AgAnt: A computational tool to assess Agonist/Antagonist mode of interaction
Aggarwal, B.; Ray, A.
Show abstract
Activity modulation of proteins is an essential biochemical process in cell. The interplay of the protein, as receptor, and its corresponding ligand dictates the functional effect. An agonist molecule when bound to a receptor produces a response within the cell while an antagonist will block the binding site/produce the opposite effect of that of an agonist. Complexity grows with scenarios where some ligands might act as an agonist in certain conditions while as an antagonist in others [1, 3]. It is imperative to decipher the receptor-ligand functional effect for understanding native biochemical processes as well as for drug discovery. Experimental activity determination is a time extensive process and computational solution towards prediction of activity specific to the receptor-ligand interaction would be of wide interest.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques 95%
- Classification of protein binding ligands using structural dispersion of binding site atoms from principal axes 95%
- Antivirals for Monkeypox Virus: Proposing an Effective Machine/Deep Learning Framework 94%
Similar papers in this journal
- Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions 95%
- PDAUG - a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling 94%
- RAFTS3G - An efficient and versatile clustering software to analyses in large protein datasets 94%
Similar papers in this journal
- Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum 95%
- Pred-AHCP: Robust feature selection enabled Sequence Specific Prediction of Anti-Hepatitis C Peptides via Machine Learning 94%
- Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening 93%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.