New publication: Predicting resistance is (not) futile Philip Fowler, 21st August 201921st August 2019 Our “First Reactions” article has been published in ACS Central Science. We discuss the paper, Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches, by Matteo Aldeghi, Vytautas Gapsys and Bert de Groot, which is in the same issue of the journal. Aldeghi et al. apply a series of methods to try and predict the effect of individual mutations on how well tyrosine kinase inhibitors bind to the Abl kinase. These methods include not only alchemical free energy methods but also machine learning. You can read it here. Share this:TwitterBlueskyEmailLinkedInMastodon Related antimicrobial resistance molecular dynamics publication
antimicrobial resistance New paper: quantitative measurement of effect of mutations on antibiotics in M. tuberculosis 15th January 202415th January 2024 The CRyPTIC project played a major role in the release by the WHO of their… Share this:TwitterBlueskyEmailLinkedInMastodon Read More
antimicrobial resistance New paper: Quantitative drug susceptibility testing for M. tuberculosis using unassembled sequencing data and machine learning 14th August 202414th August 2024 This is the last paper from the initial set of CRyPTIC publications following the project’s… Share this:TwitterBlueskyEmailLinkedInMastodon Read More
antimicrobial resistance New paper: detecting compensatory mutations in the RNAP of M. tuberculosis 5th February 20245th February 2024 In this paper, by examining testing the association between mutations known to be associate with… Share this:TwitterBlueskyEmailLinkedInMastodon Read More