| Year |
Citation |
Score |
| 2025 |
Wang Y, Sun K, Li J, Guan X, Zhang O, Bagni D, Zhang Y, Carlson HA, Head-Gordon T. A workflow to create a high-quality protein-ligand binding dataset for training, validation, and prediction tasks. Digital Discovery. PMID 40190768 DOI: 10.1039/d4dd00357h |
0.487 |
|
| 2025 |
Wang Y, Sun K, Li J, Guan X, Zhang O, Bagni D, Head-Gordon T. A Workflow to Create a High-Quality Protein-Ligand Binding Dataset for Training, Validation, and Prediction Tasks. Arxiv. PMID 40093369 |
0.492 |
|
| 2024 |
Ptaszek AL, Li J, Konrat R, Platzer G, Head-Gordon T. UCBShift 2.0: Bridging the Gap from Backbone to Side Chain Protein Chemical Shift Prediction for Protein Structures. Journal of the American Chemical Society. 146: 31733-31745. PMID 39531038 DOI: 10.1021/jacs.4c10474 |
0.547 |
|
| 2024 |
Li J, Zhang O, Sun K, Wang Y, Guan X, Bagni D, Haghighatlari M, Kearns FL, Parks C, Amaro RE, Head-Gordon T. Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design. Journal of Chemical Information and Modeling. PMID 38843070 DOI: 10.1021/acs.jcim.4c00634 |
0.514 |
|
| 2024 |
Li J, Liang J, Wang Z, Ptaszek AL, Liu X, Ganoe B, Head-Gordon M, Head-Gordon T. Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning. Journal of Chemical Theory and Computation. PMID 38331423 DOI: 10.1021/acs.jctc.3c01256 |
0.573 |
|
| 2023 |
Liu ZH, Zhang O, Teixeira JMC, Li J, Head-Gordon T, Forman-Kay JD. SPyCi-PDB: A modular command-line interface for back-calculating experimental datatypes of protein structures. Journal of Open Source Software. 8. PMID 38726305 DOI: 10.21105/joss.04861 |
0.452 |
|
| 2023 |
Liu ZH, Teixeira JMC, Zhang O, Tsangaris TE, Li J, Gradinaru CC, Head-Gordon T, Forman-Kay JD. Local Disordered Region Sampling (LDRS) for Ensemble Modeling of Proteins with Experimentally Undetermined or Low Confidence Prediction Segments. Bioinformatics (Oxford, England). PMID 38060268 DOI: 10.1093/bioinformatics/btad739 |
0.502 |
|
| 2023 |
Li J, Guan X, Zhang O, Sun K, Wang Y, Bagni D, Head-Gordon T. Leak Proof PDBBind: A Reorganized Dataset of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction. Arxiv. PMID 37645037 |
0.508 |
|
| 2023 |
Liu ZH, Teixeira JMC, Zhang O, Tsangaris TE, Li J, Gradinaru CC, Head-Gordon T, Forman-Kay JD. Local Disordered Region Sampling (LDRS) for Ensemble Modeling of Proteins with Experimentally Undetermined or Low Confidence Prediction Segments. Biorxiv : the Preprint Server For Biology. PMID 37546943 DOI: 10.1101/2023.07.25.550520 |
0.505 |
|
| 2023 |
Zhang O, Haghighatlari M, Li J, Liu ZH, Namini A, Teixeira JMC, Forman-Kay JD, Head-Gordon T. Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data. The Journal of Chemical Physics. 158. PMID 37144719 DOI: 10.1063/5.0141474 |
0.528 |
|
| 2023 |
Wong J, Ganoe B, Liu X, Neudecker T, Lee J, Liang J, Wang Z, Li J, Rettig A, Head-Gordon T, Head-Gordon M. An in-silico NMR laboratory for nuclear magnetic shieldings computed via finite fields: Exploring nucleus-specific renormalizations of MP2 and MP3. The Journal of Chemical Physics. 158. PMID 37114707 DOI: 10.1063/5.0145130 |
0.48 |
|
| 2023 |
Li J, Zhang O, Lee S, Namini A, Liu ZH, Teixeira JMC, Forman-Kay JD, Head-Gordon T. Learning Correlations between Internal Coordinates to Improve 3D Cartesian Coordinates for Proteins. Journal of Chemical Theory and Computation. PMID 36749957 DOI: 10.1021/acs.jctc.2c01270 |
0.533 |
|
| 2023 |
Liang J, Wang Z, Li J, Wong J, Liu X, Ganoe B, Head-Gordon T, Head-Gordon M. Efficient Calculation of NMR Shielding Constants Using Composite Method Approximations and Locally Dense Basis Sets. Journal of Chemical Theory and Computation. PMID 36594660 DOI: 10.1021/acs.jctc.2c00933 |
0.479 |
|
| 2022 |
Teixeira JMC, Liu ZH, Namini A, Li J, Vernon RM, Krzeminski M, Shamandy AA, Zhang O, Haghighatlari M, Yu L, Head-Gordon T, Forman-Kay JD. IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States. The Journal of Physical Chemistry. A. PMID 36030416 DOI: 10.1021/acs.jpca.2c03726 |
0.514 |
|
| 2022 |
Haghighatlari M, Li J, Guan X, Zhang O, Das A, Stein CJ, Heidar-Zadeh F, Liu M, Head-Gordon M, Bertels L, Hao H, Leven I, Head-Gordon T. NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces. Digital Discovery. 1: 333-343. PMID 35769203 DOI: 10.1039/d2dd00008c |
0.706 |
|
| 2022 |
Guan X, Das A, Stein CJ, Heidar-Zadeh F, Bertels L, Liu M, Haghighatlari M, Li J, Zhang O, Hao H, Leven I, Head-Gordon M, Head-Gordon T. A benchmark dataset for Hydrogen Combustion. Scientific Data. 9: 215. PMID 35581204 DOI: 10.1038/s41597-022-01330-5 |
0.7 |
|
| 2022 |
Naullage PM, Haghighatlari M, Namini A, Teixeira JMC, Li J, Zhang O, Gradinaru CC, Forman-Kay JD, Head-Gordon T. Protein Dynamics to Define and Refine Disordered Protein Ensembles. The Journal of Physical Chemistry. B. PMID 35213160 DOI: 10.1021/acs.jpcb.1c10925 |
0.488 |
|
| 2021 |
Stauch T, Ganoe B, Wong J, Lee J, Rettig A, Liang J, Li J, Epifanovsky E, Head-Gordon T, Head-Gordon M. Molecular Magnetizabilities Computed Via Finite Fields: Assessing Alternatives to MP2 and Revisiting Magnetic Exaltations in Aromatic and Antiaromatic Species. Molecular Physics. 119. PMID 35264815 DOI: 10.1080/00268976.2021.1990426 |
0.483 |
|
| 2021 |
Wang X, Li J, Ha HD, Dahl JC, Ondry JC, Moreno-Hernandez I, Head-Gordon T, Alivisatos AP. AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles. Jacs Au. 1: 316-327. PMID 33778811 DOI: 10.1021/jacsau.0c00030 |
0.451 |
|
| 2020 |
Li J, Bennett KC, Liu Y, Martin MV, Head-Gordon T. Accurate prediction of chemical shifts for aqueous protein structure on "Real World" data. Chemical Science. 11: 3180-3191. PMID 34122823 DOI: 10.1039/c9sc06561j |
0.734 |
|
| 2020 |
Haghighatlari M, Li J, Heidar-Zadeh F, Liu Y, Guan X, Head-Gordon T. Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Methods. Chem. 6: 1527-1542. PMID 32695924 DOI: 10.1016/J.Chempr.2020.05.014 |
0.586 |
|
| 2019 |
Liu S, Li J, Bennett K, Ganoe B, Stauch T, Head-Gordon M, Hexemer A, Ushizima D, Head-Gordon T. A Multi-Resolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography. The Journal of Physical Chemistry Letters. PMID 31305081 DOI: 10.1021/Acs.Jpclett.9B01570 |
0.73 |
|
| Show low-probability matches. |