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Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors

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Lagorce, David, Douguet, Dominique, Miteva, Maria A., Villoutreix, Bruno O.·2017·Scientific Reports

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Abstract

The modulation of PPIs by low molecular weight chemical compounds, particularly by orally bioavailable molecules, would be very valuable in numerous disease indications. However, it is known that PPI inhibitors (iPPIs) tend to have properties that are linked to poor Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and in some cases to poor clinical outcomes. Previously reported in silico analyses of iPPIs have essentially focused on physicochemical properties but several other ADMET parameters would be important to assess. In order to gain new insights into the ADMET properties of iPPIs, computations were carried out on eight datasets collected from several databases. These datasets involve compounds targeting enzymes, GPCRs, ion channels, nuclear receptors, allosteric modulators, oral marketed drugs, oral natural product-derived marketed drugs and iPPIs. Several trends are reported that should assist the design and optimization of future PPI inhibitors, either for drug discovery endeavors or for chemical biology projects.

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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139
Molecular properties that influence the oral bioavailability of drug candidates
Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020017n · Cited by 8,420
pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures
Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00104 · Cited by 5,716
A Theoretical Basis for a Biopharmaceutic Drug Classification: The Correlation of in Vitro Drug Product Dissolution and in Vivo Bioavailability
Amidon, G. L., Lennernäs, H., Shah, V. P., & Crison, J. R. (1995) · Pharmaceutical Research · doi:10.1023/a:1016212804288 · Cited by 5,430
Escape from Flatland: Increasing Saturation as an Approach to Improving Clinical Success
Lovering, F., Bikker, J., & Humblet, C. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm901241e · Cited by 4,495
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382
Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation
Zanger, U. M., & Schwab, M. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2012.12.007 · Cited by 4,169
New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays
Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 3,944
Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties
Ertl, P., Rohde, B., & Selzer, P. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm000942e · Cited by 3,084
The re-emergence of natural products for drug discovery in the genomics era
Harvey, A. L., Edrada-Ebel, R., & Quinn, R. J. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4510 · Cited by 2,572
The influence of drug-like concepts on decision-making in medicinal chemistry
Leeson, P. D., & Springthorpe, B. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2445 · Cited by 2,403
Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S., & Hopkins, A. L. (2012) · Nature Chemistry · doi:10.1038/nchem.1243 · Cited by 2,242
Reaching for high-hanging fruit in drug discovery at protein–protein interfaces
Wells, J. A., & McClendon, C. L. (2007) · Nature · doi:10.1038/nature06526 · Cited by 1,960
DataWarrior: An Open-Source Program For Chemistry Aware Data Visualization And Analysis
Sander, T., Freyss, J., von Korff, M., & Rufener, C. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500588j · Cited by 1,800
The ChEMBL bioactivity database: an update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1031 · Cited by 1,492
An analysis of the attrition of drug candidates from four major pharmaceutical companies
Waring, M. J., Arrowsmith, J., Leach, A. R., Leeson, P. D., Mandrell, S., Owen, R. M., Pairaudeau, G., Pennie, W. D., Pickett, S. D., Wang, J., et al. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4609 · Cited by 1,450
Escape from Flatland 2: complexity and promiscuity
Lovering, F. (2013) · MedChemComm · doi:10.1039/c2md20347b · Cited by 1,444
ESOL: Estimating Aqueous Solubility Directly from Molecular Structure
Delaney, J. S. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034243x · Cited by 1,091
Small-Molecule Inhibitors of Protein-Protein Interactions: Progressing toward the Reality
Arkin, M. R., Tang, Y., & Wells, J. A. (2014) · Chemistry & Biology · doi:10.1016/j.chembiol.2014.09.001 · Cited by 1,065
Generation of a Set of Simple, Interpretable ADMET Rules of Thumb
Gleeson, M. P. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm701122q · Cited by 969
Physiochemical drug properties associated with in vivo toxicological outcomes
Hughes, J. D., Blagg, J., Price, D. A., Bailey, S., DeCrescenzo, G. A., Devraj, R. V., Ellsworth, E., Fobian, Y. M., Gibbs, M. E., Gilles, R. W., et al. (2008) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2008.07.071 · Cited by 946
Predicting in vivo drug interactions from in vitro drug discovery data
Wienkers, L. C., & Heath, T. G. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1851 · Cited by 904
The impact of aromatic ring count on compound developability – are too many aromatic rings a liability in drug design?
Ritchie, T. J., & Macdonald, S. J. F. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.07.014 · Cited by 901
Lipophilicity in drug discovery
Waring, M. J. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441003605098 · Cited by 873
Oral Druggable Space beyond the Rule of 5: Insights from Drugs and Clinical Candidates
Doak, B. C., Over, B., Giordanetto, F., & Kihlberg, J. (2014) · Chemistry & Biology · doi:10.1016/j.chembiol.2014.08.013 · Cited by 835
Drug-Like Properties: Concepts, Structure Design and Methods from ADME to Toxicity Optimization
Kerns, E. H., & Di, L. (2016) · Cited by 784
Polar Molecular Surface Properties Predict the Intestinal Absorption of Drugs in Humans
Palm, K., Stenberg, P., Luthman, K., & Artursson1, P. (1997) · Pharmaceutical Research · doi:10.1023/a:1012188625088 · Cited by 756
Structural Alert/Reactive Metabolite Concept as Applied in Medicinal Chemistry to Mitigate the Risk of Idiosyncratic Drug Toxicity: A Perspective Based on the Critical Examination of Trends in the Top 200 Drugs Marketed in the United States
Stepan, A. F., Walker, D. P., Bauman, J., Price, D. A., Baillie, T. A., Kalgutkar, A. S., & Aleo, M. D. (2011) · Chemical Research in Toxicology · doi:10.1021/tx200168d · Cited by 698
BDDCS Applied to Over 900 Drugs
Benet, L. Z., Broccatelli, F., & Oprea, T. I. (2011) · The AAPS Journal · doi:10.1208/s12248-011-9290-9 · Cited by 626
Improving Drug Candidates by Design: A Focus on Physicochemical Properties As a Means of Improving Compound Disposition and Safety
Meanwell, N. A. (2011) · Chemical Research in Toxicology · doi:10.1021/tx200211v · Cited by 620
A Comprehensive Listing of Bioactivation Pathways of Organic Functional Groups
Kalgutkar, A., Gardner, I., Obach, R., Shaffer, C., Callegari, E., Henne, K., Mutlib, A., Dalvie, D., Lee, J., Nakai, Y., et al. (2005) · Current Drug Metabolism · doi:10.2174/1389200054021799 · Cited by 616
Passive Permeability and P-Glycoprotein-Mediated Efflux Differentiate Central Nervous System (CNS) and Non-CNS Marketed Drugs
Doan, K. M. M., Humphreys, J. E., Webster, L. O., Wring, S. A., Shampine, L. J., Serabjit-Singh, C. J., Adkison, K. K., & Polli, J. W. (2002) · The Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.102.039255 · Cited by 614
Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions
Lipinski, C. A. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.04.029 · Cited by 609
The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME–Tox)
Szakács, G., Váradi, A., Özvegy-Laczka, C., & Sarkadi, B. (2008) · Drug Discovery Today · doi:10.1016/j.drudis.2007.12.010 · Cited by 569
A Comparison of Physiochemical Property Profiles of Development and Marketed Oral Drugs
Wenlock, M. C., Austin, R. P., Barton, P., Davis, A. M., & Leeson, P. D. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm021053p · Cited by 546
Probing the links between in vitro potency, ADMET and physicochemical parameters
Gleeson, M. P., Hersey, A., Montanari, D., & Overington, J. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3367 · Cited by 503
Predicting drug metabolism: experiment and/or computation?
Kirchmair, J., Göller, A. H., Lang, D., Kunze, J., Testa, B., Wilson, I. D., Glen, R. C., & Schneider, G. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4581 · Cited by 502
In silico prediction of blood–brain barrier permeation
Clark, D. E. (2003) · Drug Discovery Today · doi:10.1016/s1359-6446(03)02827-7 · Cited by 483
Identification and Prediction of Promiscuous Aggregating Inhibitors among Known Drugs
Seidler, J., McGovern, S. L., Doman, T. N., & Shoichet, B. K. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm030191r · Cited by 476
Managing the challenge of chemically reactive metabolites in drug development
Park, B. K., Boobis, A., Clarke, S., Goldring, C. E. P., Jones, D., Kenna, J. G., Lambert, C., Laverty, H. G., Naisbitt, D. J., Nelson, S., et al. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3408 · Cited by 462
The Role of Transporters in the Pharmacokinetics of Orally Administered Drugs
Shugarts, S., & Benet, L. Z. (2009) · Pharmaceutical Research · doi:10.1007/s11095-009-9924-0 · Cited by 438
Finding the sweet spot: the role of nature and nurture in medicinal chemistry
Hann, M. M., & Keserü, G. M. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3701 · Cited by 411
Targeting protein–protein interactions as an anticancer strategy
Ivanov, A. A., Khuri, F. R., & Fu, H. (2013) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2013.04.007 · Cited by 401
Using the Golden Triangle to optimize clearance and oral absorption
Johnson, T. W., Dress, K. R., & Edwards, M. (2009) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2009.08.045 · Cited by 392
Nonleadlikeness and leadlikeness in biochemical screening
Rishton, G. M. (2003) · Drug Discovery Today · doi:10.1016/s1359644602025722 · Cited by 381
Mechanisms of Chemical Carcinogenicity and Mutagenicity: A Review with Implications for Predictive Toxicology
Benigni, R., & Bossa, C. (2011) · Chemical Reviews · doi:10.1021/cr100222q · Cited by 377
The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates
Kramer, J. A., Sagartz, J. E., & Morris, D. L. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2378 · Cited by 372
In silico ADME/T modelling for rational drug design
Wang, Y., Xing, J., Xu, Y., Zhou, N., Peng, J., Xiong, Z., Liu, X., Luo, X., Luo, C., Chen, K., et al. (2015) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583515000190 · Cited by 367
Molecular obesity, potency and other addictions in drug discovery
Hann, M. M. (2011) · MedChemComm · doi:10.1039/c1md00017a · Cited by 363
Reactive compounds and in vitro false positives in HTS
Rishton, G. M. (1997) · Drug Discovery Today · doi:10.1016/s1359-6446(97)01083-0 · Cited by 351
PAINS in the Assay: Chemical Mechanisms of Assay Interference and Promiscuous Enzymatic Inhibition Observed during a Sulfhydryl-Scavenging HTS
Dahlin, J. L., Nissink, J. W. M., Strasser, J. M., Francis, S., Higgins, L., Zhou, H., Zhang, Z., & Walters, M. A. (2015) · Journal of Medicinal Chemistry · doi:10.1021/jm5019093 · Cited by 351
Rules for Identifying Potentially Reactive or Promiscuous Compounds
Bruns, R. F., & Watson, I. A. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm301008n · Cited by 338
How Beyond Rule of 5 Drugs and Clinical Candidates Bind to Their Targets
Doak, B. C., Zheng, J., Dobritzsch, D., & Kihlberg, J. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01286 · Cited by 335
Intramolecular hydrogen bonding to improve membrane permeability and absorption in beyond rule of five chemical space
Alex, A., Millan, D. S., Perez, M., Wakenhut, F., & Whitlock, G. A. (2011) · MedChemComm · doi:10.1039/c1md00093d · Cited by 329
Time-Related Differences in the Physical Property Profiles of Oral Drugs
Leeson, P. D., & Davis, A. M. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm049717d · Cited by 326
Elucidating mechanisms of drug-induced toxicity
Liebler, D. C., & Guengerich, F. P. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1720 · Cited by 323
Oncogenic protein interfaces: small molecules, big challenges
Nero, T. L., Morton, C. J., Holien, J. K., Wielens, J., & Parker, M. W. (2014) · Nature Reviews Cancer · doi:10.1038/nrc3690 · Cited by 312
Predicting druggable binding sites at the protein–protein interface
Fuller, J. C., Burgoyne, N. J., & Jackson, R. M. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2008.10.009 · Cited by 310
Toward in silico structure-based ADMET prediction in drug discovery
Moroy, G., Martiny, V. Y., Vayer, P., Villoutreix, B. O., & Miteva, M. A. (2012) · Drug Discovery Today · doi:10.1016/j.drudis.2011.10.023 · Cited by 302
Self-citation
Introduction to in vitro estimation of metabolic stability and drug interactions of new chemical entities in drug discovery and development.
Baranczewski, P., Stańczak, A., Sundberg, K., Svensson, R., Wallin, A., Jansson, J., Garberg, P., & Postlind, H. (2006) · PubMed · Cited by 292
Combined Application of Parallel Artificial Membrane Permeability Assay and Caco-2 Permeability Assays in Drug Discovery
Kerns, E. H., Di, L., Petusky, S., Farris, M., Ley, R., & Jupp, P. (2004) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.20075 · Cited by 275
FAF-Drugs3: a web server for compound property calculation and chemical library design
Lagorce, D., Sperandio, O., Baell, J. B., Miteva, M. A., & Villoutreix, B. O. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv353 · Cited by 272
Self-citation
Predictive in silico modeling for hERG channel blockers
Aronov, A. M. (2005) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03278-7 · Cited by 267
Getting physical in drug discovery: a contemporary perspective on solubility and hydrophobicity
Hill, A. P., & Young, R. J. (2010) · Drug Discovery Today · doi:10.1016/j.drudis.2010.05.016 · Cited by 258
Hepatotoxicity by drugs: The most common implicated agents
Björnsson, E. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17020224 · Cited by 254
Development of a Virtual Screening Method for Identification of “Frequent Hitters” in Compound Libraries
Roche, O., Schneider, P., Zuegge, J., Guba, W., Kansy, M., Alanine, A., Bleicher, K., Danel, F., Gutknecht, E.-M., Rogers-Evans, M., et al. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm010934d · Cited by 248
Structural Biology and Drug Discovery of Difficult Targets: The Limits of Ligandability
Surade, S., & Blundell, T. L. (2012) · Chemistry & Biology · doi:10.1016/j.chembiol.2011.12.013 · Cited by 230
Drug-induced phospholipidosis: issues and future directions
Reasor, M. J., Hastings, K. L., & Ulrich, R. G. (2006) · Expert Opinion on Drug Safety · doi:10.1517/14740338.5.4.567 · Cited by 225
Chemical and structural lessons from recent successes in protein–protein interaction inhibition (2P2I)
Morelli, X., Bourgeas, R., & Roche, P. (2011) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2011.05.024 · Cited by 223
Predicting Drug-Induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches
Low, Y., Uehara, T., Minowa, Y., Yamada, H., Ohno, Y., Urushidani, T., Sedykh, A., Muratov, E., Kuz’min, V., Fourches, D., et al. (2011) · Chemical Research in Toxicology · doi:10.1021/tx200148a · Cited by 222
Structure alerts for carcinogenicity, and the Salmonella assay system: A novel insight through the chemical relational databases technology
BENIGNI, R., & BOSSA, C. (2008) · Mutation Research/Reviews in Mutation Research · doi:10.1016/j.mrrev.2008.05.003 · Cited by 218
Rationalizing the chemical space of protein–protein interaction inhibitors
Sperandio, O., Reynès, C. H., Camproux, A.-C., & Villoutreix, B. O. (2010) · Drug Discovery Today · doi:10.1016/j.drudis.2009.11.007 · Cited by 202
Self-citation
Classification of Cytochrome P450 Inhibitors and Noninhibitors Using Combined Classifiers
Cheng, F., Yu, Y., Shen, J., Yang, L., Li, W., Liu, G., Lee, P. W., & Tang, Y. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200028n · Cited by 196
In-Silico ADME Models: A General Assessment of their Utility in Drug Discovery Applications
Paul Gleeson, M., Hersey, A., & Hannongbua, S. (2011) · Current Topics in Medicinal Chemistry · doi:10.2174/156802611794480927 · Cited by 196
The Physicochemical Challenges of Designing Multiple Ligands
Morphy, R., & Rankovic, Z. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0603015 · Cited by 194
Toxicology Strategies for Drug Discovery: Present and Future
Blomme, E. A. G., & Will, Y. (2016) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.5b00407 · Cited by 194
Observations on Screening-Based Research and Some Concerning Trends in the Literature
Baell, J. B. (2010) · Future Medicinal Chemistry · doi:10.4155/fmc.10.237 · Cited by 179
Molecular inflation, attrition and the rule of five
Leeson, P. D. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.01.018 · Cited by 179
New Technologies and Screening Strategies for Hepatotoxicity: Use of In Vitro Models
Dambach, D. M., Andrews, B. A., & Moulin, F. (2005) · Toxicologic Pathology · doi:10.1080/01926230590522284 · Cited by 177
Expanding the Number of ‘Druggable’ Targets: Non‐Enzymes and Protein–Protein Interactions
Makley, L. N., & Gestwicki, J. E. (2013) · Chemical Biology & Drug Design · doi:10.1111/cbdd.12066 · Cited by 176
Cheminformatics Analysis of Assertions Mined from Literature That Describe Drug-Induced Liver Injury in Different Species
Fourches, D., Barnes, J. C., Day, N. C., Bradley, P., Reed, J. Z., & Tropsha, A. (2010) · Chemical Research in Toxicology · doi:10.1021/tx900326k · Cited by 162
Drug Design Targeting Protein–Protein Interactions (PPIs) Using Multiple Ligand Simultaneous Docking (MLSD) and Drug Repositioning: Discovery of Raloxifene and Bazedoxifene as Novel Inhibitors of IL-6/GP130 Interface
Li, H., Xiao, H., Lin, L., Jou, D., Kumari, V., Lin, J., & Li, C. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm401144z · Cited by 161
e-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design
Pihan, É., Colliandre, L., Guichou, J., & Douguet, D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts186 · Cited by 151
Self-citation
Computational models for predicting substrates or inhibitors of P-glycoprotein
Chen, L., Li, Y., Yu, H., Zhang, L., & Hou, T. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.11.003 · Cited by 141
Applications of Human Pharmacokinetic Prediction in First-in-Human Dose Estimation
Zou, P., Yu, Y., Zheng, N., Yang, Y., Paholak, H. J., Yu, L. X., & Sun, D. (2012) · The AAPS Journal · doi:10.1208/s12248-012-9332-y · Cited by 138
Pan Assay Interference Compounds (PAINS) and Other Promiscuous Compounds in Antifungal Research
Pouliot, M., & Jeanmart, S. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00361 · Cited by 132
Protein–protein interactions as druggable targets: recent technological advances
Higueruelo, A. P., Jubb, H. C., & Blundell, T. L. (2013) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2013.05.009 · Cited by 131
Assessment of the blood–brain barrier in CNS drug discovery
Jeffrey, P., & Summerfield, S. G. (2009) · Neurobiology of Disease · doi:10.1016/j.nbd.2009.07.033 · Cited by 130
Drug‐Like ProteinProtein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology
Villoutreix, B. O., Kuenemann, M. A., Poyet, J., Bruzzoni‐Giovanelli, H., Labbé, C. M., Lagorce, D., Spérandio, O., & Miteva, M. A. (2014) · Molecular Informatics · doi:10.1002/minf.201400040 · Cited by 119
Self-citation
iPPI-DB: a manually curated and interactive database of small non-peptide inhibitors of protein–protein interactions
Labbé, C. M., Laconde, G., Kuenemann, M. A., Villoutreix, B. O., & Sperandio, O. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.05.003 · Cited by 104
Self-citation
ASD v2.0: updated content and novel features focusing on allosteric regulation
Huang, Z., Mou, L., Shen, Q., Lu, S., Li, C., Liu, X., Wang, G., Li, S., Geng, L., Liu, Y., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1247 · Cited by 102
Filtering promiscuous compounds in early drug discovery: is it a good idea?
Senger, M. R., Fraga, C. A. M., Dantas, R. F., & Silva, F. P. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.02.004 · Cited by 95
Towards a detailed atlas of protein–protein interactions
Mosca, R., Pons, T., Ceol, A. G. M., Valencia, A., & Aloy, P. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.07.005 · Cited by 93
TIMBAL v2: update of a database holding small molecules modulating protein–protein interactions
Higueruelo, A. P., Jubb, H., & Blundell, T. L. (2013) · Database · doi:10.1093/database/bat039 · Cited by 93
Chemical, Target, and Bioactive Properties of Allosteric Modulation
van Westen, G. J. P., Gaulton, A., & Overington, J. P. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003559 · Cited by 90
How drug-like are ‘ugly’ drugs: do drug-likeness metrics predict ADME behaviour in humans?
Ritchie, T. J., & Macdonald, S. J. F. (2014) · Drug Discovery Today · doi:10.1016/j.drudis.2014.01.007 · Cited by 88
Growing PAINS in Academic Drug Discovery
Whitty, A. (2011) · Future Medicinal Chemistry · doi:10.4155/fmc.11.44 · Cited by 88
Why and how have drug discovery strategies in pharma changed? What are the new mindsets?
Mignani, S. M., Huber, S., Tomás, H., Rodrigues, J., & Majoral, J. (2015) · Drug Discovery Today · doi:10.1016/j.drudis.2015.09.007 · Cited by 85
How to Triage PAINS-Full Research
Dahlin, J. L., & Walters, M. A. (2015) · Assay and Drug Development Technologies · doi:10.1089/adt.2015.674 · Cited by 85
2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein–protein interactions
Basse, M.-J., Betzi, S., Morelli, X., & Roche, P. (2016) · Database · doi:10.1093/database/baw007 · Cited by 83
A BH3 Mimetic for Killing Cancer Cells
Green, D. R. (2016) · Cell · doi:10.1016/j.cell.2016.05.080 · Cited by 79
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See all 266

Authors: Lagorce, D., Douguet, D., Miteva, M. A., & Villoutreix, B. O.
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2017-04-11
Volume: 7
Issue: 1
Pages: 46277-46277
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/srep46277
OpenAlex: W2607333556
Cited by: 266
References: 131
FWCI: 8.77
Type: journal article
Language: en
Open access: gold

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Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors

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