3 citations · 3 across the 1 of their papers we have counts for
6 papers
Inference-time optimization for experiment-grounded protein ensemble generation
Advaith Maddipatla, Anar Rzayev, Marco Pegoraro +5
Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data. Recent expe…
Representing local protein environments with machine learning force fields
Meital Bojan, Sanketh Vedula, Advaith Maddipatla +5
The local structure of a protein strongly impacts its function and interactions with other molecules. Therefore, a concise, informative representation of a local protein environmen…
Seek and You Shall Fold
Nadav Bojan Sellam, Meital Bojan, Paul Schanda +1
Accurate protein structures are essential for understanding biological function, yet incorporating experimental data into protein generative models remains a major challenge. Most…
Inverse problems with experiment-guided AlphaFold
Advaith Maddipatla, Nadav Bojan Sellam, Meital Bojan +4
Proteins exist as a dynamic ensemble of multiple conformations, and these motions are often crucial for their functions. However, current structure prediction methods predominantly…
From Lab to Wrist: Bridging Metabolic Monitoring and Consumer Wearables for Heart Rate and Oxygen Consumption Modeling
Barak Gahtan, Sanketh Vedula, Gil Samuelly Leichtag +2
Understanding physiological responses during running is critical for performance optimization, tailored training prescriptions, and athlete health management. We introduce a compre…
Generative modeling of protein ensembles guided by crystallographic electron densities
Sai Advaith Maddipatla, Nadav Bojan Sellam, Sanketh Vedula +2
Proteins are dynamic, adopting ensembles of conformations. The nature of this conformational heterogenity is imprinted in the raw electron density measurements obtained from X-ray…