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Chemical Science
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November 26, 2019
A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules
Mohammad Atif Faiz Afzal, Aditya Sonpal, Mojtaba Haghighatlari, et al.
Chem
|
July 23, 2020
Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Methods
Mojtaba Haghighatlari, Jie Li, Farnaz Heidar-Zadeh, et al.
The Journal of Chemical Physics
|
May 5, 2023
Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data
Oufan Zhang, Mojtaba Haghighatlari, Jie Li, et al.
Communications Chemistry
|
August 11, 2020
Extended Experimental Inferential Structure Determination Method in Determining the Structural Ensembles of Disordered Protein States
James Lincoff, Mojtaba Haghighatlari, Mickael Krzeminski, et al.
Journal of Chemical Information and Modeling
|
June 6, 2024
Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design
Jie Li, Oufan Zhang, Kunyang Sun, et al.
The Journal of Physical Chemistry. B
|
February 25, 2022
Protein Dynamics to Define and Refine Disordered Protein Ensembles
Pavithra M Naullage, Mojtaba Haghighatlari, Ashley Namini, et al.
Communications Biology
|
July 31, 2025
HLAIIPred: cross-attention mechanism for modeling the interaction of HLA class II molecules with peptides
Mojtaba Haghighatlari, Nicholas Marze, Robert Seward, et al.
The Journal of Physical Chemistry. A
|
August 28, 2022
IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States
João M C Teixeira, Zi Hao Liu, Ashley Namini, et al.
Digital Discovery
|
June 30, 2022
NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces
Mojtaba Haghighatlari, Jie Li, Xingyi Guan, et al.
Scientific Data
|
May 17, 2022
A benchmark dataset for Hydrogen Combustion
Xingyi Guan, Akshaya Das, Christopher J Stein, et al.
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Search research articles
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Showing results (1-10 of 10) with videos related to
Sort By:
Page
of 1
Chemical Science
|
November 26, 2019
A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules
Mohammad Atif Faiz Afzal, Aditya Sonpal, Mojtaba Haghighatlari, et al.
Chem
|
July 23, 2020
Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Methods
Mojtaba Haghighatlari, Jie Li, Farnaz Heidar-Zadeh, et al.
The Journal of Chemical Physics
|
May 5, 2023
Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data
Oufan Zhang, Mojtaba Haghighatlari, Jie Li, et al.
Communications Chemistry
|
August 11, 2020
Extended Experimental Inferential Structure Determination Method in Determining the Structural Ensembles of Disordered Protein States
James Lincoff, Mojtaba Haghighatlari, Mickael Krzeminski, et al.
Journal of Chemical Information and Modeling
|
June 6, 2024
Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design
Jie Li, Oufan Zhang, Kunyang Sun, et al.
The Journal of Physical Chemistry. B
|
February 25, 2022
Protein Dynamics to Define and Refine Disordered Protein Ensembles
Pavithra M Naullage, Mojtaba Haghighatlari, Ashley Namini, et al.
Communications Biology
|
July 31, 2025
HLAIIPred: cross-attention mechanism for modeling the interaction of HLA class II molecules with peptides
Mojtaba Haghighatlari, Nicholas Marze, Robert Seward, et al.
The Journal of Physical Chemistry. A
|
August 28, 2022
IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States
João M C Teixeira, Zi Hao Liu, Ashley Namini, et al.
Digital Discovery
|
June 30, 2022
NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces
Mojtaba Haghighatlari, Jie Li, Xingyi Guan, et al.
Scientific Data
|
May 17, 2022
A benchmark dataset for Hydrogen Combustion
Xingyi Guan, Akshaya Das, Christopher J Stein, et al.
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of 1