@article{TEXTUAL,
      recid = {13472},
      author = {Liu, Changchang and Kutchukian, Peter and Nguyen, Nhan D.  and AlQuraishi, Mohammed and Sorger, Peter K.},
      title = {A Hybrid Structure-Based Machine Learning Approach for  Predicting Kinase Inhibition by Small Molecules},
      journal = {Journal of Chemical Information and Modeling},
      address = {2023-08-18},
      number = {TEXTUAL},
      abstract = {Kinases have been the focus of drug discovery programs for  three decades leading to over 70 therapeutic kinase  inhibitors and biophysical affinity measurements for over  130,000 kinase-compound pairs. Nonetheless, the precise  target spectrum for many kinases remains only partly  understood. In this study, we describe a computational  approach to unlocking qualitative and quantitative  kinome-wide binding measurements for structure-based  machine learning. Our study has three components: (i) a  Kinase Inhibitor Complex (KinCo) data set comprising in  silico predicted kinase structures paired with experimental  binding constants, (ii) a machine learning loss function  that integrates qualitative and quantitative data for model  training, and (iii) a structure-based machine learning  model trained on KinCo. We show that our approach  outperforms methods trained on crystal structures alone in  predicting binary and quantitative kinase-compound  interaction affinities; relative to structure-free methods,  our approach also captures known kinase biochemistry and  more successfully generalizes to distant kinase sequences  and compound scaffolds.},
      url = {http://knowledge.uchicago.edu/record/13472},
}