/compound-target interactions.

ABSTRACT: The identification of drug/compound-target interactions (DTIs) constitutes the basis of drug discovery, for which computational predictive approaches have been developed. As a relatively new data-driven paradigm, proteochemometric (PCM) modeling utilizes both protein and compound properties as a pair at the input level and processes them via statistical/machine learning. The representation of input samples (i.e., proteins and their ligands) in the form of quantitative feature vectors is crucial for the extraction of interaction-related properties during the artificial learning and subsequent prediction of DTIs. Lately, the representation learning approach, in which input samples are automatically featurized via training and applying a machine/deep learning model, has been utilized in biome

SUBMITTER: Atas Guvenilir H 

PROVIDER: S-EPMC9901167 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature