Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Pratyush Tiwary"'
Autor:
Shams Mehdi, Pratyush Tiwary
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract In recent years, predictive machine learning models have gained prominence across various scientific domains. However, their black-box nature necessitates establishing trust in them before accepting their predictions as accurate. One promisi
Externí odkaz:
https://doaj.org/article/8f327dd28a31487893e23bbc65ebac13
Autor:
Kamal Choudhary, Daniel Wines, Kangming Li, Kevin F. Garrity, Vishu Gupta, Aldo H. Romero, Jaron T. Krogel, Kayahan Saritas, Addis Fuhr, Panchapakesan Ganesh, Paul R. C. Kent, Keqiang Yan, Yuchao Lin, Shuiwang Ji, Ben Blaiszik, Patrick Reiser, Pascal Friederich, Ankit Agrawal, Pratyush Tiwary, Eric Beyerle, Peter Minch, Trevor David Rhone, Ichiro Takeuchi, Robert B. Wexler, Arun Mannodi-Kanakkithodi, Elif Ertekin, Avanish Mishra, Nithin Mathew, Mitchell Wood, Andrew Dale Rohskopf, Jason Hattrick-Simpers, Shih-Han Wang, Luke E. K. Achenie, Hongliang Xin, Maureen Williams, Adam J. Biacchi, Francesca Tavazza
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-17 (2024)
Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful be
Externí odkaz:
https://doaj.org/article/dd95e89a6ffb48b6b7fe3dd56910340f
Publikováno v:
eLife, Vol 13 (2024)
Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2’s strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, d
Externí odkaz:
https://doaj.org/article/f45465984fba4e399ff46335a9866a57
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-10 (2022)
Adding prior experimentally or theoretically obtained knowledge to the training of recurrent neural networks may be challenging due to their feedback nature with arbitrarily long memories. The authors propose a path sampling approach that allows to i
Externí odkaz:
https://doaj.org/article/f27bb0948645418fa6a7d48ac4813ba5
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Artificial neural networks have been successfully used for language recognition. Tsai et al. use the same techniques to link between language processing and prediction of molecular trajectories and show capability to predict complex thermodynamics an
Externí odkaz:
https://doaj.org/article/b8be5637b2ea4eada6f89c34651af171
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-8 (2019)
Efficient sampling of rare events in all-atom molecular dynamics simulations remains a challenge. Here, the authors adapt the Predictive Information Bottleneck framework to sample biomolecular structure and dynamics through iterative rounds of biased
Externí odkaz:
https://doaj.org/article/caa466098ced4ead883d94055910040d
Publikováno v:
Frontiers in Molecular Biosciences, Vol 8 (2021)
Externí odkaz:
https://doaj.org/article/0967c38b447c4a0ab86253f57316a828
Publikováno v:
Applied and Computational Harmonic Analysis. 64:62-101
The study of rare events in molecular and atomic systems such as conformal changes and cluster rearrangements has been one of the most important research themes in chemical physics. Key challenges are associated with long waiting times rendering mole
Publikováno v:
Journal of Chemical Theory and Computation.
Publikováno v:
The Journal of Physical Chemistry B. 126:3950-3960
When examining dynamics occurring at non-zero temperatures, both energy and entropy must be taken into account while describing activated barrier crossing events. Furthermore, good reaction coordinates need to be constructed to describe different met