Publications
I am broadly interested in harnessing machine learning to address pressing questions in both computational biology and chemistry with a focus on building reliable ML models from unreliable data. Some of my current research interests include:
- Building foundation models for sequence data in biology and/ or small-molecules in chemistry.
- Leveraging foundation models to guide autonomous scientific discovery.
- Retrosynthetic planning and reasoning with reinforcement learning.
- Generally quantifying uncertainty/ reliability in AI4Science.
Below are my academic publications and research contributions:
Publications
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bioRxiv
Yash Chainani, Aidan Cornman, Yunha Hwang
bioRxiv, 2026. Work done in collaboration with Tatta Bio during my internship.
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Nat Commun
Yash Chainani, Jacob Diaz, Margaret Guilarte-Silva, Vincent Blay, Quan Zhang, William Sprague, Keith E. J. Tyo, Linda J. Broadbelt, Aindrila Mukhopadhyay, Jay D. Keasling, Hector Garcia Martin & Tyler W. H. Backman
Nature Communications, 2025.
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Mol Syst Des Eng
Yash Chainani, Zhuofu Ni, Kevin M. Shebek, Linda J. Broadbelt, Keith E. J. Tyo
Molecular Systems Design & Engineering, 2025.
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Curr Opin Biotechnol
Yash Chainani, Geoffrey Bonnanzio, Keith E. J. Tyo, Linda J. Broadbelt
Current Opinion in Biotechnology, 2023.