activity
20192022
most citedLearning to Evolve Structural Ensembles of Unfolded and Disordered Proteins Using Experimental Solution Data

40 citations · 45 across the 5 of their papers we have counts for

collaborators

6 papers

physics.bio-ph2022★ 40 cited

Learning to Evolve Structural Ensembles of Unfolded and Disordered Proteins Using Experimental Solution Data

Oufan Zhang, Mojtaba Haghighatlari, Jie Li +5

We have developed a Generative Recurrent Neural Networks (GRNN) that learns the probability of the next residue torsions from the pre…

q-bio.BM2021

Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design

Jie Li, Oufan Zhang, Yingze Wang +8

The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine…

physics.chem-ph2021★ 1 cited

NewtonNet: A Newtonian message passing network for deep learning of interatomic potentials and forces

Mojtaba Haghighatlari, Jie Li, Xingyi Guan +10

We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of dir…

physics.chem-ph2020★ 2 cited

Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms

Mojtaba Haghighatlari, Jie Li, Farnaz Heidar-Zadeh +3

Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we…

physics.bio-ph2019★ 2 cited

Extended Experimental Inferential Structure Determination Method for Evaluating the Structural Ensembles of Disordered Protein States

James Lincoff, Mickael Krzeminski, Mojtaba Haghighatlari +5

Characterization of proteins with intrinsic or unfolded state disorder comprises a new frontier in structural biology, requiring the characterization of diverse and dynamic structu…

physics.data-an2019

Advances of Machine Learning in Molecular Modeling and Simulation

Mojtaba Haghighatlari, Johannes Hachmann

In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, com…