Dr. Junmei Wang is an Associate Professor of Pharmaceutical Sciences and a member of the Computational Chemical Genomics Screening Center (www.CBLigand.org/CCGS), University of Pittsburgh School of Pharmacy. Dr. Wang received his PhD from Peking University in China, and his was trained as a postdoctoral associate with Dr. Peter Kollman at University of California San Francisco. Before joining University of Pittsburgh, Dr. Wang was an associate professor at University of Texas Southwestern Medical Center. Dr. Wang is also a long-term active developer of the Amber force field and software (www.ambermd.org). He and other collaborators developed a set of popular AMBER force fields, including FF99, GAFF/GAFF2 and polarizable FF based on Thole's dipole-interaction models as well as the Antechamber module implemented in AMBER software packages.
- PhD from Peking University in China
Education & Training
Computational Chemistry and Biophysics, Drug Discovery and Development, Systems Pharmacology and Outcomes, specifically
1. Molecular mechanics force field (MMFF)
2. MMFF-based scoring function
3. computer-aided drug design
4. Molecular simulations of biomolecular systems and processes
5. Machine learning and AI in biomedical research
6. Computational systems pharmacology and pharmacometrics
7. PK/PD modeling and simulations
2. Certara/SimCYP 2022 Academic Award’s for “Most-Effective Teaching Application”, 2022
3. Selected as the Graduate Faculty Member of The Year at the School of Pharmacy, University of Pittsburgh, April 2024.
2024
1. Niu, T.; He, X.; Han, F.; Wang, L.; Wang, J. M.* Development and Test of Highly Accurate Endpoint Free Energy Methods. 3: Partition Coefficient Prediction Using a Poisson-Boltzmann Method Combined with Solvent Accessible Surface Area Model for SAMPL Challenges. Physical Chemistry Chemical Physics, 2024, 26, 85-94.
2. Wang, J. M., Blount, P.; Hou, T.; Sokabe, M. In silico Gating Mechanism Studies and Modulator Discovery for MscL. Frontiers in Chemistry, 2024, 12:1376617.
3. Xue, B.; Yang, Q.; Zhang, Q.; Wan, X.; Fang, D.; Lin, X.; Sun, G.; Gobbo, G.; Cao, F.; Mathiowetz, A. M.; Burke, B. J.; Kumpf, R. A.; Rai, B. K.; Wood, G. P. F.; Pickard IV, F. C.; Wang, J. M.; Zhang, P.; Ma, J.; Jiang, Y. A.; Wen, S.; Hou, X.; Zou, J.; Yang, M. Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation. J. Chem. Theory Comput. 2024, 20, 2, 799–818.
4. Oniani, D.; Hilsman, J.; Zang, C. Wang, J. M.; Cai, L.; Zawala, J.; Wang Y.; Emerging opportunities of using large language models for translation between drug molecules and indications. Scientific Reports, 2024, 14: 10738.
5. Duan, Y.; Niu, T.; Wang, J. M.; Cieplak, P.; Luo, R. PCMRESP: A Method for Polarizable Force Field Parameter Development and Transferability of the Polarizable Gaussian Multipole Models Across Multiple Solvents. J. Chem. Theory Comput. 2024, 20, 2820-2829
6. Luo, Z.; Wan, Z.; Ren, P.; Zhang, B.; Huang, Y.; West III, R. E.; Huang, H.; Chen, Y.; Nolin, T. D.; Xie, W.; Wang, J. M.; Li, S.; Sun, J. Advanced Science, 2024, 11(19):2307940
7. Wang, L.; He, X.; Ji, B.; Han, F.; Niu, T.; Cai, L.; Zhai, J.; Hao, D.; Wang, J. M. Geometry Optimization Algorithm in Conjunction with ANI-2x Potential Facilitates the Structure-Based Virtual Screening and Binding Mode Prediction. Biomolecules, 2024, 14(6):648.
8. Cai L.; Zhai, J.; Ji, B.; Han, F.; Niu, T.; Wang, L.; Wang, J. M. Intranasal Diamorphine Population Pharmacokinetics Modeling and Simulation in Pediatric Breakthrough Pain. CPT: Pharmacometrics & Systems Pharmacology. In Press.
2023
1. Sun, J.; Wan, Z.; Huang, H.; West III, R. E.; Zhang, M.; Zhang, B.; Cai, X.; Zhang, Z.; Luo, Z.; Chen Y.; Zhang, Y.; Xie, W.; Yang, D.; Nolin, T.; Wang, J. M.; Li, S. Overcoming Pancreatic Cancer Immune Resistance by Codelivery of CCR2 Antagonist Using a STING-Activating Gemcitabine-Based Nanocarrier. Materials Today, 2023, 62, 33-50.
2. Wang, J. M.; Blount, P. Feeling the tension: the bacterial mechanosensitive channel of large conductance as a model system and drug target. Current Opinion in Physiology, 2023, 31, 100627.
3. He, X.; Man, V. H.; Gao, J.; Wang, J. M.* Investigation of the Structure of Full-Length Tau Proteins with Coarse-Grained and All-Atom Molecular Dynamics Simulations. ACS Chemical Neuroscience, 2023, 14, 2, 209–217.
4. Man, V. H.;* He, X.; Gao, J.; Wang, J. M.* Phosphorylation of Tau R2 Repeat Destabilizes Its Binding to Microtubules: A Molecular Dynamics Simulation Study. ACS Chemical Neuroscience, 2023, 14, 3, 458–467.
5. Man, V. H.;* He, X.; Han, F.; Cai, L.; Wang, L.; Niu, T.; Zhai, J.; Ji, B.; Gao, J.; Wang, J. M.* Phosphorylation at Ser289 Enhances the Oligomerization of Tau Repeat R2. Journal of Chemical Information & Modeling, 2023, 63, 4, 1351–1361.
6. Sun, Y.; He, X.; Hou, T.; Cai, L.; Man, V. H.; Wang, J. M.* Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method. Journal of Computational Chemistry, 2023, 2023, 44,1334-1346.
7. Sun, Y.; He, X.; Hou, T.; Cai, L.; Man, V. H.; Wang, J. M.* Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol–water partition coefficient (logP) for druglike molecules using MM-PBSA method. Journal of Computational Chemistry, 2023, 44(13):1300-1311.
8. Man, H. M.*; He, X.; Nguyen, P. H.; Sagui, C.; Roland, C.; Xie, X.-Q.; Wang, J. M.* Unpolarized laser method for infrared spectrum calculation of amide I Cdouble bondO bonds in proteins using molecular dynamics simulation. Computers in Biology and Medicine, 2023, 106902.
9. Wang, J.; Zeng, Y.; Sun, H.; Wang, J. M.; Wang, X.; Jin, R.; Wang, M.; Zhang, X.; Cao, D.; Chen, X.; Hsieh, C.-Y.; Hou, T. Molecular Generation with Reduced Labeling through Constraint Architecture. Journal of Chemical Information and Modeling, 2023, 63(11):3319-3327.
10. Zhai, J.; Man, V. H.; Ji, B., Cai, L., Wang, J. M.* Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism. Drug Discovery Today, 2023, 103728.
11. Huang, Y.; Wen, J.; Ramirez, L.-M.; Gümüşdil, E.; Pokhrel, P.; Man, V. H.; Ye, H.; Han, Y.; Liu, Y.; Li, P.; Su, Z.; Wang, J. M.; Mao, H.; Zweckstetter, M.; Perrett, S.; Wu, S.; Gao, M. Methylene blue accelerates liquid-to-gel transition of tau condensates impacting tau function and pathology. Natural Communications, 2023, 14, Article number: 5444.
12. Ge, H.;* Ji, H.; Fang, J.; Wang, J.; Li, J.;* Wang, J. M.;* Discovery of Potent and Selective CB2 Agonists Utilizing a Function-Based Computational Screening Protocol. ACS Chemical Neuroscience, 2023, 14, 21, 3941–3958.
13. Han, F; Hao, D.; He, X.; Wang, L.; Niu, T.; Wang, J. M. * Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential. J Chem Inf Model 2023, 63 (21), 6608-6618.
14. Ji, B.; Wu, Y.; Thomas, E. N.; Edwards, J. N.; He, X.; Wang, J. M. * Predicting Anti-SARS-CoV-2 Activities of Chemical Compounds Using Machine Learning Models. Artificial Intelligence Chemistry, 2023, 1, 100029.
15. Cai, L.; Han, F.; Ji, B.; He, X.; Wang, L.; Niu, T.; Zhai, J. Wang, J. M. * In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling. Molecules, 2023, 28(24), 8034.
2022
1. He, X.; Walker, B.; Man, V. H.; Ren, P.*; Wang, J. M.#*, Recent progress in general force fields of small molecules. Curr Opin Struct Biol 2022, 72, 187-193.
2. Wei, H.; Duan, Y.; Wang, J. M.; Cieplak, P.; Luo, R., Development of polarizable Gaussian multipole model. Biophysical Journal 2022, 121, 157a.
3. Yuan, J. Y.; Jiang, C.; Wang, J. M.; Chen, C. J.; Hao, Y. X.; Zhao, G. Y.; Feng, Z. W.; Xie, X. Q., In Silico Prediction and Validation of CB2 Allosteric Binding Sites to Aid the Design of Allosteric Modulators. Molecules 2022, 27 (2).
4. Zhai, J. C.; He, X. B.; Man, V. H.; Sun, Y. C.; Ji, B. H.; Cai, L. J.; Wang, J. M.#*, A multiple-step in silico screening protocol to identify allosteric inhibitors of Spike-hACE2 binding. Physical Chemistry Chemical Physics 2022, 24 (7), 4305-4316.
5. Strand, A.; Shen, S. T.; Tomchick, D. R.; Wang, J. M.; Wang, C. R.; Deisenhofer, J., Structure and dynamics of major histocompatibility class Ib molecule H2-M3 complexed with mitochondrial-derived peptides. Journal of Biomolecular Structure & Dynamics 2021, In Press. (10.1080/07391102.2021.1942214)
6. Hao, D. X.; He, X. B.; Roitberg, A. E.; Zhang, S. L.; Wang, J. M.#*, Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials. Journal of Chemical Theory and Computation 2022, 18, 978-991.
7. Zhai, J. C.; Ji, B. H.; Liu, S. H.; Zhang, Y. Z.; Cai, L. J.; Wang, J. M.#*, In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data. European Journal of Drug Metabolism and Pharmacokinetics 2022, 47, 403-417.
8. Nguyen, H. L.; Man, V. H.; Li, M. S.; Derreumaux, P.; Wang, J. M.; Nguyen, P. H., Elastic moduli of normal and cancer cell membranes revealed by molecular dynamics simulations. Physical Chemistry Chemical Physics 2022, 24, 6225-6237.
9. Wray R.; Blount P.;* Wang, J. M.;* Iscla, I.* In Silico Screen Identifies a New Family of Agonists for the Bacterial Mechanosensitive Channel MscL. Antibiotics 2022, 11(4), 433; https://doi.org/10.3390/antibiotics11040433.
10. Zhai J.; Ji, B.; Cai, L.; Liu, S.; Sun, Y.; Wang, J. M.#* Physiologically-Based Pharmacokinetics Modeling for Hydroxychloroquine as a Treatment for Malaria and Optimized Dosing Regimens for Different Populations. Journal of Personalized Medicine, 2022, 12(5), 796.
11. Man, V. H.; Lin, D.; He, X. B.; Gao, J.;* Wang, J. M.#*, Joint Computational/Cell-based Oligomerization for Screening Inhibitors of Tau Assembly: A Proof-of-Concept Study. Journal of Alzeheimer’s Disease 2022, 89(1), 107-119.
12. Zhai, J.; He, X.; Sun, Y.; Wan, Z.; Ji, B.; Liu, S.; Li, S.; Wang, J. M.#* In Silico Binding Affinity Prediction for Metabotropic Glutamate Receptors Using Both Endpoint Free Energy Methods and A Machine Learning-Based Scoring Function. Physical Chemistry Chemical Physics, 2022, 24, 18291-18305
13. Wray, R.; Wang, J. M.*; Blount, P.*; Iscla, R.* Activation of a Bacterial Mechanosensitive Channel, MscL, Underlies the Membrane Permeabilization of Dual-Targeting Antibacterial Compounds. Antibiotics, 2022, 11(7), 970.
14. Zhong, X.; Choi, J. H.; Hildebrand, S.; Ludwig, S.; Wang, J.; Nair-Gill, E.; Liao, T. C.; Moresco, J. J.; Liu, A.; Quan, J.; Sun, Q.; Zhang, D.; Zhan, X.; Choi, M.; Li, X.; Wang, J. M.; Gallagher, T.; Moresco, E.; M.; Y.; Beutler, B. RNPS1 inhibits excessive tumor necrosis factor/tumor necrosis factor receptor signaling to support hematopoiesis in mice. Proc. Natl. Acad. Sci. 2022, 119(18), e2200128119
15. Orr, A.; Sharif, S.; Wang, J. M.; MacKerell, A. Preserving the Integrity of Empirical Force Fields. Journal of Chemical Information & Modeling, 2022, 62, 16, 3825–3831.
16. Man, V.;* He, X.; Wang, J. M.* Stable cavitation interferes with Ab16-22 oligomerization. Journal of Chemical Information & Modeling, 2022, 62, 16, 3885–3895.
17. Wu, J.; Wang, J. M.; Wu, Z.; Zhang, S.; Cao, D.; Hsieh, C. Y.; Hou, T. ALipSol: An attention-driven mixture-of-experts model for lipophilicity and solubility prediction. Journal of Chemical Information & Modeling, 2022, 62, 23, 5975–5987.
18. Yang, S.; Tang, Y.; Liu, Y.; Brown, A. J.; Schaks, M.; Ding, B.; Kramer, D. A.; Mietkowska, M.; Ding, L.; Alekhina, O.; Billadeau, D. D.; Chowdhury, S.; Wang, J. M.; Rottner, K.; Chen, B. Arf GTPase activates the WAVE regulatory complex Q1 through a distinct binding site. Science Advances. 2022, 8, Epub.
2021
1. Guo, X. F.; Wiley, C. A.; Steinman, R. A.; Sheng, Y.; Ji, B. H.; Wang, J. M.; Zhang, L. Y.; Wang, T.; Zenatai, M.; Billiar, T. R.; Wang, Q. D., Aicardi-Goutieres syndrome-associated mutation at ADAR1 gene locus activates innate immune response in mouse brain. Journal of Neuroinflammation 2021, 18 (1), 169.
2. Ji, B. H.; He, X. B.; Zhai, J. C.; Zhang, Y. Z.; Man, V. H.; Wang, J. M.#*, Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction. Briefings in Bioinformatics 2021, 22 (5), bbab054.
3. Ji, B. H.; He, X. B.; Zhang, Y. Z.; Zhai, J. C.; Man, V. H.; Liu, S. H.; Wang, J. M.#*, Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities. Journal of Cheminformatics 2021, 13 (1), 11.
4. Ji, B. H.; Xue, Y.; Xu, Y. Y.; Liu, S. H.; Gough, A. H.; Xie, X. Q.*; Wang, J. M.#*, Drug-Drug Interaction Between Oxycodone and Diazepam by a Combined in Silico Pharmacokinetic and Pharmacodynamic Modeling Approach. ACS Chemical Neuroscience 2021, 12 (10), 1777-1790.
5. Kim, P.; Li, H. Y.; Wang, J. M.; Zhao, Z. M., Landscape of drug-resistance mutations in kinase regulatory hotspots. Briefings in Bioinformatics 2021, 22 (3), bbaa108.
6. Man, V. H.; He, X. B.; Gao, J.; Wang, J. M.#*, Effects of All-Atom Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of PHF6 Peptide of Tau Protein. Journal of Chemical Theory and Computation 2021, 17 (10), 6458-6471.
7. Man, V. H.; Li, M. S.; Derreumaux, P.; Wang, J. M.; Nguyen, P. H., Molecular Mechanism of Ultrasound-Induced Structural Defects in Liposomes: A Nonequilibrium Molecular Dynamics Simulation Study. Langmuir 2021, 37 (26), 7945-7954.
8. Man, V. H.; Wang, J. M.; Derreumaux, P.; Nguyen, P. H., Nonequilibrium molecular dynamics simulations of infrared laser-induced dissociation of a tetrameric A beta 42 beta-barrel in a neuronal membrane model. Chemistry and Physics of Lipids 2021, 234, 105030
9. Man, V. H.; Wu, X. W.*; He, X. B.; Xie, X. Q.; Brooks, B. R.*; Wang, J. M.#*, Determination of van der Waals Parameters Using a Double Exponential Potential for Nonbonded Divalent Metal Cations in TIP3P Solvent. Journal of Chemical Theory and Computation 2021, 17 (2), 1086-1097.
10. Su, L. J.; Athamna, M.; Wang, Y.; Wang, J. M.; Freudenberg, M.; Yue, T.; Wang, J. H.; Moresco, E. M. Y.; He, H. M.; Zor, T.; Beutler, B., Sulfatides are endogenous ligands for the TLR4-MD-2 complex. Proceedings of the National Academy of Sciences of the United States of America 2021, 118 (30), e2105316118.
11. Wang, E. C.; Fu, W. T.; Jiang, D. J.; Sun, H. Y.; Wang, J. M.; Zhang, X. J.; Weng, G. Q.; Liu, H.; Tao, P.; Hou, T. J., VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein-Ligand Binding Free Energy Calculations. Journal of Chemical Information and Modeling 2021, 61 (6), 2844-2856.
12. Xue, J.; Han, Y.; Baniasadi, H.; Zeng, W. Z.; Pei, J. M.; Grishin, N. V.; Wang, J. M.; Tu, B. P.; Jiang, Y. X., TMEM120A is a coenzyme A-binding membrane protein with structural similarities to ELOVL fatty acid elongase. eLife 2021, 10, e71220.
13. Zhang, Y. Z.; He, X. B.; Zhai, J. C.; Ji, B. H.; Man, V. H.; Wang, J. M.#*, In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor. Briefings in Bioinformatics 2021, 22 (6), bbab188
2020
1. Bogetti, X.; Ghosh, S.; Jarvi, A. G.; Wang, J. M.*; Saxena, S.*, Molecular Dynamics Simulations Based on Newly Developed Force Field Parameters for Cu2+ Spin Labels Provide Insights into Double-Histidine-Based Double Electron-Electron Resonance. Journal of Physical Chemistry B 2020, 124 (14), 2788-2797.
2. Derreumaux, P.; Man, V. H.; Wang, J. M.; Nguyen, P. H., Tau R3-R4 Domain Dimer of the Wild Type and Phosphorylated Ser356 Sequences. I. In Solution by Atomistic Simulations. Journal of Physical Chemistry B 2020, 124 (15), 2975-2983.
3. Ghosh, S.; Casto, J.; Bogetti, X.; Arora, C.; Wang, J. M.*; Saxena, S.*, Orientation and dynamics of Cu2+ based DNA labels from force field parameterized MD elucidates the relationship between EPR distance constraints and DNA backbone distances. Physical Chemistry Chemical Physics 2020, 22 (46), 26707-26719.
4. Hao, D. X.; He, X. B.; Ji, B. H.; Zhang, S. L.; Wang, J. M.#*, How Well Does the Extended Linear Interaction Energy Method Perform in Accurate Binding Free Energy Calculations? Journal of Chemical Information and Modeling 2020, 60 (12), 6624-6633.
5. He, X. B.; Liu, S. H.; Lee, T. S.; Ji, B. H.; Man, V. H.; York, D. M.; Wang, J. M.#*, Fast, Accurate, and Reliable Protocols for Routine Calculations of Protein-Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFF. ACS Omega 2020, 5 (9), 4611-4619.
6. He, X. B.; Man, V. H.; Yang, W.*; Lee, T. S.*; Wang, J. M.#*, A fast and high-quality charge model for the next generation general AMBER force field. Journal of Chemical Physics 2020, 153 (11), 114502.
7. Hu, Z. H.; Jing, Y. K.; Xue, Y.; Fan, P. H.; Wang, L. R.; Vanyukov, M.; Kirisci, L.; Wang, J. M.*; Tarter, R. E.*; Xie, X. Q.*, Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity. Drug and Alcohol Dependence 2020, 206.
8. Jarvi, A. G.; Sargun, A.; Bogetti, X.; Wang, J. M.*; Achim, C.*; Saxena, S.*, Development of Cu2+-Based Distance Methods and Force Field Parameters for the Determination of PNA Conformations and Dynamics by EPR and MD Simulations. Journal of Physical Chemistry B 2020, 124 (35), 7544-7556.
9. Ji, B. H.; Liu, S. H.; He, X. B.; Man, V. H.; Xie, X. Q.; Wang, J. M.#*, Prediction of the Binding Affinities and Selectivity for CB1 and CB2 Ligands Using Homology Modeling, Molecular Docking, Molecular Dynamics Simulations, and MM-PBSA Binding Free Energy Calculations. ACS Chemical Neuroscience 2020, 11 (8), 1139-1158.
10. Jing, Y. K.; Hu, Z. H.; Fan, P. H.; Xue, Y.; Wang, L. R.; Tarter, R. E.; Kirisci, L.; Wang, J. M.*; Vanyukov, M.*; Xie, X. Q.*, Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder. Drug and Alcohol Dependence 2020, 206.
11. Kawasaki, T.; Man, V. H.; Sugimoto, Y.; Sugiyama, N.; Yamamoto, H.; Tsukiyama, K.; Wang, J. M.; Derreumaux, P.; Nguyen, P. H., Infrared Laser-Induced Amyloid Fibril Dissociation: A Joint Experimental/Theoretical Study on the GNNQQNY Peptide. Journal of Physical Chemistry B 2020, 124 (29), 6266-6277.
12. Man, V. H.; He, X. B.; Ji, B. H.; Liu, S. H.; Xie, X. Q.; Wang, J. M.#*, Introducing Virtual Oligomerization Inhibition to Identify Potent Inhibitors of A beta Oligomerization. Journal of Chemical Theory and Computation 2020, 16 (6), 3920-3935.
13. Man, V. H.; Li, M. S.; Derreumaux, P.; Wang, J. M.; Nguyen, T. T.; Nangia, S.; Nguyen, P. H., Molecular mechanism of ultrasound interaction with a blood brain barrier model. Journal of Chemical Physics 2020, 153 (4), 045104.
14. Wang, E. C.; Liu, H.; Wang, J. M.; Weng, G. Q.; Sun, H. Y.; Wang, Z.; Kang, Y.; Hou, T. J., Development and Evaluation of MM/GBSA Based on a Variable Dielectric GB Model for Predicting Protein-Ligand Binding Affinities. Journal of Chemical Information and Modeling 2020, 60 (11), 5353-5365.
15. Wang, J. M.*, Fast Identification of Possible Drug Treatment of Coronavirus Disease-19 (COVID-19) through Computational Drug Repurposing Study. Journal of Chemical Information and Modeling 2020, 60 (6), 3277-3286.
16. Wei, H. X.; Qi, R. X.; Wang, J. M.; Cieplak, P.; Duan, Y.; Luo, R., Efficient formulation of polarizable Gaussian multipole electrostatics for biomolecular simulations. Journal of Chemical Physics 2020, 153 (11), 114116.
17. Wray, R.; Wang, J. M.*; Iscla, I.*; Blount, P.*, Novel MscL agonists that allow multiple antibiotics cytoplasmic access activate the channel through a common binding site. PLOS One 2020, 15 (1).
18. Xavier, B. M.; Zein, A. A.; Venes, A.; Wang, J. M.; Lee, J. Y., Transmembrane Polar Relay Drives the Allosteric Regulation for ABCG5/G8 Sterol Transporter. International Journal of Molecular Sciences 2020, 21 (22), 8747.
19. Xing, C. R.; Zhuang, Y. W.; Xu, T. H.; Feng, Z. W.; Zhou, X. E.; Chen, M. Z.; Wang, L.; Meng, X.; Xue, Y.; Wang, J. M.; Liu, H.; McGuire, T. F.; Zhao, G. P.; Melcher, K.; Zhang, C.; Xu, H. E.; Xie, X. Q., Cryo-EM Structure of the Human Cannabinoid Receptor CB2-G(i) Signaling Complex. Cell 2020, 180 (4), 645-654.
20. Xue, Y.; Hu, Z. H.; Jing, Y. K.; Wu, H. Y.; Li, X. Y.; Wang, J. M.; Seybert, A.; Xie, X. Q.; Lv, Q. Z., Efficacy assessment of ticagrelor versus clopidogrel in Chinese patients with acute coronary syndrome undergoing percutaneous coronary intervention by data mining and machine-learning decision tree approaches. Journal of Clinical Pharmacy and Therapeutics 2020, 45 (5), 1076-1086.
2019
1. Bian, Y. M.; Wang, J. M.; Jun, J. J.; Xie, X. Q., Deep Convolutional Generative Adversarial Network (dcGAN) Models for Screening and Design of Small Molecules Targeting Cannabinoid Receptors. Molecular Pharmaceutics 2019, 16 (11), 4451-4460.
2. Ge, H. X.*; Bian, Y. M.; He, X. B.; Xie, X. Q.*; Wang, J. M.#*, Significantly different effects of tetrahydroberberrubine enantiomers on dopamine D1/D2 receptors revealed by experimental study and integrated in silico simulation. Journal of Computer-Aided Molecular Design 2019, 33 (4), 447-459.
3. He, X. B.; Man, V. H.; Ji, B. H.; Xie, X. Q.; Wang, J. M.#*, Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3. Journal of Computer-Aided Molecular Design 2019, 33 (1), 105-117.
4. Ji, B. H.; Liu, S. H.; Xue, Y.; He, X. B.; Man, V. H.; Xie, X. Q.*; Wang, J. M.#*, Prediction of Drug-Drug Interactions Between Opioids and Overdosed Benzodiazepines Using Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation. Drugs in R&D 2019, 19 (3), 297-305.
5. Liu, S. H.; He, X. B.; Man, V. H.; Ji, B. H.; Liu, J. J.; Wang, J. M.#*, New application of in silico methods in identifying mechanisms of action and key components of anti-cancer herbal formulation YIV-906 (PHY906). Physical Chemistry Chemical Physics 2019, 21 (42), 23501-23513.
6. Man, V. H.; He, X. B.; Derreumaux, P.; Ji, B. H.; Xie, X. Q.; Nguyen, P. H.; Wang, J. M.#*, Effects of All-Atom Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of A beta(16-22) Dimer. Journal of Chemical Theory and Computation 2019, 15 (2), 1440-1452.
7. Man, V. H.; He, X. B.; Ji, B. H.; Liu, S. H.; Xie, X. Q.; Wang, J. M.#*, Molecular Mechanism and Kinetics of Amyloid-beta(42) Aggregate Formation: A Simulation Study. ACS Chemical Neuroscience 2019, 10 (11), 4643-4658.
8. Man, V. H.; Li, M. S.; Wang, J. M.; Derreumaux, P.; Nguyen, P. H., Interaction mechanism between the focused ultrasound and lipid membrane at the molecular level. Journal of Chemical Physics 2019, 150 (21), 215101.
9. Man, V. H.; Li, M. S.; Wang, J. M.; Derreumaux, P.; Nguyen, P. H., Nonequilibrium atomistic molecular dynamics simulation of tubular nanomotor propelled by bubble propulsion. Journal of Chemical Physics 2019, 151 (2), 024103.
10. Man, V. H.; Truong, P. M.; Li, M. S.; Wang, J. M.; Van-Oanh, N. T.; Derreumaux, P.; Nguyen, P. H., Molecular Mechanism of the Cell Membrane Pore Formation Induced by Bubble Stable Cavitation. Journal of Physical Chemistry B 2019, 123 (1), 71-78.
11. Su, L. J.; Wang, Y.; Wang, J. M.; Mifune, Y.; Morin, M. D.; Jones, B. T.; Moresco, E. M. Y.; Boger, D. L.; Beutler, B.; Zhang, H., Structural Basis of TLR2/TLR1 Activation by the Synthetic Agonist Diprovocim. Journal of Medicinal Chemistry 2019, 62 (6), 2938-2949.
12. Taylor, C. A.; Cormier, K. W.; Keenan, S. E.; Earnest, S.; Stippec, S.; Wichaidit, C.; Juang, Y. C.; Wang, J. M.; Shvartsman, S. Y.; Goldsmith, E. J.; Cobb, M. H., Functional divergence caused by mutations in an energetic hotspot in ERK2. Proceedings of the National Academy of Sciences of the United States of America 2019, 116 (31), 15514-15523.
13. Wang, E. C.; Sun, H. Y.; Wang, J. M.; Wang, Z.; Liu, H.; Zhang, J. Z. H.; Hou, T. J., End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chemical Reviews 2019, 119 (16), 9478-9508.
14. Wang, J. M.*; Cieplak, P.; Luo, R.; Duan, Y.*, Development of Polarizable Gaussian Model for Molecular Mechanical Calculations I: Atomic Polarizability Parameterization To Reproduce ab Initio Anisotropy. Journal of Chemical Theory and Computation 2019, 15 (2), 1146-1158.
15. Wang, J. M.*; Ge, Y. B.; Xie, X. Q., Development and Testing of Druglike Screening Libraries. Journal of Chemical Information and Modeling 2019, 59 (1), 53-65.
16. Wray, R.; Herrera, N.; Iscla, I.*; Wang, J. M.*; Blount, P.*, An agonist of the MscL channel affects multiple bacterial species and increases membrane permeability and potency of common antibiotics. Molecular Microbiology 2019, 112 (3), 896-905.
17. Wray, R.; Iscla, I.; Kovacs, Z.; Wang, J. M.*; Blount, P.*, Novel compounds that specifically bind and modulate MscL: insights into channel gating mechanisms. FASEB Journal 2019, 33 (3), 3180-3189.
18. Wu, N.; Feng, Z. W.; He, X. B.; Kwon, W.; Wang, J. M.*; Xie, X. Q.*, Insight of Captagon Abuse by Chemogenomics Knowledgebase-guided Systems Pharmacology Target Mapping Analyses. Scientific Reports 2019, 9, 2268.
19. Xavier, B. M.; Jennings, W. J.; Zein, A. A.; Wang, J. M.; Lee, J. Y., Structural snapshot of the cholesterol-transport ATP-binding cassette proteins. Biochemistry and Cell Biology 2019, 97 (3), 224-233.
2018
1. Chen, F.; Sun, H. Y.; Wang, J. M.; Zhu, F.; Liu, H.; Wang, Z.; Lei, T. L.; Li, Y. Y.; Hou, T. J., Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes. RNA 2018, 24 (9), 1183-1194.
2. Domin, D.; Man, V. H.; Van-Oanh, N. T.; Wang, J. M.; Kawasaki, T.; Derreumaux, P.; Nguyen, P. H., Breaking down cellulose fibrils with a mid-infrared laser. Cellulose 2018, 25 (10), 5553-5568.
3. Liu, N.; Zhou, W. F.; Guo, Y.; Wang, J. M.; Fu, W. T.; Sun, H. Y.; Liu, D.; Duan, M. J.; Hou, T. J., Molecular Dynamics Simulations Revealed the Regulation of Ligands to the Interactions between Androgen Receptor and Its Coactivator. Journal of Chemical Information and Modeling 2018, 58 (8), 1652-1661.
4. Shang, J.; Hu, B.; Wang, J. M.; Zhu, F.; Kang, Y.; Li, D.; Sun, H. Y.; Kong, D. X.; Hou, T., Cheminformatic Insight into the Differences between Terrestrial and Marine Originated Natural Products. Journal of Chemical Information and Modeling 2018, 58 (6), 1182-1193.
5. Suno, R.; Kimura, K. T.; Nakane, T.; Yamashita, K.; Wang, J. M.; Fujiwara, T.; Yamanaka, Y.; Im, D.; Horita, S.; Tsujimoto, H.; Tawaramoto, M. S.; Hirokawa, T.; Nango, E.; Tono, K.; Kameshima, T.; Hatsui, T.; Joti, Y.; Yabashi, M.; Shimamoto, K.; Yamamoto, M.; Rosenbaum, D. M.; Iwata, S.; Shimamura, T.; Kobayashi, T., Crystal Structures of Human Orexin 2 Receptor Bound to the Subtype-Selective Antagonist EMPA. Structure 2018, 26 (1), 7-19.
6. Wang, Y. Q.; Lin, W. W.; Wu, N.; He, X. B.; Wang, J. M.; Feng, Z. W.; Xie, X. Q., An insight into paracetamol and its metabolites using molecular docking and molecular dynamics simulation. Journal of Molecular Modeling 2018, 24 (9).
7. Yin, J.; Chapman, K.; Clark, L. D.; Shao, Z. H.; Borek, D.; Xu, Q. P.; Wang, J. M.; Rosenbaum, D. M., Crystal structure of the human NK1 tachykinin receptor. Proceedings of the National Academy of Sciences of the United States of America 2018, 115 (52), 13264-13269
2017
1. Guinney, J.; Wang, T.; Laajala, T. D.; Winner, K. K.; Bare, J. C.; Neto, E. C.; Khan, S. A.; Peddinti, G.; Airola, A.; Pahikkala, T.; Mirtti, T.; Yu, T.; Bot, B. M.; Shen, L.; Abdallah, K.; Norman, T.; Friend, S.; Stolovitzky, G.; Soule, H.; Sweeney, C. J.; Ryan, C. J.; Scher, H. I.; Sartor, O.; Xie, Y.; Aittokallio, T.; Zhou, F. L.; Costello, J. C.; Abdallah, K.; Aittokallio, T.; Airola, A.; Anghel, C.; Azima, H.; Baertsch, R.; Ballester, P. J.; Bare, C.; Bhandari, V.; Bot, B. M.; Dang, C. C.; Dunba, M. B. N.; Buchardt, A. S.; Buturovic, L.; Cao, D.; Chalise, P.; Cho, J.; Chu, T. M.; Coley, R. Y.; Conjeti, S.; Correia, S.; Costello, J. C.; Dai, Z. W.; Dai, J. Q.; Dargatz, P.; Delavarkhan, S.; Deng, D. T.; Dhanik, A.; Du, Y.; Elangovan, A.; Ellis, S.; Elo, L. L.; Espiritu, S. M.; Fan, F.; Farshi, A. B.; Freitas, A.; Fridley, B.; Friend, S.; Fuchs, C.; Gofer, E.; Peddinti, G.; Graw, S.; Greiner, R.; Guan, Y. F.; Guinney, J.; Guo, J.; Gupta, P.; Guyer, A. I.; Han, J. W.; Hansen, N. R.; Chang, B. H. W.; Hirvonen, O.; Huang, B.; Huang, C.; Hwang, J.; Ibrahim, J. G.; Jayaswal, V.; Jeon, J.; Ji, Z. C.; Juvvadi, D.; Jyrkkio, S.; Kanigel-Winner, K.; Katouzian, A.; Kazanov, M. D.; Khan, S. A.; Khayyer, S.; Dalho; Golinska, A. K.; Koestler, D.; Kokowicz, F.; Kondofersky, I.; Krautenbacher, N.; Krstajic, D.; Kumar, L.; Kurz, C.; Kyan, M.; Laajala, T. D.; Laimighofer, M.; Lee, E.; Lesinski, W.; Li, M. Z.; Li, Y.; Lian, Q. Y.; Liang, X. T.; Lim, M.; Lin, H.; Lin, X. H.; Lu, J.; Mahmoudian, M.; Manshaei, R.; Meier, R.; Miljkovic, D.; Mirtti, T.; Mnich, K.; Navab, N.; Neto, E. C.; Newton, Y.; Norman, T.; Pahikkala, T.; Pal, S.; Park, B.; Patel, J.; Pathak, S.; Pattin, A.; Ankerst, D. P.; Peng, J.; Petersen, A. H.; Philip, R.; Piccolo, S. R.; Polsterl, S.; Polewko-Klim, A.; Rao, K.; Ren, X.; Rocha, M.; Rudnicki, W. R.; Ryan, C. J.; Ryu, H.; Sartor, O.; Scherb, H.; Sehgal, R.; Seyednasrollah, F.; Shang, J. B.; Shao, B.; Shen, L. J.; Sher, H.; Shiga, M.; Sokolov, A.; Sollner, J. F.; Song, L.; Soule, H.; Stolovitzky, G.; Stuart, J.; Sun, R.; Sweeney, C. J.; Tahmasebi, N.; Tan, K. T.; Tomaziu, L.; Usset, J.; Vang, Y. S.; Vega, R.; Vieira, V.; Wang, D.; Wang, D. F.; Wang, J. M.; Wang, L. C.; Wang, S.; Wang, T.; Wang, Y.; Wolfinger, R.; Wong, C.; Wu, Z. K.; Xiao, J. F.; Xie, X. H.; Xie, Y.; Xin, D.; Yang, H. J.; Yu, N.; Yu, T.; Yu, X.; Zahedi, S.; Zanin, M.; Zhang, C. H.; Zhang, J. W.; Zhang, S. H.; Zhang, Y. C.; Zhou, F. L.; Zhu, H. T.; Zhu, S. F.; Zhu, Y. X.; Prostate Canc Challenge, D. C., Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncology 2017, 18 (1), 132-142.
2. Seyednasrollah, F.; Koestler, D. C.; Wang, T.; Piccolo, S. R.; Vega, R.; Greiner, R.; Fuchs, C.; Gofer, E.; Kumar, L.; Wolfinger, R. D.; Winner, K. K.; Bare, C.; Neto, E. C.; Yu, T.; Shen, L. J.; Abdallah, K.; Norman, T.; Stolovitzky, G.; Soule, H. R.; Sweeney, C. J.; Ryan, C. J.; Scher, H. I.; Sartor, O.; Elo, L. L.; Zhou, F. L.; Guinney, J.; Costello, J. C.; Abdallah, K.; Airola, A.; Aittokallio, T.; Anghel, C.; Ankerst, D. P.; Azima, H.; Baertsch, R.; Ballester, P. J.; Bare, C.; Bhandari, V.; Bot, B. M.; Buchardt, A. S.; Buturovic, L.; Cao, D.; Chalise, P.; Chang, B. H. W.; Cho, J.; Chu, T. M.; Coley, R. Y.; Conjeti, S.; Correia, S.; Costello, J. C.; Dai, Z. W.; Dai, J. Q.; Dang, C. C.; Dargatz, P.; Delavarkhan, S.; Deng, D. T.; Dhanik, A.; Du, Y.; Elangovan, A.; Ellis, S.; Elo, L. L.; Espiritu, S. M.; Fan, F.; Farshi, A. B.; Freitas, A.; Fridley, B.; Fuchs, C.; Gofer, E.; Golinska, A. K.; Graw, S.; Greiner, R.; Guinney, J.; Guo, J.; Gupta, P.; Guyer, A. I.; Han, J. W.; Hansen, N. R.; Hirvonen, O.; Huang, B.; Huang, C.; Hwang, J.; Ibrahim, J. G.; Jayaswal, V.; Jeon, J.; Ji, Z. C.; Juvvadi, D.; Jyrkkio, S.; Kanigel-Winner, K.; Katouzian, A.; Kazanov, M. D.; Khan, S. A.; Khayyer, S.; Kim, D.; Koestler, D.; Kokowicz, F.; Kondofersky, I.; Krstajic, D.; Kumar, L.; Kurz, C.; Kyan, M.; Laajala, T. D.; Laimighofer, M.; Lee, E.; Lesinski, W.; Li, M. Z.; Li, Y.; Lian, Q. Y.; Liang, X. T.; Lim, M.; Lin, H.; Lin, X. H.; Lin, X.; Lu, J.; Mahmoudian, M.; Manshaei, R.; Meier, R.; Miljkovic, D.; Mirtti, T.; Mnich, K.; Navab, N.; Neto, E. C.; Newton, Y.; Norman, T.; Pahikkala, T.; Pal, S.; Park, B.; Patel, J.; Pathak, S.; Pattin, A.; Peddinti, G.; Peng, J.; Petersen, A. H.; Philip, R.; Piccolo, S. R.; Polsterl, S.; Polewko-Klim, A.; Rao, K.; Ren, X.; Rocha, M.; Rudnicki, W. R.; Ryan, C. J.; Ryu, H.; Sartor, O.; Scherb, H.; Sehgal, R.; Seyednasrollah, F.; Shang, J. B.; Shao, B.; Shen, L. J.; Sher, H.; Shiga, M.; Sokolov, A.; Sollner, J. F.; Song, L.; Soule, H.; Stolovitzky, G.; Stuart, J.; Sun, R.; Sweeney, C. J.; Tahmasebi, N.; Tan, K. T.; Tomaziu, L.; Usset, J.; Vang, Y. S.; Vega, R.; vieira, V.; Wang, D.; Wang, D. F.; Wang, J. M.; Wang, L. C.; Wang, S.; Wang, T.; Wang, Y.; Wolfinger, R.; Wong, C.; Wu, Z. K.; Xiao, J. F.; Xie, X. H.; Xin, D.; Yang, H.; Yu, N.; Yu, T.; Yu, X.; Zahedi, S.; Zanin, M.; Zhang, C. H.; Zhang, J. W.; Zhang, S. H.; Zhang, Y. C.; Zhou, F. L.; Zhu, H. T.; Zhu, S. F.; Zhu, Y. X.; Prostate Canc, D. C., A DREAM Challenge to guild Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration- Resistant Prostate Cancer. JCO Clinical Cancer Informatics 2017, 1.
3. Xiao, L.; Diao, J. M.; Greene, D.; Wang, J. M.; Luo, R., A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins. Journal of Chemical Theory and Computation 2017, 13 (7), 3398-3412.
Publications Before 2017
1. Wang, J. M.; Hu, Z. L.; Ye, X. Q., Conformational Analysis of Leu-enkephalin by Molecular Dynamics Method. Acta Physico-Chimica Sinica 1995, 11 (8), 673-677.
2. Wang, J. M.; Zhao, Z. L.; Ye, X. Q., Parameterization Procedures in Molecular Mechanics Calculation. Acta Physico-Chimica Sinica 1995, 11 (5), 424-428.
3. Wang, J. M.; Hou, T. J.; Li, Y. Y.; Xu, X. J., The QSAR research of pyrrolobenzothiazepinones and pyrrolobenzo-xazepinones - Novel and specific non-nucleoside HIV-1 reverse transcriptase inhibitors. Chinese Chemical Letters 1997, 8 (10), 889-892.
4. Hou, T. J.; Wang, J. M.; Li, Y. Y.; Xu, X. J., Application of genetic algorithm to the QSAR research of pyrrolobenzothiazepinones and pyrrolobenzoxazepinones-novel and specific non-nucleoside HIV-1 reverse transcription inhibitors. Chinese Chemical Letters 1998, 9 (7), 651-654.
5. Wang, J. M.; Zhang, H.; He, H. X.; Hou, T. J.; Liu, Z. F.; Xu, X. J., Theoretical studies on force titration of amino-group-terminated self-assembled monolayers. Journal of Molecular Structure-Theochem 1998, 451 (3), 295-303.
6. Hou, T. J.; Wang, J. M.; Chen, L. R.; Xu, X. J., Automated docking of peptides and proteins by using a genetic algorithm combined with a tabu search. Protein Engineering 1999, 12 (8), 639-647.
7. Hou, T. J.; Wang, J. M.; Liao, N.; Xu, X. J., Applications of genetic algorithms on the structure-activity relationship analysis of some cinnamamides. Journal of Chemical Information and Computer Sciences 1999, 39 (5), 775-781.
8. Hou, T. J.; Wang, J. M.; Xu, X. J., Applications of genetic algorithms on the structure-activity correlation study of a group of non-nucleoside HIV-1 inhibitors. Chemometrics and Intelligent Laboratory Systems 1999, 45 (1-2), 303-310.
9. Hou, T. J.; Wang, J. M.; Xu, X. J., A comparison of three heuristic algorithms for molecular docking. Chinese Chemical Letters 1999, 10 (7), 615-618.
10. Wang, J. M.; Hou, T. J.; Chen, L. R.; Xu, X. J., Automated docking of peptides and proteins by genetic algorithm. Chemometrics and Intelligent Laboratory Systems 1999, 45 (1-2), 281-286.
11. Wang, J. M.; Hou, T. J.; Chen, L. R.; Xu, X. J., Conformational analysis of peptides using Monte Carlo simulations combined with the genetic algorithm. Chemometrics and Intelligent Laboratory Systems 1999, 45 (1-2), 347-351.
12. Wang, J. M.; Cieplak, P.; Kollman, P. A., How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? Journal of Computational Chemistry 2000, 21 (12), 1049-1074.
13. Wang, J. M.; Kollman, P. A., Automatic parameterization of force field by systematic search and genetic algorithms. Journal of Computational Chemistry 2001, 22 (12), 1219-1228.
14. Wang, J. M.; Morin, P.; Wang, W.; Kollman, P. A., Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. Journal of the American Chemical Society 2001, 123 (22), 5221-5230.
15. Wang, J. M.; Wang, W.; Huo, S. H.; Lee, M.; Kollman, P. A., Solvation model based on weighted solvent accessible surface area. Journal of Physical Chemistry B 2001, 105 (21), 5055-5067.
16. Wang, W.; Lim, W. A.; Jakalian, A.; Wang, J.; Wang, J. M.; Luo, R.; Bayly, C. T.; Kollman, P. A., An analysis of the interactions between the Sem-5 SH3 domain and its ligands using molecular dynamics, free energy calculations, and sequence analysis. Journal of the American Chemical Society 2001, 123 (17), 3986-3994.
17. Huo, S. H.; Wang, J. M.; Cieplak, P.; Kollman, P. A.; Kuntz, I. D., Molecular dynamics and free energy analyses of cathepsin D-inhibitor interactions: Insight into structure-based ligand design. Journal of Medicinal Chemistry 2002, 45 (7), 1412-1419.
18. Duan, Y.; Wu, C.; Chowdhury, S.; Lee, M. C.; Xiong, G. M.; Zhang, W.; Yang, R.; Cieplak, P.; Luo, R.; Lee, T.; Caldwell, J.; Wang, J. M.; Kollman, P., A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. Journal of Computational Chemistry 2003, 24 (16), 1999-2012.
19. Wang, J. M.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A., Development and testing of a general amber force field. Journal of Computational Chemistry 2004, 25 (9), 1157-1174.
20. Wu, C. D.; Decker, E. R.; Blok, N.; Bui, H.; You, T. J.; Wang, J. M.; Bourgoyne, A. R.; Knowles, V.; Berens, K. L.; Holland, G. W.; Brock, T. A.; Dixon, R. A. F., Discovery, modeling, and human pharmacokinetics of N-(2-acetyl-4,6-dimethylphenyl)-3-(3,4-dimethylisoxazol-5-ylsulfamoyl)th iophene-2-carboxamide (TBC3711), a second generation, ETA selective, and orally bioavailable endothelin antagonist. Journal of Medicinal Chemistry 2004, 47 (8), 1969-1986.
21. Shan, J. F.; Shi, D. L.; Wang, J. M.; Zheng, J., Identification of a specific inhibitor of the dishevelled PDZ domain. Biochemistry 2005, 44 (47), 15495-15503.
22. Shan, J. F.; Wang, J. M.; Zheng, J., Identification of non-peptide inhibitor of the dishevelled PDZ domain. Biophysical Journal 2005, 88 (1), 334A-334A.
23. Wang, J. M.*; Kang, X. S.; Kuntz, I. D.; Kollman, P. A., Hierarchical database screenings for HIV-1 reverse transcriptase using a pharmacophore model, rigid docking, solvation docking, and MM-PB/SA. Journal of Medicinal Chemistry 2005, 48 (7), 2432-2444.
24. Zhang, J. M.; Wang, J. M.; Brodbelt, J. S., Characterization of flavonoids by aluminum complexation and collisionally activated dissociation. Journal of Mass Spectrometry 2005, 40 (3), 350-363.
25. Hou, T. J.; Wang, J. M.; Zhang, W.; Wang, W.; Xu, X., Recent advances in computational prediction of drug absorption and permeability in drug discovery. Current Medicinal Chemistry 2006, 13 (22), 2653-2667.
26. Wang, J. M.*; Hou, T. J.; Xu, X. J.*, Recent Advances in Free Energy Calculations with a Combination of Molecular Mechanics and Continuum Models. Current Computer-Aided Drug Design 2006, 2 (3), 287-306.
27. Wang, J. M.*; Krudy, G.; Xie, X. Q.; Wu, C. D.; Holland, G., Genetic algorithm-optimized QSPR models for bioavailability, protein binding, and urinary excretion. Journal of Chemical Information and Modeling 2006, 46 (6), 2674-2683.
28. Wang, J. M.*; Wang, W.; Kollman, P. A.; Case, D. A.*, Automatic atom type and bond type perception in molecular mechanical calculations. Journal of Molecular Graphics & Modelling 2006, 25 (2), 247-260.
29. Yang, L. J.; Tan, C. H.; Hsieh, M. J.; Wang, J. M.; Duan, Y.; Cieplak, P.; Caldwell, J.; Kollman, P. A.; Luo, R., New-generation amber united-atom force field. Journal of Physical Chemistry B 2006, 110 (26), 13166-13176.
30. Chen, J. Z.; Wang, J. M.; Xie, X. Q., GPCR structure-based virtual screening approach for CB2 antagonist search. Journal of Chemical Information and Modeling 2007, 47 (4), 1626-1637.
31. Hou, T. J.; Wang, J. M.; Li, Y. Y., ADME evaluation in drug discovery. 8. The prediction of human intestinal absorption by a support vector machine. Journal of Chemical Information and Modeling 2007, 47 (6), 2408-2415.
32. Hou, T. J.; Wang, J. M.; Zhang, W.; Xu, X. J., ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification. Journal of Chemical Information and Modeling 2007, 47 (1), 208-218.
33. Hou, T. J.; Wang, J. M.; Zhang, W.; Xu, X. J., ADME evaluation in drug discovery. 6. Can oral bioavailability in humans be effectively predicted by simple molecular property-based rules? Journal of Chemical Information and Modeling 2007, 47 (2), 460-463.
34. Mazzitelli, C. L.; Wang, J. M.; Smith, S. I.; Brodbelt, J. S., Gas-phase stability of G-quadruplex DNA determined by electrospray ionization tandem mass spectrometry and molecular dynamics Simulations. Journal of the American Society for Mass Spectrometry 2007, 18 (10), 1760-1773.
35. Wang, J. M.*; Krudy, G.; Hou, T. J.; Zhang, W.; Holland, G.; Xu, X. J.*, Development of reliable aqueous solubility models and their application in druglike analysis. Journal of Chemical Information and Modeling 2007, 47 (4), 1395-1404.
36. Wang, J. M.*; Xie, X. Q.; Hou, T. J.; Xu, X. J., Fast approaches for molecular polarizability calculations. Journal of Physical Chemistry A 2007, 111 (20), 4443-4448.
37. Hou, T.; Wang, J. M., Structure - ADME relationship: still a long way to go? Expert Opinion on Drug Metabolism & Toxicology 2008, 4 (6), 759-770.
38. Cieplak, P.; Dupradeau, F. Y.; Duan, Y.; Wang, J. M., Polarization effects in molecular mechanical force fields. Journal of Physics-Condensed Matter 2009, 21 (33).
39. Hou, T. J.; Li, Y. Y.; Zhang, W.; Wang, J. M., Recent Developments of In Silico Predictions of Intestinal Absorption and Oral Bioavailability. Combinatorial Chemistry & High Throughput Screening 2009, 12 (5), 497-506.
40. Pierce, S. E.; Wang, J. M.; Jayawickramarajah, J.; Hamilton, A. D.; Brodbelt, J. S., Examination of the Effect of the Annealing Cation on Higher Order Structures Containing Guanine or Isoguanine Repeats. Chemistry-a European Journal 2009, 15 (42), 11244-11255.
41. Wang, J. M.*; Hou, T. J.; Xu, X. J.*, Aqueous Solubility Prediction Based on Weighted Atom Type Counts and Solvent Accessible Surface Areas. Journal of Chemical Information and Modeling 2009, 49 (3), 571-581.
42. Wang, J. M.*; Hou, T. J., Drug and Drug Candidate Building Block Analysis. Journal of Chemical Information and Modeling 2010, 50 (1), 55-67.
43. Hou, T. J.; Wang, J. M.; Li, Y. Y.; Wang, W., Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations. Journal of Chemical Information and Modeling 2011, 51 (1), 69-82.
44. Hou, T. J.; Wang, J. M.; Li, Y. Y.; Wang, W., Assessing the Performance of the Molecular Mechanics/Poisson Boltzmann Surface Area and Molecular Mechanics/Generalized Born Surface Area Methods. II. The Accuracy of Ranking Poses Generated From Docking. Journal of Computational Chemistry 2011, 32 (5), 866-877.
45. Tian, S.; Li, Y. Y.; Wang, J. M.; Zhang, J.; Hou, T. J., ADME Evaluation in Drug Discovery. 9. Prediction of Oral Bioavailability in Humans Based on Molecular Properties and Structural Fingerprints. Molecular Pharmaceutics 2011, 8 (3), 841-851.
46. Wang, J. M.; Cieplak, P.; Li, J.; Hou, T. J.; Luo, R.; Duan, Y., Development of Polarizable Models for Molecular Mechanical Calculations I: Parameterization of Atomic Polarizability. Journal of Physical Chemistry B 2011, 115 (12), 3091-3099.
47. Wang, J. M.; Cieplak, P.; Li, J.; Wang, J.; Cai, Q.; Hsieh, M. J.; Lei, H. X.; Luo, R.; Duan, Y., Development of Polarizable Models for Molecular Mechanical Calculations II: Induced Dipole Models Significantly Improve Accuracy of Intermolecular Interaction Energies. Journal of Physical Chemistry B 2011, 115 (12), 3100-3111.
48. Wang, J. M.*; Hou, T. J.*, Application of Molecular Dynamics Simulations in Molecular Property Prediction II: Diffusion Coefficient. Journal of Computational Chemistry 2011, 32 (16), 3505-3519.
49. Wang, J. M.*; Hou, T. J., Recent Advances on Aqueous Solubility Prediction. Combinatorial Chemistry & High Throughput Screening 2011, 14 (5), 328-338.
50. Wang, J. M.*; Hou, T. J.*, Application of Molecular Dynamics Simulations in Molecular Property Prediction. 1. Density and Heat of Vaporization. Journal of Chemical Theory and Computation 2011, 7 (7), 2151-2165.
51. Zhu, J. Y.; Wang, J. M.; Yu, H. D.; Li, Y. Y.; Hou, T. J., Recent Developments of In Silico Predictions of Oral Bioavailability. Combinatorial Chemistry & High Throughput Screening 2011, 14 (5), 362-374.
52. Cao, D. Y.; Wang, J. M.; Zhou, R.; Li, Y. Y.; Yu, H. D.; Hou, T. J., ADMET Evaluation in Drug Discovery. 11. PharmacoKinetics Knowledge Base (PKKB): A Comprehensive Database of Pharmacokinetic and Toxic Properties for Drugs. Journal of Chemical Information and Modeling 2012, 52 (5), 1132-1137.
53. Shen, M. Y.; Tian, S.; Li, Y. Y.; Li, Q.; Xu, X. J.; Wang, J. M.; Hou, T. J., Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines. Journal of Cheminformatics 2012, 4.
54. Tian, S.; Wang, J. M.; Li, Y. Y.; Xu, X. J.; Hou, T. J., Drug-likeness Analysis of Traditional Chinese Medicines: Prediction of Drug-likeness Using Machine Learning Approaches. Molecular Pharmaceutics 2012, 9 (10), 2875-2886.
55. Wang, J.; Cieplak, P.; Cai, Q.; Hsieh, M. J.; Wang, J. M.; Duan, Y.; Luo, R., Development of Polarizable Models for Molecular Mechanical Calculations. 3. Polarizable Water Models Conforming to Thole Polarization Screening Schemes. Journal of Physical Chemistry B 2012, 116 (28), 7999-8008.
56. Wang, J. M.; Cieplak, P.; Li, J.; Cai, Q.; Hsieh, M. J.; Luo, R.; Duan, Y., Development of Polarizable Models for Molecular Mechanical Calculations. 4. van der Waals Parametrization. Journal of Physical Chemistry B 2012, 116 (24), 7088-7101.
57. Wang, J. M.*; Hou, T. J., Develop and Test a Solvent Accessible Surface Area-Based Model in Conformational Entropy Calculations. Journal of Chemical Information and Modeling 2012, 52 (5), 1199-1212.
58. Wang, S. C.; Li, Y. Y.; Wang, J. M.; Chen, L.; Zhang, L. L.; Yu, H. D.; Hou, T. J., ADMET Evaluation in Drug Discovery. 12. Development of Binary Classification Models for Prediction of hERG Potassium Channel Blockage. Molecular Pharmaceutics 2012, 9 (4), 996-1010.
59. Zhang, Q.; Zhang, W.; Li, Y. Y.; Wang, J. M.; Zhang, L. L.; Hou, T. J., A rule-based algorithm for automatic bond type perception. Journal of Cheminformatics 2012, 4.
60. Tian, S.; Li, Y. Y.; Li, D.; Xu, X. J.; Wang, J. M.; Zhang, Q.; Hou, T. J., Modeling Compound-Target Interaction Network of Traditional Chinese Medicines for Type II Diabetes Mellitus: Insight for Polypharmacology and Drug Design. Journal of Chemical Information and Modeling 2013, 53 (7), 1787-1803.
61. Tian, S.; Li, Y. Y.; Wang, J. M.; Xu, X. J.; Xu, L.; Wang, X. H.; Chen, L.; Hou, T. J., Drug-likeness analysis of traditional Chinese medicines: 2. Characterization of scaffold architectures for drug-like compounds, non-drug-like compounds, and natural compounds from traditional Chinese medicines. Journal of Cheminformatics 2013, 5.
62. Xu, L.; Sun, H. Y.; Li, Y. Y.; Wang, J. M.; Hou, T. J., Assessing the Performance of MM/PBSA and MM/GBSA Methods. 3. The Impact of Force Fields and Ligand Charge Models. Journal of Physical Chemistry B 2013, 117 (28), 8408-8421.
63. Zhang, Q.; Wang, J. M.; Guerrero, G. D.; Cecilia, J. M.; Garcia, J. M.; Li, Y. Y.; Perez-Sanchez, H.; Hou, T. J., Accelerated Conformational Entropy Calculations Using Graphic Processing Units. Journal of Chemical Information and Modeling 2013, 53 (8), 2057-2064.
64. Sun, H. Y.; Li, Y. Y.; Tian, S.; Wang, J. M.; Hou, T. J., P-loop Conformation Governed Crizotinib Resistance in G2032R-Mutated ROS1 Tyrosine Kinase: Clues from Free Energy Landscape. PLOS Computational Biology 2014, 10 (7).
65. Zhang, Q.; Zhang, W.; Li, Y. Y.; Wang, J. M.; Zhang, J.; Hou, T. J., MORT: a powerful foundational library for computational biology and CADD. Journal of Cheminformatics 2014, 6.
66. Tian, S.; Wang, J. M.; Li, Y. Y.; Li, D.; Xu, L.; Hou, T. J., The application of in silico drug-likeness predictions in pharmaceutical research. Advanced Drug Delivery Reviews 2015, 86, 2-10.
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Key #: Finished by students and fellows under my direction;
*: Corresponding author
ACTIVE
National Science Foundation/1955260, Wang, Junmei (PI) 07/01/20-06/30/23
Title: CDS&E: D3SC: Developing a molecular mechanics modeling platform (MMMP) for studying molecular interactions
Total Cost: $500,000 Effort: 8.3%
Goals: This proposal seeks to (1) develop and advance a set of computational tools to facilitate users from a broad range of disciplines to generate high-quality molecular mechanics force field (MMFF) parameters; and (2) develop a set of MMFF-based models and software tools to facilitate the study of molecular interactions with a focus on free energy calculation. The developed software and parameters will be released via the website https://clickff.org, and distributed through AMBER, a mainstream molecular simulation software package.
Role: PI
NIH/ R01GM147673 Wang, Junmei (PI) 09/24/2022-8/31/2026 Title: New generation of general AMBER force field for biomedical research
Total Cost: $1,252,000 Effort: 25%
Goals: We plan to (1) develop a new generation of general AMBER force field (GAFF3) for studying biomolecule-ligand interactions; (2) critically evaluate GAFF3 in protein-ligand and nucleic acid-ligand binding free energy predictions using a novel GPU-accelerated λ-dynamics based orthogonal space tempering (OST) algorithm; and (3) apply a variety of strategies to further improve the performance of GAFF3 until it approaches the best performance an additive force field model can achieve.
Role: PI
NIH/R01 GM149705-01 Wang, Junmei (PI) 04/01/2023-3/31/2028 Title: AI-powered Biased Ligand Design
Total Cost: $1,252,000 Effort: 20%
Goals: Biased ligand design is an attractive approach for designing drugs that target a particular signaling pathway with high specificity and selectivity to minimize side effects, however, it is also a grand challenge due to lack of computational tools. Also, there is an urgent need to expand the druglike chemical space for promising drug targets which have plenty of potent ligands developed, but unfortunately, no approved drugs. We plan to apply artificial intelligence (AI) techniques to overcome the two challenges by developing interaction profile scoring function models to enable biased ligand design and Drug-GAN models to achieve de novo novel chemical structure design.
Role: PI
NIH/R01AG057555 Xie, Lei (PI) 06/01/2023-05/31/2028
Title: AI-Powered Quantitative Systems Pharmacology for AD Drug Repurposing
Total Award Amount: $464,315 (to PITT) Effort: 5%
Major Goals: The major goal of this proposal is to develop and experimentally validate innovative machine-learning methods for predicting genome-wide protein-ligand interactions and ligand-induced functional activities for drug discovery targeting understudied proteins.
Role: PBPK expert
Eli Lilly/LRAP Wang, Junmei (PI) 07/01/2024-06/30/2026
Title: ABCG2 charge model-based platform for rational drug design
Total Award Amount: $250,513 (to PITT) Effort: 5%
Major Goals: The major goal of this proposal is to develop a platform to facilitate structure-based drug design. This ABCG2 charge model-based drug design platform, coined ADDP, seamlessly incorporates high-accurate electrostatic interaction calculation and machine learning/deep learning (ML/DL) to calculate protein-ligand binding free energy accurately and efficiently.
Role: PI
ACCESS/BIO220051 Wang, Junmei (PI) 6/1/2022-12/31/2022
Title: Discovery of structural and dynamic information of tau oligomerization process through molecular simulations of full-length tau proteins
Total GPU hours: 30,000
Goal: This proposal seeks to elucidate the oligomerization mechanism of full-length tau protein through multiscale molecular dynamics simulations
Role: PI
NIH/K25AG070277 Man, Viet (PI) 8/15/2021-7/31/2026
Title: Development of novel computational protocols to study amyloid oligomerization
Total Cost: $736,240 Effort: 0%
Goals: This career development grant provides support for research on amyloid oligomerization that is associated with neurodegenerative diseases.
Role: Primary Mentor
PENDING
NIH/R01LM014509-01 Wang, Junmei (PI) 09/01/2024 - 08/31/2028
Title: Development of a PBPK model repository for biomedical research
Total Cost: $1,590,000 Effort:10%
Goals: Physiologically based pharmacokinetic (PBPK) modeling plays an increasingly important role in drug development, therefore, global regulatory agencies including the FDA encourage the inclusion of PBPK modeling in new drug applications, as PBPK models can be applied to study drug-drug interactions and design/optimize dosage regimens for special populations to achieve precision medicine. However, how to efficiently use those PBPK models can be challenging as they are disseminated in a variety of journals and many of them need further optimization. To overcome this challenge, we propose to build a public PBPK model repository to collect/optimize 200+ published PBPK models, develop 100+ new models, and then store the validated models in the SimCYP workspace format to guarantee model reproducibility, easy access, and retrieval.
Role: PI
NIH/R01000000-00 Wang, Junmei (PI) 02/01/2025 - 01/31/2030
Title: Discovery of Inhibitors for Tau Oligomerization
Total Cost: $1,590,000 Effort:20%
Goals: We plan to utilize large multiscale molecular simulations to elucidate the molecular mechanisms that govern pathological tau oligomerization in the earliest stage of tau aggregation, which will provide a structural basis for the subsequent drug screenings and de novo drug design. We will utilize an iterative process to develop 2-3 drug candidates that have optimal in vivo efficacy in reducing tauopathy and possess satisfactory DMPK and safety profiles for further preclinical development.
Role: PI
NIH/ R01CA281365 Xie, Lei (PI) 06/01/2023-05/31/2028
Title: Systems pharmacology-oriented humanized phenotype screening for precision
drug discovery
Total Award Amount: $464,315 (to PITT) Effort: 5%
Major Goals: The goal of this project is to develop computational tools that can predict patient
drug responses from cell line screens for anti-cancer drug discovery and personalized medicine
COMPLETED
NIH/NIGMS/1R01GM079383 Duan, Yong (PI) 09/28/2007-02/27/2014
Title: AMBER force field consortium: a coherent biomolecular simulation platform
Total Cost: $281,400 Effort:25%
Goals: The major goals of the project are to develop polarizable force fields for proteins, nucleic acids and organic molecules.
Role: co-PI of this MPI grant
NIH/NIGMS/5R01GM079383 Duan, Yong (PI) 03/26/2015-02/29/2020
Title: AMBER force field consortium: a coherent biomolecular simulation platform
Total Cost: $281,400 Effort:25%
Goals: The major goals of the project are to develop polarizable force fields for proteins, nucleic acids and organic molecules.
Role: co-PI of this MPI grant
NIH/NIGMS/ 5R21GM097617-02 Wang, Junmei (PI) 09/01/2011-08/31/2014
Title: Protein design using physical scoring functions integrated with site couplings
Total Cost: $396,875 Effort:35%
Goals: We intend to develop novel approaches to conquer the challenges in protein design. The new protein design strategies can facilitate us to engineer dynamical controls into a novel protein so that it can undertake a dynamic function. The novel approaches could be used to develop more effective biomedicine.
Role: PI
XtalPI/CGAFF Wang, Junmei 09/01/2019-08/31/2020
Title: Evaluation and reparameterization of GAFF2 for modeling crystal structures of drug molecules
Total Cost: $60,000 Effort:0%
Goals: We intend to develop a special version of GAFF2 force field for the prediction of crystal structures of drugs and drug candidates
Role: PI
NIH/ULITR001857/QUMP Wang, Junmei 02/01/2019-01/31/2020
Title: Quantitatively predict drug-drug interactions between oxycodone and other drugs by PBPK modeling and molecular modeling
Total Cost: $10,000 Effort:0%
Goals: We intend to predict drug-drug interactions between oxycodone and benzodiazepines and elucidate the underlying mechanisms through physiologically-based PK modeling and simulations as well as molecular modeling and simulations.
Role: PI for the QUMP grant
NIH/3R01MH113857 - 02W1 Price, Rebecca (PI) 12/01/18-06/30/22
Title: Improving precision of ketamine metabolite assays
Total Cost: $46,468 Effort: 7.0%
Goals: This project seeks to identify the neural and cognitive changes that accompany rapid relief from depressive symptoms following intravenous ketamine.
Role: Pharmacokinetics modeling expert
