Author

J. Fass

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 6

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Approach:deep learning· 4 Approach:statistical model· 4 Research Focus:benchmarking· 3 Research Focus:drug response / drug discovery· 2

Frequent Co-Authors

J. D. Chodera· 5 shared M. Wieder· 3 shared P. B. Grinaway· 2 shared A. E. Roitberg· 1 shared A. S. Rustenburg· 1 shared B. Leimkuhler· 1 shared C. D. Stern· 1 shared C. I. Bayly· 1 shared

Activity by Year

2018-2021 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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5 shared papersBiophysics

J. D. Chodera

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6 papers

Recent·Most cited

Teaching free energy calculations to learn from experimental data

M. Wieder, J. Fass, J. D. Chodera

This paper uses quantum machine learning potentials to improve alchemical free energy calculations by overcoming limitations in force fields and enabling learning from experimental data.

bioRxiv · Aug 24, 2021 · PreprintBiophysics

Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution

M. Wieder, J. Fass, J. D. Chodera

This paper uses quantum machine learning to predict tautomer ratios in solution by correcting limitations in quantum chemical thermochemistry approximations.

bioRxiv · Oct 24, 2020Biophysics

Capturing non-local through-bond effects in molecular mechanics force fields I: Fragmenting molecules for quantum chemical torsion scans

C. D. Stern, C. I. Bayly, D. G. A. Smith, J. Fass, L.-P. Wang, D. L. Mobley, J. D. Chodera

The paper introduces a method to assess and improve fragmentation schemes for quantum chemical torsion scans by using fractional Wiberg bond orders to preserve chemical environment accuracy.

bioRxiv · Aug 27, 2020 · PreprintBiophysics

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials

D. A. Rufa, H. E. Bruce Macdonald, J. Fass, M. Wieder, P. B. Grinaway, A. E. Roitberg, O. Isayev, J. D. Chodera

This paper improves the accuracy of alchemical free energy calculations for protein-ligand binding by combining machine learning and molecular mechanics potentials, reducing errors from 0.97 to 0.47 kcal mol-1.

bioRxiv · Jul 29, 2020 · PreprintBiophysics

Quantifying configuration-sampling error in Langevin simulations of complex molecular systems

J. Fass, D. Sivak, G. E. Crooks, K. A. Beauchamp, B. Leimkuhler, J. Chodera

The paper introduces a method to quantify configuration-space sampling errors in Langevin simulations and validates it against a Monte Carlo estimator.

bioRxiv · Apr 15, 2018Biophysics

Biomolecular simulations under realistic macroscopic salt conditions

G. A. Ross, A. S. Rustenburg, P. B. Grinaway, J. Fass, J. D. Chodera

This paper introduces a Monte Carlo osmostat to simulate biomolecules under dynamic salt conditions, showing significant differences in salt concentration distributions compared to fixed-salt simulations.

bioRxiv · Mar 18, 2018Biophysics