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 ...
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...
While compound representations, such as SMILES, graphs, and various fingerprints [55,56] have been extensively benchmarked, until recently, fewer studies had concentrated on protein descriptors. Most algorithms for compound–protein interaction prediction take the full amino acid sequence of a protein ...
Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design and development, as genome-scale experimental validation of CPIs is not only time-consuming but also prohibitively expensive. With the availability of an increasing number of validated interactions, the...
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, ...
MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction Background and Objective: Protein-protein interaction (PPI) is a vital process in all living cells, controlling essential cell functions such as cell cycle... S Ghosh,P...
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 ...
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...
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
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments https://doi.org/10.1093/bioinformatics/btaa524 - WangYitian123/transformerCPI