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Gan JL, Kumar D, Chen C, Taylor BC, Jagger BR, Amaro RE, Lee CT. Benchmarking ensemble docking methods in D3R Grand Challenge 4. J Comput Aided Mol Des 2022; 36:87-99. [PMID: 35199221 PMCID: PMC8907095 DOI: 10.1007/s10822-021-00433-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 11/16/2021] [Indexed: 11/30/2022]
Abstract
The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.
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Affiliation(s)
- Jessie Low Gan
- San Diego Jewish Academy, San Diego, 92130, CA, USA.,California Institute of Technology, Pasadena, CA, 91125, USA
| | - Dhruv Kumar
- Rancho Bernardo High School, San Diego, CA, 92128, USA.,University of California Berkeley, Berkeley, CA, USA
| | - Cynthia Chen
- California Institute of Technology, Pasadena, CA, 91125, USA.,Canyon Crest Academy, San Diego, CA, 92130, USA
| | - Bryn C Taylor
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA.,Discovery Sciences, Janssen Research and Development, San Diego, CA, 92121, USA
| | - Benjamin R Jagger
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA.,Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Christopher T Lee
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
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2
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Eckert WA, Wiener JJM, Cai H, Ameriks MK, Zhu J, Ngo K, Nguyen S, Fung-Leung WP, Thurmond RL, Grice C, Edwards JP, Chaplan SR, Karlsson L, Sun S. Selective inhibition of peripheral cathepsin S reverses tactile allodynia following peripheral nerve injury in mouse. Eur J Pharmacol 2020; 880:173171. [PMID: 32437743 DOI: 10.1016/j.ejphar.2020.173171] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 04/29/2020] [Accepted: 05/04/2020] [Indexed: 10/24/2022]
Abstract
Cathepsin S (CatS) is a cysteine protease found in lysosomes of hematopoietic and microglial cells and in secreted form in the extracellular space. While CatS has been shown to contribute significantly to neuropathic pain, the precise mechanisms remain unclear. In this report, we describe JNJ-39641160, a novel non-covalent, potent, selective and orally-available CatS inhibitor that is peripherally restricted (non-CNS penetrant) and may represent an innovative class of immunosuppressive and analgesic compounds and tools useful toward investigating peripheral mechanisms of CatS in neuropathic pain. In C57BL/6 mice, JNJ-39641160 dose-dependently blocked the proteolysis of the invariant chain, and inhibited both T-cell activation and antibody production to a vaccine antigen. In the spared nerve injury (SNI) model of chronic neuropathic pain, in which T-cell activation has previously been demonstrated to be a prerequisite for the development of pain hypersensitivity, JNJ-39641160 fully reversed tactile allodynia in wild-type mice but was completely ineffective in the same model in CatS knockout mice (which exhibited a delayed onset in allodynia). By contrast, in the acute mild thermal injury (MTI) model, JNJ-39641160 only weakly attenuated allodynia at the highest dose tested. These findings support the hypothesis that blockade of peripheral CatS alone is sufficient to fully reverse allodynia following peripheral nerve injury and suggest that the mechanism of action likely involves interruption of T-cell activation and peripheral cytokine release. In addition, they provide important insights toward the development of selective CatS inhibitors for the treatment of neuropathic pain in humans.
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Affiliation(s)
- William A Eckert
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA.
| | - John J M Wiener
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Hui Cai
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Michael K Ameriks
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Jian Zhu
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Karen Ngo
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Steven Nguyen
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Wai-Ping Fung-Leung
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Robin L Thurmond
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Cheryl Grice
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - James P Edwards
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Sandra R Chaplan
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Lars Karlsson
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
| | - Siquan Sun
- Janssen Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA, 92121, USA
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3
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The role of human in the loop: lessons from D3R challenge 4. J Comput Aided Mol Des 2020; 34:121-130. [PMID: 31965405 DOI: 10.1007/s10822-020-00291-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 01/14/2020] [Indexed: 12/27/2022]
Abstract
The rapid development of new machine learning techniques led to significant progress in the area of computer-aided drug design. However, despite the enormous predictive power of new methods, they lack explainability and are often used as black boxes. The most important decisions in drug discovery are still made by human experts who rely on intuitions and simplified representation of the field. We used D3R Grand Challenge 4 to model contributions of human experts during the prediction of the structure of protein-ligand complexes, and prediction of binding affinities for series of ligands in the context of absence or abundance of experimental data. We demonstrated that human decisions have a series of biases: a tendency to focus on easily identifiable protein-ligand interactions such as hydrogen bonds, and neglect for a more distributed and complex electrostatic interactions and solvation effects. While these biases still allow human experts to compete with blind algorithms in some areas, the underutilization of the information leads to significantly worse performance in data-rich tasks such as binding affinity prediction.
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4
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Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S. J Comput Aided Mol Des 2019; 33:1095-1105. [PMID: 31729618 DOI: 10.1007/s10822-019-00247-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 11/02/2019] [Indexed: 12/12/2022]
Abstract
Cathepsin S (CatS), a member of cysteine cathepsin proteases, has been well studied due to its significant role in many pathological processes, including arthritis, cancer and cardiovascular diseases. CatS inhibitors have been included in D3R-GC3 for both docking pose prediction and affinity ranking, and in D3R-GC4 for binding affinity ranking. The difficulties posed by CatS inhibitors in D3R mainly come from three aspects: large size, high flexibility and similar chemical structures. We have participated in GC4; our best submitted model, which employs a similarity-based alignment docking and Vina scoring protocol, yielded Kendall's τ of 0.23 for 459 binders in GC4. In our further explorations with machine learning, by curating a CatS specific training set, adopting a similarity-based constrained docking method as well as an arm-based fragmentation strategy which can describe large inhibitors in a locality-sensitive fashion, our best structure-based ranking protocol can achieve Kendall's τ of 0.52 for all binders in GC4. In this exploration process, we have demonstrated the importance of training data, docking approaches and fragmentation strategies in inhibitor-ranking protocol development with machine learning.
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Zou J, Tian C, Simmerling C. Blinded prediction of protein-ligand binding affinity using Amber thermodynamic integration for the 2018 D3R grand challenge 4. J Comput Aided Mol Des 2019; 33:1021-1029. [PMID: 31555923 DOI: 10.1007/s10822-019-00223-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Accepted: 09/13/2019] [Indexed: 11/26/2022]
Abstract
In the framework of the 2018 Drug Design Data Resource grand challenge 4, blinded predictions on relative binding free energy were performed for a set of 39 ligands of the Cathepsin S protein. We leveraged the GPU-accelerated thermodynamic integration of Amber 18 to advance our computational prediction. When our entry was compared to experimental results, a good correlation was observed (Kendall's τ: 0.62, Spearman's ρ: 0.80 and Pearson's R: 0.82). We designed a parallelized transformation map that placed ligands into several groups based on common alchemical substructures; TI transformations were carried out for each ligand to the relevant substructure, and between substructures. Our calculations were all conducted using the linear potential scaling scheme in Amber TI because we believe the softcore potential/dual-topology approach as implemented in current Amber TI is highly fault-prone for some transformations. The issue is illustrated by using two examples in which typical preparation for the dual-topology approach of Amber TI fails. Overall, the high accuracy of our prediction is a result of recent advances in force fields (ff14SB and GAFF), as well as rapid calculation of ensemble averages enabled by the GPU implementation of Amber. The success shown here in a blinded prediction strongly suggests that alchemical free energy calculation in Amber is a promising tool for future commercial drug design.
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Affiliation(s)
- Junjie Zou
- Department of Chemistry, Stony Brook University, Stony Brook, NY, 11794-3400, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794-3400, USA
| | - Chuan Tian
- Department of Chemistry, Stony Brook University, Stony Brook, NY, 11794-3400, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794-3400, USA
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, NY, 11794-3400, USA.
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794-3400, USA.
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6
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Koukos PI, Xue LC, Bonvin AMJJ. Protein-ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3. J Comput Aided Mol Des 2019; 33:83-91. [PMID: 30128928 PMCID: PMC6373529 DOI: 10.1007/s10822-018-0148-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 08/09/2018] [Indexed: 12/30/2022]
Abstract
We report the performance of HADDOCK in the 2018 iteration of the Grand Challenge organised by the D3R consortium. Building on the findings of our participation in last year's challenge, we significantly improved our pose prediction protocol which resulted in a mean RMSD for the top scoring pose of 3.04 and 2.67 Å for the cross-docking and self-docking experiments respectively, which corresponds to an overall success rate of 63% and 71% when considering the top1 and top5 models respectively. This performance ranks HADDOCK as the 6th and 3rd best performing group (excluding multiple submissions from a same group) out of a total of 44 and 47 submissions respectively. Our ligand-based binding affinity predictor is the 3rd best predictor overall, behind only the two leading structure-based implementations, and the best ligand-based one with a Kendall's Tau correlation of 0.36 for the Cathepsin challenge. It also performed well in the classification part of the Kinase challenges, with Matthews Correlation Coefficients of 0.49 (ranked 1st), 0.39 (ranked 4th) and 0.21 (ranked 4th) for the JAK2, vEGFR2 and p38a targets respectively. Through our participation in last year's competition we came to the conclusion that template selection is of critical importance for the successful outcome of the docking. This year we have made improvements in two additional areas of importance: ligand conformer selection and initial positioning, which have been key to our excellent pose prediction performance this year.
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Affiliation(s)
- Panagiotis I Koukos
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Li C Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
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7
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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 2018; 33:105-117. [PMID: 30218199 DOI: 10.1007/s10822-018-0162-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 09/10/2018] [Indexed: 10/28/2022]
Abstract
We participated in the Cathepsin S (CatS) sub-challenge of the Drug Design Data Resource (D3R) Grand Challenge 3 (GC3) in 2017 to blindly predict the binding poses of 24 CatS-bound ligands, the binding affinity ranking of 136 ligands, and the binding free energies of a subset of 33 ligands in Stage 1A and Stage 2. Our submitted predictions ranked relatively well compared to the submissions from other participants. Here we present our methodologies used in the challenge. For the binding pose prediction, we employed the Glide module in the Schrodinger Suite 2017 and AutoDock Vina. For the binding affinity/free energy prediction, we carried out molecular dynamics simulations of the complexes in explicit water solvent with counter ions, and then estimated the binding free energies with our newly developed model of extended linear interaction energy (ELIE), which is inspired by two other popular end-point approaches: the linear interaction energy (LIE) method, and the molecular mechanics with Poisson-Boltzmann surface area solvation method (MM/PBSA). Our studies suggest that ELIE is a good trade-off between efficiency and accuracy, and it is appropriate for filling the gap between the high-throughput docking and scoring methods and the rigorous but much more computationally demanding methods like free energy perturbation (FEP) or thermodynamics integration (TI) in computer-aided drug design (CADD) projects.
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8
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Lei Y, Hu T, Wu X, Wu Y, Xiang H, Sun H, You Q, Zhang X. Microwave-assisted copper- and palladium-catalyzed sonogashira-type coupling of aryl bromides and iodides with trimethylsilylacetylene. Tetrahedron Lett 2016. [DOI: 10.1016/j.tetlet.2016.01.088] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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9
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Yogeeswari P, Sharma M, Samala G, Gangadhar M, Karthick S, Mallipeddi S, Semwal A, Sriram D. Discovery of novel tetrahydro-pyrazolo [4,3-c] pyridines for the treatment of neuropathic pain: synthesis and neuropharmacology. Eur J Med Chem 2013; 66:211-20. [PMID: 23807113 DOI: 10.1016/j.ejmech.2013.05.022] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2013] [Revised: 05/11/2013] [Accepted: 05/18/2013] [Indexed: 10/26/2022]
Abstract
We disclose the discovery of a novel series of tetrahydropyrido-pyrazoles that are potent inhibitors of tumour necrosis factor-alpha (TNF-α), nitric oxide and cannabinoid receptor subtype 1 (CB₁). We report herein the synthesis and neuropharmacological screening results of the titled compounds in two acute pain and two neuropathic pain models in rodents. Particularly the analogue N-(4-bromophenyl)-3-tert-butyl-5-ethyl-4,5,6,7-tetrahydro-1H-pyrazolo[4,3-c]pyridine-1-carboxamide (8a) exhibited pronounced acute antinociceptive efficacy, also being effective in chronic constriction injury (ED₅₀ = 23.8 mg/kg) and partial sciatic nerve injury (ED₅₀ = 29.0 mg/kg) models with CB₁ receptor activity (IC₅₀ = 49.6 nM) and inhibitory effect on TNF-α (86.4% inhibition at 100 mg/kg). These results suggest the importance of the development of this lead as multi-targeted treatment strategy for neuropathic pain.
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Affiliation(s)
- Perumal Yogeeswari
- Neuropathic Pain Research Laboratory, Department of Pharmacy, Birla Institute of Technology & Science - Pilani, Hyderabad Campus, RR District, 500078 Andhra Pradesh, India.
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10
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Wiener JJ, Wickboldt AT, Nguyen S, Sun S, Rynberg R, Rizzolio M, Karlsson L, Edwards JP, Grice CA. Pyrazole-based arylalkyne Cathepsin S inhibitors. Part III: Modification of P4 region. Bioorg Med Chem Lett 2013; 23:1070-4. [DOI: 10.1016/j.bmcl.2012.12.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2012] [Revised: 11/28/2012] [Accepted: 12/10/2012] [Indexed: 11/25/2022]
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11
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Agrafiotis DK, Wiener JJM, Skalkin A, Kolpak J. Single R-Group Polymorphisms (SRPs) and R-Cliffs: An Intuitive Framework for Analyzing and Visualizing Activity Cliffs in a Single Analog Series. J Chem Inf Model 2011; 51:1122-31. [DOI: 10.1021/ci200054u] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Dimitris K. Agrafiotis
- Informatics, Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - John J. M. Wiener
- Medicinal Chemistry, Johnson & Johnson Pharmaceutical Research & Development, L.L.C., 3210 Merryfield Road, San Diego, California 92121, United States
| | - Andrew Skalkin
- Informatics, Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Jeremy Kolpak
- Informatics, Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
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13
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Park K, Bae G, Park A, Kim Y, Choe J, Song KH, Lee S. Synthesis of symmetrical diarylalkyne from palladium-catalyzed decarboxylative couplings of propiolic acid and aryl bromides under water. Tetrahedron Lett 2011. [DOI: 10.1016/j.tetlet.2010.11.110] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Agrafiotis DK, Wiener JJM. Scaffold Explorer: An Interactive Tool for Organizing and Mining Structure−Activity Data Spanning Multiple Chemotypes. J Med Chem 2010; 53:5002-11. [DOI: 10.1021/jm1004495] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Dimitris K. Agrafiotis
- Johnson & Johnson Pharmaceutical Research & Development, LLC, Welsh & McKean Roads, Spring House, Pennsylvania 19477
| | - John J. M. Wiener
- Johnson & Johnson Pharmaceutical Research & Development, LLC, 3210 Merryfield Road, San Diego, California 92121
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15
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Discovery and SAR of novel pyrazole-based thioethers as cathepsin S inhibitors: Part 1. Bioorg Med Chem Lett 2010; 20:2370-4. [DOI: 10.1016/j.bmcl.2010.01.108] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2009] [Revised: 01/15/2010] [Accepted: 01/20/2010] [Indexed: 11/24/2022]
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16
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Deng X, Liang JT, Peterson M, Rynberg R, Cheung E, Mani NS. Practical Synthesis of a Cathepsin S Inhibitor: Route Identification, Purification Strategies, and Serendipitous Discovery of a Crystalline Salt Form. J Org Chem 2010; 75:1940-7. [DOI: 10.1021/jo902650b] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Xiaohu Deng
- Johnson & Johnson Pharmaceutical Research & Development LLC, 3210 Merryfield Row, San Diego, California 92121
| | - Jimmy T. Liang
- Johnson & Johnson Pharmaceutical Research & Development LLC, 3210 Merryfield Row, San Diego, California 92121
| | - Matthew Peterson
- Johnson & Johnson Pharmaceutical Research & Development LLC, 3210 Merryfield Row, San Diego, California 92121
| | - Raymond Rynberg
- Johnson & Johnson Pharmaceutical Research & Development LLC, 3210 Merryfield Row, San Diego, California 92121
| | - Eugene Cheung
- Johnson & Johnson Pharmaceutical Research & Development LLC, 3210 Merryfield Row, San Diego, California 92121
| | - Neelakandha S. Mani
- Johnson & Johnson Pharmaceutical Research & Development LLC, 3210 Merryfield Row, San Diego, California 92121
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