“…Developments in computational approaches are also likely to be key to unlocking the potential of pharmacological approaches to understand protein kinase signaling networks. Efforts to rationalize the inhibitor libraries used for phenotypic studies, either to create libraries of high selectivity for each target kinase ( Moret et al, 2019 ; Wells et al, 2021 ) or suitable for deconvolution approaches ( Gujral et al, 2014b ; Rata et al, 2020 ; Watson et al, 2020 ; Golkowski et al, 2023 ), will help optimize the trade-off between library size and experimental practicality. Further work should help to determine which algorithms are most effective for identifying relevant kinases and pathways from inhibitor-induced perturbations in the phosphoproteome, for target deconvolution based on phenotypic screens, and for rational design of polypharmacological agents and drug combinations ( Gujral et al, 2014b ; Hernandez-Armenta et al, 2017 ; Tang, 2017 ; Rocca and Kholodenko, 2021 ) Finally, machine learning is poised to provide notable advances in determining features of kinase networks that predict drug efficacy for personalized medicine, as well as in these other areas.…”