87 results on '"Tiwary, Pratyush"'
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2. JARVIS-Leaderboard: a large scale benchmark of materials design methods
3. From Latent Dynamics to Meaningful Representations
4. Enhanced Sampling of Crystal Nucleation with Graph Representation Learnt Variables
5. Enhanced Sampling with Machine Learning
6. Is the Local Ion Density Sufficient to Drive NaCl Nucleation from the Melt and Aqueous Solution?
7. Quantifying the Relevance of Long-Range Forces for Crystal Nucleation in Water
8. Recent advances in describing and driving crystal nucleation using machine learning and artificial intelligence
9. Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention
10. Learning high-dimensional reaction coordinates of fast-folding proteins using State Predictive Information Bottleneck and Bias Exchange Metadynamics
11. Metadynamics: A Unified Framework for Accelerating Rare Events and Sampling Thermodynamics and Kinetics
12. AlphaFold2-RAVE: From Sequence to Boltzmann Ranking
13. Computing committors in collective variables via Mahalanobis diffusion maps
14. Hinging on Success: Leveraging the Power of CAR T-Cell Therapy through In-Silico Modeling of Hinge Length and Epitope Location
15. Driving and characterizing nucleation of urea and glycine polymorphs in water
16. Metadynamics: A Unified Framework for Accelerating Rare Events and Sampling Thermodynamics and Kinetics
17. Path sampling of recurrent neural networks by incorporating known physics
18. Artificial intelligence in computational materials science
19. From data to noise to data for mixing physics across temperatures with generative artificial intelligence
20. AlphaFold2-RAVE: From sequence to Boltzmann ensemble
21. Quantifying Energetic and Entropic Pathways in Molecular Systems
22. Protein Flexibility and Dissociation Pathway Differentiation Can Explain Onset of Resistance Mutations in Kinases**
23. Interrogating RNA–Small Molecule Interactions with Structure Probing and Artificial Intelligence-Augmented Molecular Simulations
24. Protein Flexibility and Dissociation Pathway Differentiation Can Explain Onset Of Resistance Mutations in Kinases
25. Accelerating All-Atom Simulations and Gaining Mechanistic Understanding of Biophysical Systems through State Predictive Information Bottleneck
26. Influence of Long-Range Forces on the Transition States and Dynamics of NaCl Ion-Pair Dissociation in Water
27. Molecular recognition of methylated amino acids and peptides by Pillar[6]MaxQ
28. A Review of Enhanced Sampling Approaches for Accelerated Molecular Dynamics
29. Toward Automated Sampling of Polymorph Nucleation and Free Energies with the SGOOP and Metadynamics
30. SGOOP-d: Estimating Kinetic Distances and Reaction Coordinate Dimensionality for Rare Event Systems from Biased/Unbiased Simulations
31. Making High-Dimensional Molecular Distribution Functions Tractable through Belief Propagation on Factor Graphs
32. Interrogating RNA-small molecule interactions with structure probing and AI augmented-molecular simulations
33. Protein flexibility and dissociation pathway differentiation can explain onset of resistance mutations in kinases
34. Making high-dimensional molecular distribution functions tractable through Belief Propagation on Factor Graphs
35. Editorial: Molecular Dynamics and Machine Learning in Drug Discovery
36. State predictive information bottleneck
37. Confronting pitfalls of AI-augmented molecular dynamics using statistical physics
38. Learning molecular dynamics with simple language model built upon long short-term memory neural network
39. Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics
40. Confronting pitfalls of AI-augmented molecular dynamics using statistical physics
41. On the distance between A and B in molecular configuration space
42. Discovering loop conformational flexibility in T4 lysozyme mutants through artificial intelligence aided molecular dynamics
43. Understanding the role of predictive time delay and biased propagator in RAVE
44. Machine learning approaches for analyzing and enhancing molecular dynamics simulations
45. Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems
46. Reaction coordinates and rate constants for liquid droplet nucleation: Quantifying the interplay between driving force and memory
47. Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems
48. Past–future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics
49. Can One Trust Kinetic and Thermodynamic Observables from Biased Metadynamics Simulations?: Detailed Quantitative Benchmarks on Millimolar Drug Fragment Dissociation
50. Can one trust kinetic and thermodynamic observables from biased metadynamics simulations: detailed quantitative benchmarks on millimolar drug fragment dissociation
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