GcForest-based compound-protein interaction prediction model and its application in discovering small-molecule drugs targeting CD47.

ABSTRACT: Identifying compound-protein interaction plays a vital role in drug discovery. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are playing increasingly important roles in compound-protein interaction (CPI) prediction. However, ML relies on learning from large sample data. And the CPI for specific target often has a small amount of data available. To overcome the dilemma, we propose a virtual screening model, in which word2vec is used as an embedding tool to generate low-dimensional vectors of SMILES of compounds and amino acid sequences of proteins, and the modified multi-grained cascade forest based gcForest is used as the classifier. This proposed method is capable of constructing a model from raw data, adjusting model complexity accordin

SUBMITTER: Shan W 

PROVIDER: S-EPMC10623438 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature