← Sarah Alamdari

Learning Protein Function

Building better protein models only matters if we can connect them to biological function. I develop methods to predict, annotate, and benchmark protein function. This work ranges from fitness landscape prediction to multimodal protein function annotation so that protein models stay comparable, reproducible, and grounded in real-world tasks.

Publications

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  FLIP2: Expanding protein fitness landscape benchmarks for real-world machine learning applications

  K Didi, S Alamdari, AX Lu, B Wittmann, KE Johnston, AP Amini, A Madani, M Czeneszew, C Dallago, KK Yang · bioRxiv, 2026

  Oral presentation at ICML 2026 (168 of 23,918 submissions, ~top 3%).

  PaperCodeWebsite
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  Closing the loop: Experimentally validated methods in artificial intelligence–driven protein design

  CW Kosonocky, S Alamdari, KK Yang, AP Amini · Current Opinion in Structural Biology, 2026

  Paper
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  ProtNote: a multimodal method for protein–function annotation

  S Char, N Corley, S Alamdari, KK Yang, AP Amini · Bioinformatics, 2025

  PaperCode
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  Deep learning guided design of protease substrates

  C Martin-Alonso^*, S Alamdari^*, TS Samad, KK Yang, SN Bhatia^†, AP Amini^† · Nature Communications, 2025

  PaperCodePress