1
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Smith S, Cassada JB, Von Bredow L, Erreger K, Webb EM, Trombley TA, Kalbfleisch JJ, Bender BJ, Zagol-Ikapitte I, Kramlinger VM, Bouchard JL, Mitchell SG, Tretbar M, Shoichet BK, Lindsley CW, Meiler J, Hamm HE. Discovery of Protease-Activated Receptor 4 (PAR4)-Tethered Ligand Antagonists Using Ultralarge Virtual Screening. ACS Pharmacol Transl Sci 2024; 7:1086-1100. [PMID: 38633591 PMCID: PMC11020070 DOI: 10.1021/acsptsci.3c00378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 04/19/2024]
Abstract
Here, we demonstrate a structure-based small molecule virtual screening and lead optimization pipeline using a homology model of a difficult-to-drug G-protein-coupled receptor (GPCR) target. Protease-activated receptor 4 (PAR4) is activated by thrombin cleavage, revealing a tethered ligand that activates the receptor, making PAR4 a challenging target. A virtual screen of a make-on-demand chemical library yielded a one-hit compound. From the single-hit compound, we developed a novel series of PAR4 antagonists. Subsequent lead optimization via simultaneous virtual library searches and structure-based rational design efforts led to potent antagonists of thrombin-induced activation. Interestingly, this series of antagonists was active against PAR4 activation by the native protease thrombin cleavage but not the synthetic PAR4 agonist peptide AYPGKF.
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Affiliation(s)
- Shannon
T. Smith
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Jackson B. Cassada
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Lukas Von Bredow
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig 04109, Germany
| | - Kevin Erreger
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Emma M. Webb
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Trevor A. Trombley
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Jacob J. Kalbfleisch
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Brian J. Bender
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Irene Zagol-Ikapitte
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Valerie M. Kramlinger
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Jacob L. Bouchard
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Sidnee G. Mitchell
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Maik Tretbar
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig 04109, Germany
| | - Brian K. Shoichet
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Craig W. Lindsley
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Jens Meiler
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig 04109, Germany
| | - Heidi E. Hamm
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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2
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Biyuzan H, Masrour MA, Grandmougin L, Payan F, Douguet D. SENSAAS-Flex: a joint optimization approach for aligning 3D shapes and exploring the molecular conformation space. Bioinformatics 2024; 40:btae105. [PMID: 38383065 PMCID: PMC10918633 DOI: 10.1093/bioinformatics/btae105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/13/2024] [Accepted: 02/20/2024] [Indexed: 02/23/2024] Open
Abstract
MOTIVATION Popular shape-based alignment methods handle molecular flexibility by utilizing conformational ensembles to select the most fitted conformer. However, the initial conformer library generation step is computationally intensive and limiting to the overall alignment process. In this work, we describe a method to perform flexible alignment of two molecular shapes by optimizing the 3D conformation. SENSAAS-Flex, an add-on to the SENSAAS tool, is able to proceed from a limited set of initial conformers through an iterative process where additional conformational optimizations are made at the substructure level and constrained by the target shape. RESULTS In self- and cross-alignment experiments, SENSAAS-Flex is able to reproduce the crystal structure geometry of ligands of the AstraZeneca Molecule Overlay Test set and PDBbind refined dataset. Our study shows that the point-based representation of molecular surfaces is appropriate in terms of shape constraint to sample the conformational space and perform flexible molecular alignments. AVAILABILITY AND IMPLEMENTATION The documentation and source code are available at https://chemoinfo.ipmc.cnrs.fr/Sensaas-flex/sensaas-flex-main.tar.gz.
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Affiliation(s)
- Hamza Biyuzan
- Université Côte d’Azur, CNRS UMR7271, I3S, Sophia Antipolis 06900, France
| | | | - Lucas Grandmougin
- Université Côte d’Azur, CNRS UMR7271, I3S, Sophia Antipolis 06900, France
| | - Frédéric Payan
- Université Côte d’Azur, CNRS UMR7271, I3S, Sophia Antipolis 06900, France
| | - Dominique Douguet
- Université Côte d’Azur, Inserm U1323, CNRS UMR7275, IPMC, Valbonne 06560, France
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3
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Kim T, Chung KC, Park H. Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm. Pharmaceuticals (Basel) 2023; 16:1509. [PMID: 38004375 PMCID: PMC10675541 DOI: 10.3390/ph16111509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/14/2023] [Accepted: 10/20/2023] [Indexed: 11/26/2023] Open
Abstract
The hERG potassium channel serves as an annexed target for drug discovery because the associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative structure-activity relationship (QSAR) models were developed to predict inhibitory activities against the hERG potassium channel, utilizing the three-dimensional (3D) distribution of quantum mechanical electrostatic potential (ESP) as the molecular descriptor. To prepare the optimal atomic coordinates of dataset molecules, pairwise 3D structural alignments were carried out in order for the quantum mechanical cross correlation between the template and other molecules to be maximized. This alignment method stands out from the common atom-by-atom matching technique, as it can handle structurally diverse molecules as effectively as chemical derivatives that share an identical scaffold. The alignment problem prevalent in 3D-QSAR methods was ameliorated substantially by dividing the dataset molecules into seven subsets, each of which contained molecules with similar molecular weights. Using an artificial neural network algorithm to find the functional relationship between the quantum mechanical ESP descriptors and the experimental hERG inhibitory activities, highly predictive 3D-QSAR models were derived for all seven molecular subsets to the extent that the squared correlation coefficients exceeded 0.79. Given their simplicity in model development and strong predictability, the 3D-QSAR models developed in this study are expected to function as an effective virtual screening tool for assessing the potential cardiotoxicity of drug candidate molecules.
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Affiliation(s)
| | - Kee-Choo Chung
- Department of Bioscience and Biotechnology, Sejong University, 209 Neungdong-ro, Kwangjin-gu, Seoul 05006, Republic of Korea;
| | - Hwangseo Park
- Department of Bioscience and Biotechnology, Sejong University, 209 Neungdong-ro, Kwangjin-gu, Seoul 05006, Republic of Korea;
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4
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Hönig SMN, Lemmen C, Rarey M. Small molecule superposition: A comprehensive overview on pose scoring of the latest methods. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Sophia M. N. Hönig
- ZBH ‐ Center for Bioinformatics Universität Hamburg Hamburg Germany
- BioSolveIT Sankt Augustin Germany
| | | | - Matthias Rarey
- ZBH ‐ Center for Bioinformatics Universität Hamburg Hamburg Germany
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5
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Brown BP, Vu O, Geanes AR, Kothiwale S, Butkiewicz M, Lowe EW, Mueller R, Pape R, Mendenhall J, Meiler J. Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery. Front Pharmacol 2022; 13:833099. [PMID: 35264967 PMCID: PMC8899505 DOI: 10.3389/fphar.2022.833099] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 01/24/2022] [Indexed: 01/31/2023] Open
Abstract
The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.
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Affiliation(s)
- Benjamin P. Brown
- Chemical and Physical Biology Program, Medical Scientist Training Program, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- *Correspondence: Jens Meiler, ; Jeffrey Mendenhall, ; Benjamin P. Brown,
| | - Oanh Vu
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Alexander R. Geanes
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Sandeepkumar Kothiwale
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Mariusz Butkiewicz
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Edward W. Lowe
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Ralf Mueller
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Richard Pape
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Jeffrey Mendenhall
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- *Correspondence: Jens Meiler, ; Jeffrey Mendenhall, ; Benjamin P. Brown,
| | - Jens Meiler
- Department of Chemistry, Departments of Pharmacology and Biomedical Informatics, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Germany
- *Correspondence: Jens Meiler, ; Jeffrey Mendenhall, ; Benjamin P. Brown,
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6
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Kim T, You BH, Han S, Shin HC, Chung KC, Park H. Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage. Int J Mol Sci 2021; 22:ijms222010995. [PMID: 34681653 PMCID: PMC8537149 DOI: 10.3390/ijms222010995] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/07/2021] [Accepted: 10/10/2021] [Indexed: 01/07/2023] Open
Abstract
A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.
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Affiliation(s)
- Taeho Kim
- Department of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, Korea;
| | - Byoung Hoon You
- Whan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, Korea; (B.H.Y.); (S.H.); (H.C.S.)
| | - Songhee Han
- Whan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, Korea; (B.H.Y.); (S.H.); (H.C.S.)
| | - Ho Chul Shin
- Whan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, Korea; (B.H.Y.); (S.H.); (H.C.S.)
| | - Kee-Choo Chung
- Department of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, Korea;
- Correspondence: (K.-C.C.); (H.P.); Tel.: +82-2-2963-1635 (K.-C.C.); +82-2-3408-3766 (H.P.); Fax: +82-2-3408-4334 (K.-C.C. & H.P.)
| | - Hwangseo Park
- Department of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, Korea;
- Correspondence: (K.-C.C.); (H.P.); Tel.: +82-2-2963-1635 (K.-C.C.); +82-2-3408-3766 (H.P.); Fax: +82-2-3408-4334 (K.-C.C. & H.P.)
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7
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Cappel D, Jerome S, Hessler G, Matter H. Impact of Different Automated Binding Pose Generation Approaches on Relative Binding Free Energy Simulations. J Chem Inf Model 2020; 60:1432-1444. [DOI: 10.1021/acs.jcim.9b01118] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
| | - Steven Jerome
- Schrödinger Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Gerhard Hessler
- Integrated Drug Discovery (IDD), Synthetic Molecular Design, Sanofi-Aventis Deutschland GmbH, Building G838, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Hans Matter
- Integrated Drug Discovery (IDD), Synthetic Molecular Design, Sanofi-Aventis Deutschland GmbH, Building G838, Industriepark Höchst, 65926 Frankfurt am Main, Germany
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8
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Choi H, Kang H, Chung KC, Park H. Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties. Phys Chem Chem Phys 2019; 21:5189-5199. [PMID: 30775759 DOI: 10.1039/c8cp07002d] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
We establish a comprehensive quantitative structure-activity relationship (QSAR) model termed AlphaQ through the machine learning algorithm to associate the fully quantum mechanical molecular descriptors with various biochemical and pharmacological properties. Preliminarily, a novel method for molecular structural alignments was developed in such a way to maximize the quantum mechanical cross correlations among the molecules. Besides the improvement of structural alignments, three-dimensional (3D) distribution of the molecular electrostatic potential was introduced as the unique numerical descriptor for individual molecules. These dual modifications lead to a substantial accuracy enhancement in multifarious 3D-QSAR prediction models of AlphaQ. Most remarkably, AlphaQ has been proven to be applicable to structurally diverse molecules to the extent that it outperforms the conventional QSAR methods in estimating the inhibitory activity against thrombin, the water-cyclohexane distribution coefficient, the permeability across the membrane of the Caco-2 cell, and the metabolic stability in human liver microsomes. Due to the simplicity in model building and the high predictive capability for varying biochemical and pharmacological properties, AlphaQ is anticipated to serve as a valuable screening tool at both early and late stages of drug discovery.
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Affiliation(s)
- Hwanho Choi
- Department of Bioscience and Biotechnology, Sejong University, 209 Neungdong-ro, Kwangjin-gu, Seoul 05006, Korea.
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9
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Brown BP, Mendenhall J, Meiler J. BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping. J Chem Inf Model 2019; 59:689-701. [PMID: 30707580 DOI: 10.1021/acs.jcim.9b00020] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Small molecule flexible alignment is a critical component of both ligand- and structure-based methods in computer-aided drug discovery. Despite its importance, the availability of high-quality flexible alignment software packages is limited. Here, we present BCL::MolAlign, a freely available property-based molecular alignment program. BCL::MolAlign accommodates ligand flexibility through a combination of pregenerated conformers and on-the-fly bond rotation. BCL::MolAlign converges on alignment poses by sampling the relative orientations of mutually matching atom pairs between molecules through Monte Carlo Metropolis sampling. Across six diverse ligand data sets, BCL::MolAlign flexible alignment outperforms MOE, ROCS, and FLEXS in recovering native ligand binding poses. Moreover, the BCL::MolAlign alignment score is more predictive of ligand activity than maximum common substructure similarity across 10 data sets. Finally, on a recently published benchmark set of 20 high quality congeneric ligand-protein complexes, BCL::MolAlign is able to recover a larger fraction of native binding poses than maximum common substructure-based alignment and RosettaLigand. BCL::MolAlign can be obtained as part of the Biology and Chemistry Library (BCL) software package freely with an academic license or can be accessed via Web server at http://meilerlab.org/index.php/servers/molalign .
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Affiliation(s)
- Benjamin P Brown
- Chemical and Physical Biology Program, Medical Scientist Training Program, Center for Structural Biology , Vanderbilt University , Nashville , Tennessee 37232 , United States
| | - Jeffrey Mendenhall
- Department of Chemistry, Center for Structural Biology , Vanderbilt University , Nashville , Tennessee 37232 , United States
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology , Vanderbilt University , Nashville , Tennessee 37232 , United States.,Departments of Pharmacology and Biomedical Informatics , Vanderbilt University , Nashville , Tennessee 37212 , United States
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10
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Vázquez J, Deplano A, Herrero A, Ginex T, Gibert E, Rabal O, Oyarzabal J, Herrero E, Luque FJ. Development and Validation of Molecular Overlays Derived from Three-Dimensional Hydrophobic Similarity with PharmScreen. J Chem Inf Model 2018; 58:1596-1609. [PMID: 30010337 DOI: 10.1021/acs.jcim.8b00216] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Molecular alignment is a standard procedure for three-dimensional (3D) similarity measurements and pharmacophore elucidation. This process is influenced by several factors, such as the physicochemical descriptors utilized to account for the molecular determinants of biological activity and the reference templates. Relying on the hypothesis that the maximal achievable binding affinity for a drug-like molecule is largely due to desolvation, we explore a novel strategy for 3D molecular overlays that exploits the partitioning of molecular hydrophobicity into atomic contributions in conjunction with information about the distribution of hydrogen-bond (HB) donor/acceptor groups. A brief description of the method, as implemented in the software package PharmScreen, including the derivation of the fractional hydrophobic contributions within the quantum mechanical version of the Miertus-Scrocco-Tomasi (MST) continuum model, and the procedure utilized for the optimal superposition between molecules, is presented. The computational procedure is calibrated by using a data set of 402 molecules pertaining to 14 distinct targets taken from the literature and validated against the AstraZeneca test, which comprises 121 experimentally derived sets of molecular overlays. The results point out the suitability of the MST-based hydrophobic parameters for generating molecular overlays, as correct predictions were obtained for 94%, 79%, and 54% of the molecules classified into easy, moderate, and hard sets, respectively. Moreover, the results point out that this accuracy is attained at a much lower degree of identity between the templates used by hydrophobic/HB fields and electrostatic/steric ones. These findings support the usefulness of the hydrophobic/HB descriptors to generate complementary overlays that may be valuable to rationalize structure-activity relationships and for virtual screening campaigns.
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Affiliation(s)
- Javier Vázquez
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain.,Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB) , University of Barcelona , Av. Prat de la Riba 171 , Santa Coloma de Gramenet E-08921 , Spain
| | - Alessandro Deplano
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - Albert Herrero
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - Tiziana Ginex
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB) , University of Barcelona , Av. Prat de la Riba 171 , Santa Coloma de Gramenet E-08921 , Spain
| | - Enric Gibert
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA) , University of Navarra , Avda. Pio XII 55 , Pamplona E-31008 , Spain
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA) , University of Navarra , Avda. Pio XII 55 , Pamplona E-31008 , Spain
| | - Enric Herrero
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - F Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB) , University of Barcelona , Av. Prat de la Riba 171 , Santa Coloma de Gramenet E-08921 , Spain
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