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Perebyinis M, Rognan D. Overlap of On-demand Ultra-large Combinatorial Spaces with On-the-shelf Drug-like Libraries. Mol Inform 2023; 42:e2200163. [PMID: 36072995 DOI: 10.1002/minf.202200163] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/07/2022] [Indexed: 01/12/2023]
Abstract
On-demand combinatorial spaces are shifting paradigms in early drug discovery, by considerably increasing the searchable chemical space to several billions of compounds while securing their synthetic accessibility. We here systematically compared the on-the-shelf available drug-like chemical space (9 million compounds) to three on-demand ultra-large (ODUL) combinatorial fragment spaces (REAL, CHEMriya, GalaXi) covering 32 billion of readily accessible molecules. Surprisingly, only one space (REAL) intersects almost entirely the currently available drug-like space, suggesting that it is the only ODUL widely suitable for in-stock hit expansion. Of course, expanding a preliminary ODUL hit in the same chemical space is the best possible strategy to rapidly generate structure-activity relationships. All three spaces remain well suited to early hit finding initiatives since they all provide numerous unique scaffolds that are not described by on-the shelf collections.
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Affiliation(s)
- Mariana Perebyinis
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 74 route du Rhin, F-67400, Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 74 route du Rhin, F-67400, Illkirch, France
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2
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Unsupervised Representation Learning for Proteochemometric Modeling. Int J Mol Sci 2021; 22:ijms222312882. [PMID: 34884688 PMCID: PMC8657702 DOI: 10.3390/ijms222312882] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 11/18/2022] Open
Abstract
In silico protein–ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection and prioritization of possible drug candidates. Proteochemometric modeling (PCM) attempts to create an accurate model of the protein–ligand interaction space by combining explicit protein and ligand descriptors. This requires the creation of information-rich, uniform and computer interpretable representations of proteins and ligands. Previous studies in PCM modeling rely on pre-defined, handcrafted feature extraction methods, and many methods use protein descriptors that require alignment or are otherwise specific to a particular group of related proteins. However, recent advances in representation learning have shown that unsupervised machine learning can be used to generate embeddings that outperform complex, human-engineered representations. Several different embedding methods for proteins and molecules have been developed based on various language-modeling methods. Here, we demonstrate the utility of these unsupervised representations and compare three protein embeddings and two compound embeddings in a fair manner. We evaluate performance on various splits of a benchmark dataset, as well as on an internal dataset of protein–ligand binding activities and find that unsupervised-learned representations significantly outperform handcrafted representations.
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Uba AI, Radicella C, Readmond C, Scorese N, Liao S, Liu H, Wu C. Binding of agonist WAY-267,464 and antagonist WAY-methylated to oxytocin receptor probed by all-atom molecular dynamics simulations. Life Sci 2020; 252:117643. [DOI: 10.1016/j.lfs.2020.117643] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 03/25/2020] [Accepted: 04/03/2020] [Indexed: 01/07/2023]
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4
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Frantz MC, Pellissier LP, Pflimlin E, Loison S, Gandía J, Marsol C, Durroux T, Mouillac B, Becker JAJ, Le Merrer J, Valencia C, Villa P, Bonnet D, Hibert M. LIT-001, the First Nonpeptide Oxytocin Receptor Agonist that Improves Social Interaction in a Mouse Model of Autism. J Med Chem 2018; 61:8670-8692. [DOI: 10.1021/acs.jmedchem.8b00697] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Marie-Céline Frantz
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg, 74 Route du Rhin, F-67412 Illkirch, France
| | - Lucie P. Pellissier
- Physiologie de la Reproduction et des Comportements, INRA UMR-0085, CNRS UMR-7247, IFCE, Inserm, Université François Rabelais de Tours, F-37380 Nouzilly, France
| | - Elsa Pflimlin
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg, 74 Route du Rhin, F-67412 Illkirch, France
| | - Stéphanie Loison
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg, 74 Route du Rhin, F-67412 Illkirch, France
| | - Jorge Gandía
- Physiologie de la Reproduction et des Comportements, INRA UMR-0085, CNRS UMR-7247, IFCE, Inserm, Université François Rabelais de Tours, F-37380 Nouzilly, France
| | - Claire Marsol
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg, 74 Route du Rhin, F-67412 Illkirch, France
- LabEx MEDALIS, Université de Strasbourg, F-67000 Strasbourg, France
- PCBIS Plateforme de Chimie Biologique Intégrative de Strasbourg, UMS3286, CNRS/Université de Strasbourg, F-67000 Strasbourg, France
| | - Thierry Durroux
- Institut de Génomique Fonctionnelle, CNRS UMR5203, INSERM U661, Université de Montpellier (IFR3), 141 Rue de la Cardonille, F-34094 Montpellier Cedex 5, France
| | - Bernard Mouillac
- Institut de Génomique Fonctionnelle, CNRS UMR5203, INSERM U661, Université de Montpellier (IFR3), 141 Rue de la Cardonille, F-34094 Montpellier Cedex 5, France
| | - Jérôme A. J. Becker
- Physiologie de la Reproduction et des Comportements, INRA UMR-0085, CNRS UMR-7247, IFCE, Inserm, Université François Rabelais de Tours, F-37380 Nouzilly, France
| | - Julie Le Merrer
- Physiologie de la Reproduction et des Comportements, INRA UMR-0085, CNRS UMR-7247, IFCE, Inserm, Université François Rabelais de Tours, F-37380 Nouzilly, France
| | - Christel Valencia
- LabEx MEDALIS, Université de Strasbourg, F-67000 Strasbourg, France
- PCBIS Plateforme de Chimie Biologique Intégrative de Strasbourg, UMS3286, CNRS/Université de Strasbourg, F-67000 Strasbourg, France
| | - Pascal Villa
- LabEx MEDALIS, Université de Strasbourg, F-67000 Strasbourg, France
- PCBIS Plateforme de Chimie Biologique Intégrative de Strasbourg, UMS3286, CNRS/Université de Strasbourg, F-67000 Strasbourg, France
| | - Dominique Bonnet
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg, 74 Route du Rhin, F-67412 Illkirch, France
- LabEx MEDALIS, Université de Strasbourg, F-67000 Strasbourg, France
| | - Marcel Hibert
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg, 74 Route du Rhin, F-67412 Illkirch, France
- LabEx MEDALIS, Université de Strasbourg, F-67000 Strasbourg, France
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HajiEbrahimi A, Ghafouri H, Ranjbar M, Sakhteman A. Protein Ligand Interaction Fingerprints. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.
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Cortés-Ciriano I, Ain QU, Subramanian V, Lenselink EB, Méndez-Lucio O, IJzerman AP, Wohlfahrt G, Prusis P, Malliavin TE, van Westen GJP, Bender A. Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects. MEDCHEMCOMM 2015. [DOI: 10.1039/c4md00216d] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.
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Affiliation(s)
- Isidro Cortés-Ciriano
- Unité de Bioinformatique Structurale
- Institut Pasteur and CNRS UMR 3825
- Structural Biology and Chemistry Department
- 75 724 Paris
- France
| | - Qurrat Ul Ain
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
| | | | - Eelke B. Lenselink
- Division of Medicinal Chemistry
- Leiden Academic Centre for Drug Research
- Leiden
- The Netherlands
| | - Oscar Méndez-Lucio
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
| | - Adriaan P. IJzerman
- Division of Medicinal Chemistry
- Leiden Academic Centre for Drug Research
- Leiden
- The Netherlands
| | - Gerd Wohlfahrt
- Computer-Aided Drug Design
- Orion Pharma
- FIN-02101 Espoo
- Finland
| | - Peteris Prusis
- Computer-Aided Drug Design
- Orion Pharma
- FIN-02101 Espoo
- Finland
| | - Thérèse E. Malliavin
- Unité de Bioinformatique Structurale
- Institut Pasteur and CNRS UMR 3825
- Structural Biology and Chemistry Department
- 75 724 Paris
- France
| | - Gerard J. P. van Westen
- European Molecular Biology Laboratory
- European Bioinformatics Institute
- Wellcome Trust Genome Campus
- Hinxton
- UK
| | - Andreas Bender
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
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Karpenko IA, Kreder R, Valencia C, Villa P, Mendre C, Mouillac B, Mély Y, Hibert M, Bonnet D, Klymchenko AS. Red Fluorescent Turn-On Ligands for Imaging and Quantifying G Protein-Coupled Receptors in Living Cells. Chembiochem 2014; 15:359-63. [DOI: 10.1002/cbic.201300738] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2013] [Indexed: 12/26/2022]
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Desaphy J, Raimbaud E, Ducrot P, Rognan D. Encoding protein-ligand interaction patterns in fingerprints and graphs. J Chem Inf Model 2013; 53:623-37. [PMID: 23432543 DOI: 10.1021/ci300566n] [Citation(s) in RCA: 109] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
We herewith present a novel and universal method to convert protein-ligand coordinates into a simple fingerprint of 210 integers registering the corresponding molecular interaction pattern. Each interaction (hydrophobic, aromatic, hydrogen bond, ionic bond, metal complexation) is detected on the fly and physically described by a pseudoatom centered either on the interacting ligand atom, the interacting protein atom, or the geometric center of both interacting atoms. Counting all possible triplets of interaction pseudoatoms within six distance ranges, and pruning the full integer vector to keep the most frequent triplets enables the definition of a simple (210 integers) and coordinate frame-invariant interaction pattern descriptor (TIFP) that can be applied to compare any pair of protein-ligand complexes. TIFP fingerprints have been calculated for ca. 10,000 druggable protein-ligand complexes therefore enabling a wide comparison of relationships between interaction pattern similarity and ligand or binding site pairwise similarity. We notably show that interaction pattern similarity strongly depends on binding site similarity. In addition to the TIFP fingerprint which registers intermolecular interactions between a ligand and its target protein, we developed two tools (Ishape, Grim) to align protein-ligand complexes from their interaction patterns. Ishape is based on the overlap of interaction pseudoatoms using a smooth Gaussian function, whereas Grim utilizes a standard clique detection algorithm to match interaction pattern graphs. Both tools are complementary and enable protein-ligand complex alignments capitalizing on both global and local pattern similarities. The new fingerprint and companion alignment tools have been successfully used in three scenarios: (i) interaction-biased alignment of protein-ligand complexes, (ii) postprocessing docking poses according to known interaction patterns for a particular target, and (iii) virtual screening for bioisosteric scaffolds sharing similar interaction patterns.
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Affiliation(s)
- Jérémy Desaphy
- Laboratory for Therapeutical Innovation, UMR 7200 Université de Strabsourg/CNRS , MEDALIS Drug Discovery Center, F-67400 Illkirch, France
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