Deep Neural Network-Assisted Drug Recommendation Systems for Identifying Potential Drug-Target Interactions.

ABSTRACT: In silico methods to identify novel drug-target interactions (DTIs) have gained significant importance over conventional techniques owing to their labor-intensive and low-throughput nature. Here, we present a machine learning-based multiclass classification workflow that segregates interactions between active, inactive, and intermediate drug-target pairs. Drug molecules, protein sequences, and molecular descriptors were transformed into machine-interpretable embeddings to extract critical features from standard datasets. Tools such as CHEMBL web resource, iFeature, and an in-house developed deep neural network-assisted drug recommendation (dNNDR)-featx were employed for data retrieval and processing. The models were trained with large-scale DTI datasets, which reported an improvement in pe

SUBMITTER: Kalakoti Y 

PROVIDER: S-EPMC9016825 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature