In Drug discovery today ; h5-index 68.0
Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success. Teaser AlphaFold was heralded as promising to transform drug discovery. To date it has not. We discuss what AlphaFold can and cannot do and suggest how to harness AlphaFold's power, circumventing its lack of structural flexibility.
Nussinov Ruth, Zhang Mingzhen, Liu Yonglan, Jang Hyunbum
2023-Mar-10
ESMfold, activating mutations, artificial intelligence, inhibitors, machine learning, molecular dynamics simulations, orthosteric drugs
