AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Ruth Nussinov*, Mingzhen Zhang, Yonglan Liu, Hyunbum Jang

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish
Article number103551
JournalDrug Discovery Today
Issue number6
StatePublished - Jun 2023


  • ESMfold
  • activating mutations
  • artificial intelligence
  • inhibitors
  • machine learning
  • orthosteric drugs


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