Zobrazeno 1 - 10
of 379
pro vyhledávání: '"Tiwary Pratyush"'
Autor:
Jain Animesh, Tiwary Pratyush, Mathew Divya Maria, Garg Gaurang, Chakrabarty Deeksha, Sashikanth Sanskrita
Publikováno v:
Indian Journal of Community Medicine, Vol 49, Iss 7, Pp 81-81 (2024)
Background: Diabetes mellitus (DM) disrupts insulin production and function leads to hyperglycaemia and subsequently exacerbating the risk of complications. Studies highlight hyperglycaemia’s impact on neutrophil function, predisposing diabetic pat
Externí odkaz:
https://doaj.org/article/5427cb70ad4c487cae772c79fb4665e0
Autor:
Beyerle, Eric R., Tiwary, Pratyush
Contemporary work implies generative machine learning models are capable of learning the phase behavior in condensed matter systems such as the Ising model. In this Letter, we utilize a score-based modeling procedure called Thermodynamic Maps to desc
Externí odkaz:
http://arxiv.org/abs/2410.21034
Molecular dynamics simulations offer detailed insights into atomic motions but face timescale limitations. Enhanced sampling methods have addressed these challenges but even with machine learning, they often rely on pre-selected expert-based features
Externí odkaz:
http://arxiv.org/abs/2409.11843
The recent surge in Generative Artificial Intelligence (AI) has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant progress in sampling molecular structures across chemical species, developing f
Externí odkaz:
http://arxiv.org/abs/2409.03118
Autor:
Wang, Dedi, Tiwary, Pratyush
The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a low-dimensional colle
Externí odkaz:
http://arxiv.org/abs/2406.14839
We investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations reveal that crystallization in the model colloidal system studied here,
Externí odkaz:
http://arxiv.org/abs/2404.17722
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, des
Externí odkaz:
http://arxiv.org/abs/2404.07102
Markov state models (MSMs) are valuable for studying dynamics of protein conformational changes via statistical analysis of molecular dynamics (MD) simulations. In MSMs, the complex configuration space is coarse-grained into conformational states, wi
Externí odkaz:
http://arxiv.org/abs/2404.02856
Autor:
Wang, Ruiyu, Tiwary, Pratyush
In this work we examine the nucleation from NaCl aqueous solutions within nano-confined environments, employing enhanced sampling molecular dynamics simulations integrated with machine learning-derived reaction coordinates. Through our simulations, w
Externí odkaz:
http://arxiv.org/abs/2403.00925
Autor:
Zou, Ziyue, Tiwary, Pratyush
In this study, we present a graph neural network-based learning approach using an autoencoder setup to derive low-dimensional variables from features observed in experimental crystal structures. These variables are then biased in enhanced sampling to
Externí odkaz:
http://arxiv.org/abs/2310.07927