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#ligand — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #ligand, aggregated by home.social.

  1. Optibrium introduces graphical interface for QuanSA to enhance ligand-based affinity predictions

    Optibrium, a leading developer of software and AI solutions for molecular design, today announced a new QuanSA™ plugin…
    #NewsBeep #News #Technology #AU #Australia #bindingaffinity #drugdiscovery #Ligand #Molecule #protein #Software
    newsbeep.com/au/562694/

  2. on some level, all carbenes are kinda insane. but hexaphenylcarbodiphosphorane (technically a carbone) might just be the most insane of all. Formally neutral with not one but _two_ lone pairs??? GTFOH
    #chemistry #carbene #ligand

  3. on some level, all carbenes are kinda insane. but hexaphenylcarbodiphosphorane (technically a carbone) might just be the most insane of all. Formally neutral with not one but _two_ lone pairs??? GTFOH
    #chemistry #carbene #ligand

  4. on some level, all carbenes are kinda insane. but hexaphenylcarbodiphosphorane (technically a carbone) might just be the most insane of all. Formally neutral with not one but _two_ lone pairs??? GTFOH
    #chemistry #carbene #ligand

  5. on some level, all carbenes are kinda insane. but hexaphenylcarbodiphosphorane (technically a carbone) might just be the most insane of all. Formally neutral with not one but _two_ lone pairs??? GTFOH
    #chemistry #carbene #ligand

  6. on some level, all carbenes are kinda insane. but hexaphenylcarbodiphosphorane (technically a carbone) might just be the most insane of all. Formally neutral with not one but _two_ lone pairs??? GTFOH
    #chemistry #carbene #ligand

  7. My favorite molecular #protein-#ligand #docking method, #DiffDock, has been updated! The new DiffDock-L, provides a significant improvement in performance and generalization capacity.

    Importantly., this new method comes with the new #DockGen benchmark, aiming to provide better evaluation metrics and help improve #generalization of #ML docking models by accounting for sequence-dissimilar proteins with very similar binding pockets in training/test splits.

    arxiv.org/abs/2402.18396

  8. Has anyone looked at CINS (doi.org/10.1371/journal.pcbi.1)?

    Learns a Bayesian network of cell type dependencies from changes in cell type proportions (#scRNAseq) in case-control studies. Causal ligands from cell-cell edges are predicted by LASSO regression of #ligand and predicted response genes (using NicheNet's ligand-reponse network).
    Cool idea, weird lack of validation in their paper... But I guess that's the challenge in #CellCellInteraction prediction - validation is _hard_

  9. Finally read the REMI paper by Alice Yu (aliceomics@twitter) et al. Calculating the partial correlation structure of #ligand #receptor interactions across #scRNAseq samples (cancer, in their case) to _more_specifically_ identify context-dependent interactions. #CellCellInteraction prediction has a specificity problem, and this method outperforms NicheNet, which uses predicted transcriptional response to improve specificity of predictions.

    twitter.com/PlevritisLab/statu

  10. We used #STRINGdb to build a zebrafish-specific ligand-receptor interactome database. We also added human interactions from IID database from Jurisica group #ligand #receptor 12/n

  11. We used #STRINGdb to build a zebrafish-specific ligand-receptor interactome database. We also added human interactions from IID database from Jurisica group #ligand #receptor 12/n

  12. Nice paper from the Schapira lab identifying potential #ligand binding pockets for future #PROTAC development.

    sciencedirect.com/science/arti