Brown, J, Nijima, S, Okuno, Y (2013) Compound–protein interaction prediction within chemogenomics: theoretical concepts, practical usage, and future directions. Mol Inf 32: pp. 906-921Brown J, Nijima S, Okuno Y (2013) Compound–protein interaction prediction within chemogenomics: theoretical ...
Herein, we proposed the CIPHEN (compound-protein interactions prediction based on the heterogeneous network) to predict potential antihepatoma NPs and their targets. The evaluation of canonical compound-protein interactions (CPIs) databases and independent test demonstrated the good ability of CPIHN to ...
ii“0_main” — 2022/12/23 — 2:04 — page 1 — #1iiiiiiSystem BiologyCSI: Contrastive Data Stratif i cation for InteractionPrediction and its Application toCompound-Protein Interaction PredictionApurva Kalia 1 , Dilip Krishnan 2 and Soha Hassoun 1,3,∗1 Department of Computer Science, T...
Boosting compound-protein interaction prediction by deep learning Methods (2016) L.Zhanget al. From machine learning to deep learning: progress in machine intelligence for rational drug discovery Drug Discovery Today (2017) J.Yuanet al. Gated CNN: Integrating multi-scale feature layers for object ...
The CNN module for encoding the features of an input protein sequence. More details can be found in STAR Methods. Figure 3. The Prediction Modules of MONN The pairwise interaction prediction module. Here, and stand for the weight parameters of two single-layerneural networksthat need to be le...
1. Compound–protein interaction prediction This is a binary classification task, which aim to predict whether there is an interaction between the compound and the protein or not. #Run the commandlinepython main.py -task interaction -dataset human ...
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524 - lifanchen-simm/transfor
protein sequence and SMILES for the DTA regression task. Experimental evaluations show that ELECTRA-DTA outperforms various state-of-the-art DTA prediction models, especially with the challenging, interaction-sparse BindingDB dataset. In target selection and drug repurposing for COVID-19, ELECTRA-DTA...
measure large-scale CPIs in an efficient way. Computational methods have been developed in this field to facilitate the discovery of hit or lead ligands for protein targets. Currently, there are still several challenges in establishing the computational models for compound-protein interaction prediction...
Categorization of compound-protein interaction (CPI) prediction models. FASTA, FAST-All; SMILES, Simplified molecular input line entry system. Protein structure-based CPI prediction Compound-protein interactions occur in three-dimensional space. The 3D structural data of protein-compound interactions can ...