529 citations · 601 across the 12 of their papers we have counts for
12 papers
Deep Learning of Proteins with Local and Global Regions of Disorder
Oufan Zhang, Zi Hao Liu, Julie D Forman-Kay +1
Although machine learning has transformed protein structure prediction of folded protein ground states with remarkable accuracy, intrinsically disordered proteins and regions (IDPs…
SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models
Kunyang Sun, Dorian Bagni, Joseph M. Cavanagh +6
Generative machine learning models for exploring chemical space have shown immense promise, but many molecules they generate are too difficult to synthesize, making them impractica…
Biological Insights from Integrative Modeling of Intrinsically Disordered Protein Systems
Zi Hao Liu, Maria Tsanai, Oufan Zhang +2
Intrinsically disordered proteins and regions are increasingly appreciated for their abundance in the proteome and the many functional roles they play in the cell. In this short re…
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…
Computational Methods to Investigate Intrinsically Disordered Proteins and their Complexes
Zi Hao Liu, Maria Tsanai, Oufan Zhang +2
In 1999 Wright and Dyson highlighted the fact that large sections of the proteome of all organisms are comprised of protein sequences that lack globular folded structures under phy…
A Curated Rotamer Library for Common Post-Translational Modifications of Proteins
Oufan Zhang, Shubhankar A. Naik, Zi Hao Liu +2
Sidechain rotamer libraries of the common amino acids of a protein are useful for folded protein structure determination and for generating ensembles of intrinsically disordered pr…
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…
ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis
Zhiling Zheng, Oufan Zhang, Christian Borgs +2
We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientifi…
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…