Directory Profile

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 he 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. He has a rich background in pharmaceutical industry due to his working experience in Encysive Pharmaceuticals as a Senior Scientist.

Dr. Wang is also a long-term Developer of the Amber Software (www.ambermd.org). He and other collaborators developed a set of popular AMBER force fields, such as FF99, GAFF and polarizable FF based on Thole’s dipole-interaction models as well as the Antechamber module implemented in AMBER software packages.

Dr. Wang’s research interests fall into three directions. First, he is dedicated to develop high quality physical scoring functions to study protein-ligand interactions. Ongoing projects include the second generation of the general-AMBER force field (GAFF2), the third generation of the general-AMBER force field (GAFF3), polarizable force fields based on atomic dipole interaction, solvation models, efficient methods for calculating entropies, and toolkits that facilitate users to study protein-ligand interactions using both the endpoint and pathway methods. 

A chief application of the developed molecular mechanics force fields and toolkits is to elucidate the molecular mechanisms of how small molecule inhibitors mediate protein and nucleic acid targets using molecular dynamics simulations, and then to rationally design high potent agonists or antagonists to enhance or eradicate the functions of the protein or nucleic acid targets. Dr. Wang’s research group is involved in many drug discovery projects, including Toll-like receptors, Cannabinoid receptors, Tachykinin receptor 1, Mechanosensitive channel of large conductance(MscL). During this procedure, his force field models and toolkits are rigorously scrutinized and critically assessed through direct comparisons with experiments.
 
The third research direction in Dr. Wang’s lab is on pharmacometrics and systems pharmacology (PSP). He is interested in studying drug-drug interactions from the perspectives of both pharmacokinetics and pharmacodynamics.

2021
1. Man, V.; Wu, X.; He, X.; Xie, X.-Q.; Brooks, B.; Wang, J. Determination of van der Waals Parameters Using A Double Exponential Potential for Nonbonded Divalent Metal Cations in TIP3P Solvent. J. Chemical Theory and Computation. 2021 Feb; 17(2):1086-1097. PMID:33503371

2. Ji B, He X, Zhang Y, Zhai J, Man V, Liu S, Wang J. Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities. J. Cheminformatics, 2021 Feb;13(1):11. PMID:33588902

3. Ji B, He X, Zhai J, Zhang Y, Man V, Wang J. Machine Learning on Ligand-Residue Interaction Profiles to Significantly Improve Binding Affinity Prediction. Briefings in Bioinformatics, 2021 Mar;bbab054. PMID:33758923

4. Ji B, Xue Y, Xu Y, Liu S, Gough A, Xie, X.-Q., Wang J. Drug-Drug Interaction Between Oxycodone and Diazepam by a Combined in silico Pharmacokinetic and Pharmacodynamic Modeling Approach. ACS Chemical Neuroscience, 2021 May; 12(10):1777-1790. PMID:33950681

5. Zhang Y, He X, Zhai J, Ji B. Man V, Wang J. In silico binding profile characterization of ARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor. Briefings in Bioinformatics, 2021 May; bbab188. PMID:34013346

6. Man, V. H.; Wang, J.; Derreumaux, P.; Nguyen, P. H., Nonequilibrium molecular dynamics simulations of infrared laser-induced dissociation of a tetrameric Abeta42 beta-barrel in a neuronal membrane model. Chem Phys Lipids 2021, 234, 105030.

7. Strand, A.; Shen, S.-T.; Tomchick, D.; Wang, J.; Wang, C.-R.; Deisenhofer, J., Structure and dynamics of major histocompatibility class Ib molecule H2-M3 complexed with mitochondrial-derived peptides. J Biomol Struct & Dynamics 2021.

8. Wang, E.; Fu, W.; Jiang, D.; Sun, H.; Wang, J.; Zhang, X.; Weng, G.; Liu, H.; Tao, P.; Hou, T., VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein–Ligand Binding Free Energy Calculations. J Chem Info Model 2021, In Press.

9. Man, V. H.; Li, M. S.; Derreumaux, P.; Wang, J.; Nguyen, P., Molecular Mechanism of Ultrasound Induced Structural Defects in Liposomes: a Nonequilibrium Molecular Dynamics Simulation Study. Langmuir 2021, In Press.

2020
1. Zhai, J.; Liu, S.; Ji, B.; Zhang, Y.; Wang, J.* PBPK Models of Two CNS Stimulants, Amphetamine and Methylphenidate, for Clinical Dosing Regimen Optimizations. Archives of Pharmaceuticals & Pharmacology Research. Article Number: 000545, 2020 (DOI: 10.33552/APPR.2019.02.000545)

2. Xing, C.; Zhuang, Y.; Xu, T.-H.; Feng, Z.; Zhou, X.; Chen, M.; Wang, L.; Meng, X.; Xue, Y.; Wang, J.; Liu, H.; McGuire, T.; Zhao, G.; Melcher, K.; Zhang, C.*; Xu, E.*; Xie, X.-Q.* Cryo-EM Structure of the Human Cannabinoid Receptor CB2-G(i) Signaling Complex. Cell, 180 (4), 645-654.e13, 2020. (DOI: 10.1016/j.cell.2020.01.007)

3. Wang J.* Fast Identification of Possible Drug Treatment of Coronavirus Disease -19 (COVID-19) Through Computational Drug Repurposing Study. Chemrxiv, 2020. (https://chemrxiv.org/articles/Fast_Identification_of_Possible_Drug_Treatment_of_Coronavirus_Disease-19_COVID-19_Through_Computational_Drug_Repurposing_Study/11875446)

4. He, X.; Liu, S.; Lee. T.-S.; Ji, B.; Man, V.; York, D.; Wang, J.* 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, 5, 9, 4611-4619, 2020. (https://doi.org/10.1021/acsomega.9b04233)

5. Ji, B.; Liu, S.; He, X.; Man, V.; Xie, X.-Q.; Wang, J.* 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, 11, 8, 1139-1158, 2020.

6. Bogetti, X.; Ghosh, S.; Gamble, J. A.; Wang, J.*; Saxena, S.* Molecular Dynamics simulations based on newly developed force field parameters for Cu(II) spin labels provide insights into Double Histidine-based Double Electron-Electron Resonance. Journal of Physical Chemistry B. 124, 14, 2788-2797, 2020.

7. Wray, R.; Wang, J.*; Iscla, I.*; Blount, P.* Novel MscL agonists that allow multiple antibiotics cytoplasmic access activate the channel through a common binding site. PLOS One, 0228153, 2020. (https://doi.org/10.1371/journal.pone.0228153)

8. Wang, E.; Liu, H.; Wang, J.; Weng, G.; Sun, H.; Wang, Z.; Kang, Y.; Hou, T. Development and Evaluation of MM/GBSA based on a Variable Dielectric GB Model for Predicting Protein-Ligand Binding Affinities, J. Chem. Info. Model., 60, 11, 5353-5365 2020. (DOI: 10.1021/acs.jcim.0c00024)

9. Derreumaux, P.; Man, V.; Wang, J.; Nguyen, P. Tau R3-R4 Domain Dimer of the Wild Type and Phosphorylated Ser356 Sequences. I. In Solution by Atomistic Simulations. J. Phys. Chem. B. 124, 2975-2983, 2020.

10. He X.; Man, V.; Yang, W.; Lee, T.-S.; Wang, J. A Fast and High-Quality Charge Model for the Next Generation General AMBER Force Field. J. Chemical Physics, 153, 114502, 2020. (JCP20-AR-CLMD2020-02364)

11. Man, V.; He, X.; Ji, B.; Liu, S.; Xie, X.-Q.; Wang, J.* Introducing virtual oligomerization inhibition to identify potent inhibitors of Aβ oligomerization. J. Chem. Theory & Computation, 2020 Jun; 16(6):3920-3935. PMID:32307994

12. Wang J. Fast Identification of Possible Drug Treatment of Coronavirus Disease -19 (COVID-19) Through Computational Drug Repurposing Study. J. Chem. Info. Model., 2020 Jun; 60:3277-3286. PMID: 32315171

13. Hao, D.; He, X.; Ji, B.; Wang, J. How Well Does Extended Linear Interaction Energy Method Perform in Accurate Binding Free Energy Calculations. J. Chem. Info. Model., 2020 Dec; 60(12):6624-6633. PMID:33213150

14. Jarvi, A.; Sargun, A.; Bogetti, X.; Wang, J.*; 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. J. Phys. Chem. B, 2020, Sep;124(35):7544-7556. PMID:32790374

15. Ghosh, S.; Casto, J.; Bogetti X.; Arora, C.; Wang, J.*; 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. Phys Chem Chem Phys, 2020 Dec;22, 26707-26719. PMID:33159779 (* corresponding authors)

16. Xavier, B.; Zein, A.; Venes, A.; Wang, J.*, Lee, J.-Y.* Transmembrane Polar Relay Drives the Allosteric Regulation for ABCG5/G8 Sterol Transporter. Int J Molec Sci, 2020 Nov; 21(22):8747. PMID:33228147 (* corresponding authors)

17. Xue, Y.; Hu, Z.; Jing, Y.; Wu, H.; Li, X.; Wang, J. Seybert, A.; Xie, X.-Q.; Lv, Q.; 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, J. Clin. Pharm. Therapeutics 2020, Oct; 45(5):1076-1086. PMID:32627223

18. Man, V. H.; Li, M. S.; Derreumaux, P.; Wang, J.; Nguyen, T. T.; Nangia, S.; Nguyen, P. H., Molecular mechanism of ultrasound interaction with a blood brain barrier model. J Chem Phys 2020, 153 (4), 045104.

19. Wei, H.; Qi, R.; Wang, J.; Cieplak, P.; Duan, Y.; Luo, R., Efficient formulation of polarizable Gaussian multipole electrostatics for biomolecular simulations. J Chem Phys 2020, 153 (11), 114116.

20. Kawasaki, T.; Man, V. H.; Sugimoto, Y.; Sugiyama, N.; Yamamoto, H.; Tsukiyama, K.; Wang, J.; Derreumaux, P.; Nguyen, P. H., Infrared Laser-Induced Amyloid Fibril Dissociation: A Joint Experimental/Theoretical Study on the GNNQQNY Peptide. J Phys Chem B 2020, 124 (29), 6266-6277.

21. Kim, P.; Li, H.; Wang, J.; Zhao, Z., Landscape of drug-resistance mutations in kinase regulatory hotspots. Brief Bioinform 2020.

2019
1. Wray, R.; Iscla, I.; Kovacs, Z.; Wang, J.; Blount, P. Novel compounds that specifically bind and modulate MscL: insights into channel gating mechanisms, FASEB Journal, 33, 3180-3189, 2019.

2. Taylor, C.; Cormier, K.; Keenan, S.; Earnest, S.; Stippec, S.; Wichaidit C.; Juang, Y.; Wang, J.; Shvartsman, S.; Goldsmith, E.; Cobb, M. Functional divergence caused by mutations in an energetic hotspot in ERK2. PNAS, 116 (31), 15514-15523, 2019.

3. Wang, J.; Ge, Y.; Xie, X.-Q. Development and testing of druglike screening libraries. J. Chem. Inf. Model., 59, 53-65, 2019.

4. Man, V.; He, X.; Derreumaux, P.; Ji, B.; Xie, X.-Q.; Nguyen, P.; Wang, J. Effects of all-atom molecular mechanics force fields on Amyloid peptide assembly: the case of Aβ16-22 Dimer. J. Chem. Theor. Comput. 15, 1440-1452, 2019.

5. Wang, J.; Cieplak, P.; Luo, R.; Duan, Y. Development of Polarizable Gaussian Model for Molecular Mechanical Calculations I: Atomic Polarizability Parameterization to Reproduce Ab Initio Anisotropy. J. Chem. Theor. Comput. 15, 1146-1158, 2019.

6. Man, V.; Truong, P.; Li, M.; Wang, J.; Van-Oanh, N.-T.; Derreumaux, P.; Nguyen, P. Molecular Mechanism of the Cell Membrane Pore Formation Induced by Bubble Stable Cavitation, J. Phys. Chem. B, 123, 71-78, 2019.

7. Bian, Y.; He, X.; Jing, Y.; Wang, L.; Wang, J.; Xie, X.-Q. “Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics-knowledgebase network analysis and integrated in silico modeling and simulation”, Acta Pharmacologica Sinica, 40, 374-386, 2019.

8. Wu, N.; Feng, Z.; He, X.; Kwon, T.; Wang, J.; Xie, X.-Q. Insight of Captagon Abuse by Chemogenomics Knowledgebase-guided System Pharmacology Target Mapping Analyses. Scientific Reports, 9:2268, 2019.

9. Yin, J.; Chapman, K.; Clark, L. D.; Shao, Z.; Borek, D.; Xu, Q.; Wang, J.; Rosenbaum, D.; Crystal structure of the human NK1 tachykinin receptor. PNAS, 115, 13264-13269, 2019.

10. Ge, H.; Bian, Y.; He, X.; Qian, K.; Xie, X.-Q.; Wang, J. L- Tetrahydroberberrubine shows antagonistic effects on Dopamine D1 and D2 receptors. J. Comput.-Aided Mol. Des., 33, 447-459, 2019.

11. Su, L.; Wang, Y.; Wang, J.; Moresco, E.; Boger, D.; Beutler, B. Zhang, H. Structural basis of TLR1/TLR2 activation by a synthetic agonist Diprovocim. J. Med. Chem, 62, 2938-2949, 2019.

12. Man, V.; Li, M.; Wang, J.; Derreumaux, P. and Nguyen, P. Nonequilibrium Atomistic Molecular Dynamics Simulation of Tubular Nano-motor Propelled by Bubble Propulsion. J. Chemical Physics, 150 (21), 024103, 2019.

13. Man, V.; Li, M.; Wang, J.; Derreumaux, P. and Nguyen, P. Interaction mechanism between the focused ultrasound and lipid membrane at the molecular level. J. Chemical Physics, 150 (21), 215101, 2019.

14. Wang, E.; Sun, H.; Wang, J.; Wang, Z.; Liu Hui, Zhang, J.; Hou, T. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chemical Reviews, 119(16), 9478-9508, 2019.

15. Ji, B.; Liu, S.; Xue, Y.; He, X.; Man, V.; Xie, X.-Q., Wang, J. Prediction of Drug-Drug Interactions Between Opioids and Overdose Benzodiazepines Using Physiologically-Based Pharmacokinetic (PBPK) Modeling and Simulation, Drugs in R&D, 19 (3), 297-305, 2019.

16. Man, V.*; He, X.; Ji, B.; Liu, S.; Xie, X.-Q., Wang, J.* The Dependence of Amyloid-β42 Self-Assembly on The Monomer Concentrations, ACS Chemical Neuroscience, 10 (11), 4643-4658, 2019.

17. Wray, R.; Herrera, N.; Iscla, I.; Wang, J.;* Blount, P.* An agonist of the MscL channel affects multiple bacterial species and increases membrane permeability and potency of common antibiotics. Mol MicroBiol. 112 (3), 896-905, 2019.

18. Bian, Y., Wang, J. Xie XQ., Deep convolutional generative adversarial network (dcGAN) models for the de novo design of small molecules targeting cannabinoid receptors. Molecular Pharmaceuticals, 16 (11), 4451-4460, 2019.

19. Liu, S.; Ji, B.; He, X.; Man, V.; Liu, J. Wang, J.* New Application of In Silico Methods in Identifying Key Components of the Anti-Cancer Herbal Formulation YIV-906 (PHY906), Physical Chemistry and Chemical Physics., 21 (42), 23501-23513, 2019.

20. Taylor, C.; Cormier, K.; Keenan, S.; Earnest, S.; Stippec, S.; Wichaidit C.; Juang, Y.; Wang, J.; Shvartsman, S.; Goldsmith, E.; Cobb, M. Functional divergence caused by mutations in an energetic hotspot in ERK2. PNAS, 116 (31), 15514-15523, 2019.

2018
1. Dominik, D.; Man, V.; Van-Oanh, N.-T.; Wang, J.; Kawasaki, T.; Derreumaux, P.; Nguyen, P. Breaking down cellulose fibrils with a mid-infrared laser, Cellulose, 25, 5553-5568, 2018.

2. Suno, R.; Kimura, K.; Nakane, T.; Yamashita, K.; Wang, J.; Fujiwara, T.;, Yamanaka, Y.; Im, D.; Horita, S.; Tsujimoto, H.; Tawaramoto, M.; Hirokawa, T.; Nango, E.; Tono, K.; Kameshima, T.; Hatsui, T.; Joti, Y.; Yabashi, M.; Shimamoto, K.; Yamamoto, M.; Rosenbaum, D. Iwata, S.; Shimamura, T.; Kobayashi, T. Crystal Structures of Human Orexin 2 Receptor Bound to the Subtype-Selective Antagonist EMPA. Structure, 26, 7-19, 2018.

3. Shang, J.; Hu, B.; Wang, J.; Zhu, F.; Kang, Y.; Li, D.; Sun, H.; Long, D.-X.; Hou, T. A cheminformatic insight into the differences between terrestrial and marine originated natural products, J. Chem. Info. Mod. 58, 1182-1193, 2018.

4. Xavier, B.; Zein, A.; Wang J.; Lee, J.-Y. Structural snapshot of the cholesterol-transport ABC proteins, Biochemistry & Cell Biology, 97, 224-233, 2018.

5. Chen, F.; Sun, H.; Wang, J.; Liu, H.; Wang, Z.; Lei, T.; Li, Y. Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes, 24, 1183-1194, 2018.

6. Wang, Y.; Lin, W.; Wu, N.; Wang, J.; Feng, Z.; Xie, X.-Q. An Insight of Acetaminophen and its Metabolites using Molecular Docking and Molecular Dynamics Simulation, J. Mol. Modeling, 24:243, 2018.

7. Liu, N.; Zhou, W.; Guo, Y.; Wang, J.; Fu, W.; Sun, H.; Li, D.; Duan, M.; Hou, T. Molecular Dynamics Simulations Revealed the Regulation of Ligands to the Interactions between Androgen Receptor and its Coactivator. J. Chem. Info. Model., 58, 1652-1661, 2018.

8. He, X.; Man, V.; Ji, B.; Xie, X.-Q.; Wang, J. Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3. J. Comput.-Aided Mol. Des. 33, 105-117, 2018.

Selected Publications Before 2018
1. Li, X.; Diao, J.; Greene, D.; Wang, J.; Luo, R. A continuum Poisson-Boltzmann model for membrane channel proteins”, J. Chem. Teor. Comput., 13, 3398-3412, 2017.

2. Wray, R.; Iscla, I.; Wang, J.*; Blount P*. Dihydrostreptomycin directly binds to, modulates, and passes through the MscL channel pore. PLOS Biology, 14, e1002473, 2016.

3. Shao, Z. H.; Yin, J.; Chapman, K.; Grzemska, M.; Clark, L.; Wang, J.; Rosenbaum, D. "High-resolution crystal structure of the human CB1 cannabinoid receptor", Nature, 540, 602-606, 2016.

4. Lee, J.-Y. ; Kinch, L. N.; Borek, D. M.; , Wang, J.; Wang, J.; Urbatsch, I. L.; Xie, X.-S.; Grishin, N. V.; Cohen, J. C.; Otwinowski, Z.; Hobbs, H. H.; Rosenbaum, D. M., Crystal structure of the human sterol transporter ABCG5/ABCG8, Nature, 533, 561-564, 2016.

5. Wray, R.; Iscla, I.; Wang, J.*; Blount P*. Dihydrostreptomycin directly binds to, modulates, and passes through the MscL channel pore. PLOS Biology, 14, e1002473, 2016. (* corresponding authors)

6. Wang, J.; Cieplak, P.; Li, J.; Cai, Q.; Hsieh, M. J.; Luo, R.; Duan, Y. Development of polarizable models for molecular mechanical calculations IV: van der Waals parameterization. J. Phys. Chem. B, 116, 7088-7101, 2012.

7. Wang, J.; Hou, T. Develop and Test a Solvent Accessible Surface Area-Based Model in Conformational Entropy Calculations. J. Chem. Info. Model., 52, 1199-1212, 2012.

8. Wang, J.; Cieplak, P.; Li, J.; Hou, T.; Luo, R.; Duan, Y. Development of polarizable models for molecular mechanical calculations I: Parameterization of atomic polarizability. J. Phys. Chem. B, 115, 3091-3099, 2011.

9. Wang, J.; Cieplak, P.; Li, J.; Wang, J.; Cai, Q.; Hsieh, M. J.; Lei, H.; Luo, R.; Duan, Y. Development of polarizable models for molecular mechanical calculations II: Induced dipole models significantly improve accuracy of intermolecular interaction energies. J. Phys. Chem. B, 115, 3100-3111, 2011.

10. Wang, J.*; Hou, T. Application of molecular dynamics simulations in molecular property prediction II: Diffusion coefficients. J. Comput. Chem., 32, 3505-3519, 2011.
18. Wang, J.*; Hou, T. Application of molecular dynamics simulations in molecular property prediction I: Density and heat of vaporization. J. Chem. Theory Comput., 7, 2151-2165, 2011.

11. Hou, T.; Wang, J.; Li, 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. J. Chem. Info. Model., 51:69-82, 2011. [cited 800 times]

12. Hou, T.; Wang, J.; Li, 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. J. Comput. Chem., 32, 866-877, 2011. [cited 327 times]

13. Wang, J.*; Hou, T. Drug and drug candidate building block analysis. J. Chem. Info. Model., 50, 55-67, 2010.

14. Wang, J.*; Hou, T.; Xu, X. Aqueous solubility prediction based on weighted atom type counts and solvent accessible surface areas. J. Chem. Info. Model., 49, 571-581, 2009.

15. Wang, J.*; Krudy, G.; Hou, T.; Holland, G.; Xu, X. Development of reliable aqueous solubility models and their application in drug-like analysis. J. Chemical Information and Modeling, 47, 1395-1404, 2007.

16. Wang, J.*; Wang, W.; Kollman, P. A.; Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations, J. Mol. Grap. Mod., 25, 247-260, 2006. [cited 1,860 times]

17. Wang, J.*; Krudy, G.; Xie, X.-Q.; Wu, C.; Holland, G. Genetic algorithm-optimized QSPR models for bioavailability, protein binding, and urinary excretion, J. Chem. Info. Model., 46, 2674-2683, 2006.

18. Wang, J.*; Hou, T.; Xu, X. Recent advances in free energy calculations with a combination of molecular mechanics and continuum models, Current Computer-Aided Drug Design, 2, 95-103, 2006.

19. Wang, J.*; Kang, X.; Kollman, P. A.; Kuntz, I. D. Hieratical database screening for HIV-1 reverse transcriptase using pharmacophore model, rigid docking, solvation docking and MM-PB/SA, J. Med. Chem. 48, 2432-2444, 2005.

20. Wang, J.; Wolf, R.; Caldwell, J.; Kollman, P. A.; Case, D. A. Development and test of a general AMBER force field, J. Comput. Chem. 25, 1157-1174, 2004. [cited 6,534 times]

21. Huo, S.; Wang, J.; 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, J. Med. Chem., 45, 1412-1419, 2002. [cited 155 times]

22. Wang, J.;, 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, J. Am. Chem. Soc., 123, 5221-5230, 2001. [cited 524 times]

23. Wang, J.; Wang, W.; Huo, S.; Lee M.; Kollman, P. A. A solvation model based on weighted solvent accessible surface area, J. Phys. Chem. B., 105, 5055-5067, 2001.

24. Wang, J.; Cieplak, P.; Kollman, P. A. How well does a restrained electrostatic potential (RESP) mode perform in calculating conformational energies of organic and biological molecules? J. Comput. Chem., 21, 1049-1074, 2000. [cited 2,910 times]

Selected as the Graduate Faculty Member of The Year at School of Pharmacy, University of Pittsburgh, April, 2020.

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