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Gadiya Y, Shetty S, Hofmann-Apitius M, Gribbon P, Zaliani A. Exploring SureChEMBL from a drug discovery perspective. Sci Data 2024; 11:507. [PMID: 38755219 PMCID: PMC11099139 DOI: 10.1038/s41597-024-03371-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024] Open
Abstract
In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.
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Affiliation(s)
- Yojana Gadiya
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525, Hamburg, Germany.
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590, Frankfurt, Germany.
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113, Bonn, Germany.
| | - Simran Shetty
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590, Frankfurt, Germany
- Hamburg University of Applied Sciences (HAW), 20099, Hamburg, Germany
| | - Martin Hofmann-Apitius
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113, Bonn, Germany
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
| | - Philip Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590, Frankfurt, Germany
| | - Andrea Zaliani
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590, Frankfurt, Germany
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Martinez-Sevillano M, Falaguera MJ, Mestres J. CIPSI: An open chemical intellectual property service for medicinal chemists. Mol Inform 2024; 43:e202300221. [PMID: 38010631 DOI: 10.1002/minf.202300221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 11/29/2023]
Abstract
The availability of patent chemical data offers public access to a chemical space that is not well covered by other sources collecting small molecules from scholarly literature. However, open applications to facilitate the search and analysis of biologically-relevant molecular structures present in patents are still largely missing. We have developed CIPSI, an open Chemical Intellectual Property Service @ IMIM to assist medicinal chemists in searching and analysing molecules in SureChEMBL patents. The current version contains 6,240,500 molecules from 236,689 pharmacological patents, of which 5,949,214 are confidently assigned to core chemical structures reminiscent of the Markush structure in the patent claim. The platform includes some graphical tools to facilitate comparative patent analyses between drugs, chemical substructures, and company assignees. CIPSI is available at https://cipsi.org.
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Affiliation(s)
- Maria Martinez-Sevillano
- Systems Pharmacology, Research Group on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute, Doctor Aiguader 88, 08028, Barcelona, Spain
| | - Maria J Falaguera
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Jordi Mestres
- Systems Pharmacology, Research Group on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute, Doctor Aiguader 88, 08028, Barcelona, Spain
- Institut de Quimica Computacional i Catalisi, Facultat de Ciencies, Universitat de Girona, Maria Aurelia Capmany 69, 17003, Girona, Spain
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Bolcato G, Heid E, Boström J. On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods. J Chem Inf Model 2022; 62:1388-1398. [PMID: 35271260 PMCID: PMC8965872 DOI: 10.1021/acs.jcim.1c01535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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Multiparameter optimization,
the heart of drug design, is still
an open challenge. Thus, improved methods for automated compound design
with multiple controlled properties are desired. Here, we present
a significant extension to our previously described fragment-based
reinforcement learning method (DeepFMPO) for the generation of novel
molecules with optimal properties. As before, the generative process
outputs optimized molecules similar to the input structures, now with
the improved feature of replacing parts of these molecules with fragments
of similar three-dimensional (3D) shape and electrostatics. We developed
and benchmarked a new python package, ESP-Sim, for the comparison
of the electrostatic potential and the molecular shape, allowing the
calculation of high-quality partial charges (e.g., RESP with B3LYP/6-31G**)
obtained using the quantum chemistry program Psi4. By performing comparisons
of 3D fragments, we can simulate 3D properties while overcoming the
notoriously difficult step of accurately describing bioactive conformations.
The new improved generative (DeepFMPO v3D) method is demonstrated
with a scaffold-hopping exercise identifying CDK2 bioisosteres. The
code is open-source and freely available.
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Affiliation(s)
- Giovanni Bolcato
- Molecular Modeling Section, University of Padova, 35131 Padova, Italy
| | - Esther Heid
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, 02139 Massachusetts, United States
| | - Jonas Boström
- Medicinal Chemistry, Early CVRM, BioPharmaceuticals R&D, AstraZeneca, 431 50 Mölndal, Sweden
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