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Machine learning for protein folding and dynamics

Machine learning for protein folding and dynamics

22 November 2019
Frank Noé
Gianni De Fabritiis
C. Clementi
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Machine learning for protein folding and dynamics"

15 / 15 papers shown
Title
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic
  Interpolants
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
Allan dos Santos Costa
Ilan Mitnikov
Franco Pellegrini
Ameya Daigavane
Mario Geiger
Zhonglin Cao
Karsten Kreis
Tess E. Smidt
E. Küçükbenli
J. Jacobson
67
1
0
12 Oct 2024
Towards Learning Stochastic Population Models by Gradient Descent
Towards Learning Stochastic Population Models by Gradient Descent
J. N. Kreikemeyer
Philipp Andelfinger
A. Uhrmacher
68
1
0
10 Apr 2024
Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate
  Optimization Problems
Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate Optimization Problems
Rafael Monteiro
K. Sau
54
1
0
14 Sep 2023
A hybrid Decoder-DeepONet operator regression framework for unaligned
  observation data
A hybrid Decoder-DeepONet operator regression framework for unaligned observation data
Bo Chen
Chenyu Wang
Weipeng Li
Haiyang Fu
78
9
0
18 Aug 2023
Top-down machine learning of coarse-grained protein force-fields
Top-down machine learning of coarse-grained protein force-fields
Carles Navarro
Maciej Majewski
Gianni De Fabritiis
AI4CE
96
12
0
20 Jun 2023
DelBugV: Delta-Debugging Neural Network Verifiers
DelBugV: Delta-Debugging Neural Network Verifiers
R. Elsaleh
Guy Katz
106
2
0
29 May 2023
Solving Maxwell's Equation in 2D with Neural Networks with Local
  Converging Inputs
Solving Maxwell's Equation in 2D with Neural Networks with Local Converging Inputs
Harris Cobb
Hwi Lee
Yingjie Liu
34
5
0
06 Feb 2023
Applying Deep Reinforcement Learning to the HP Model for Protein
  Structure Prediction
Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
Kaiyuan Yang
Houjing Huang
Olafs Vandans
A. Murali
Fujia Tian
R. Yap
Liang Dai
116
10
0
27 Nov 2022
Transferring Chemical and Energetic Knowledge Between Molecular Systems
  with Machine Learning
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning
Sajjad Heydari
S. Raniolo
L. Livi
V. Limongelli
54
2
0
06 May 2022
Optimal Binary Classification Beyond Accuracy
Optimal Binary Classification Beyond Accuracy
Shashank Singh
Justin Khim
FaML
39
6
0
05 Jul 2021
BIGDML: Towards Exact Machine Learning Force Fields for Materials
BIGDML: Towards Exact Machine Learning Force Fields for Materials
H. E. Sauceda
Luis E Gálvez-González
Stefan Chmiela
L. O. Paz-Borbón
K. Müller
A. Tkatchenko
AI4CE
62
48
0
08 Jun 2021
Artificial intelligence techniques for integrative structural biology of
  intrinsically disordered proteins
Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins
A. Ramanathan
Henglong Ma
Akash Parvatikar
C. Chennubhotla
AI4CE
55
40
0
01 Dec 2020
Machine Learning Force Fields
Machine Learning Force Fields
Oliver T. Unke
Stefan Chmiela
H. E. Sauceda
M. Gastegger
I. Poltavsky
Kristof T. Schütt
A. Tkatchenko
K. Müller
AI4CE
133
935
0
14 Oct 2020
Deep Learning in Protein Structural Modeling and Design
Deep Learning in Protein Structural Modeling and Design
Wenhao Gao
S. Mahajan
Jeremias Sulam
Jeffrey J. Gray
84
161
0
16 Jul 2020
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with
  a Kernel Approach
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
Jiang Wang
Stefan Chmiela
K. Müller
Frank Noè
C. Clementi
138
46
0
04 May 2020
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