40 citations · 70 across the 9 of their papers we have counts for
9 papers
A Workflow to Create a High-Quality Protein-Ligand Binding Dataset for Training, Validation, and Prediction Tasks
Yingze Wang, Kunyang Sun, Jie Li +4
Development of scoring functions (SFs) used to predict protein-ligand binding energies requires high-quality 3D structures and binding assay data for training and testing their par…
Leak Proof PDBBind: A Reorganized Dataset of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction
Jie Li, Xingyi Guan, Oufan Zhang +4
The majority of machine learning scoring functions used in drug discovery for predicting protein-ligand binding poses and affinities have been trained on the PDBBind dataset. Howev…
Efficient Calculation of NMR Shielding Constants Using Composite Method Approximations and Locally Dense Basis Sets
Jiashu Liang, Zhe Wang, Jie Li +5
This paper presents a systematic study of applying composite method approximations with locally dense basis sets (LDBS) to efficiently calculate NMR shielding constants in small an…
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…
Learning Correlations between Internal Coordinates to improve 3D Cartesian Coordinates for Proteins
Jie Li, Oufan Zhang, Seokyoung Lee +5
We consider a generic representation problem of internal coordinates (bond lengths, valence angles, and dihedral angles) and their transformation to 3-dimensional Cartesian coordin…
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…
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…
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…
A Multi-Resolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography
Shuai Liu, Jie Li, Kochise C. Bennett +6
We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data repr…