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Computational drug repurposing: approaches, evaluation of in silico resources and case studies

NATURE REVIEWS DRUG DISCOVERY 2025年 1-22页

作者： Tanoli, Ziaurrehman Fernandez-Torras, Adria Ozcan, Umut Onur Kushnir, Aleksandr Nader, Kristen Michelle Gadiya, Yojana Fiorenza, Laura Ianevski, Aleksandr Vaha-Koskela, Markus Miihkinen, Mitro Seemab, Umair Leinonen, Henri Seashore-Ludlow, Brinton Tampere, Marianna Kalman, Adelinn Ballante, Flavio Benfenati, Emilio Saunders, Gary Potdar, Swapnil Garcia, Ismael Gomez Garcia-Serna, Ricard Talarico, Carmine Beccari, Andrea Rosario Schaal, Wesley Polo, Andrea Costantini, Susan Cabri, Enrico Jacobs, Marc Saarela, Jani Budillon, Alfredo Spjuth, Ola Ostling, Paivi Xhaard, Henri Quintana, Jordi Mestres, Jordi Gribbon, Philip Ussi, Anton E. Lo, Donald C. de Kort, Martin Wennerberg, Krister Fratelli, Maddalena Carreras-Puigvert, Jordi Aittokallio, Tero Univ Helsinki Inst Mol Med Finland FIMM HiLIFE Helsinki Finland Univ Helsinki ICAN Digital Precis Canc Med Flagship Helsinki Finland Helsinki Univ Hosp Helsinki Finland Bioctr Finland Drug Discovery & Chem Biol DDCB Consortium Helsinki Finland Chemotargets SL Parc Cientif Barcelona Barcelona 08028 Catalonia Spain Fraunhofer Inst Translat Med & Pharmacol ITMP D-22525 Hamburg Germany Fraunhofer Frankfurt Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT Bonn Germany Politecn Milan Dipartimento Elettron Informaz & Bioingn DEIB I-20133 Milan Italy IRCCS Ist Ric Farmacolog Mario Negri Milan Italy Univ Eastern Finland Fac Hlth Sci Sch Pharm Kuopio Finland Karolinska Inst Dept Oncol Pathol Sci Life Lab SciLifeLab Stockholm Sweden Karolinska Inst Chem Biol Consortium Sweden CBCS Dept Med Biochem & Biophys Stockholm Sweden European Infrastruct Translat Med EATRIS ERIC NL-1081 HZ Amsterdam Netherlands Dompe Farmaceut SpA EXSCALATE Naples Italy Uppsala Univ Dept Pharmaceut Biosci Uppsala Sweden Uppsala Univ Sci Life Lab Uppsala Sweden IRCCS Fdn G Pascale Ist Nazl Tumori Naples Italy Fraunhofer Inst Algorithms & Sci Comp SCAI D-53754 St Augustin Augustin Germany Univ Helsinki Fac Pharm Helsinki Finland Univ Girona Inst Quim Computac & Catalisi Girona 17071 Catalonia Spain Univ Copenhagen Biotech Res & Innovat Ctr Copenhagen Denmark Oslo Univ Hosp Inst Canc Res Dept Canc Genet Oslo Norway Univ Oslo Fac Med Oslo Ctr Biostat & Epidemiol OCBE Oslo Norway

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

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The recovery of ridge functions on the hypercube suffers from the curse of dimensionality

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JOURNAL OF COMPLEXITY 2021年 63卷 101521-101521页

作者： Doerr, Benjamin Mayer, Sebastian Inst Polytech Paris Lab Informat LIX Ecole Polytech CNRS Palaiseau France Fraunhofer Ctr Machine Learning D-53754 St Augustin Germany Fraunhofer Inst Algorithms & Sci Comp SCAI D-53754 St Augustin Germany

A multivariate ridge function is a function of the form f (x) = g(a(T) x), where g is univariate and a is an element of R-d. We show that the recovery of an unknown ridge function defined on the hypercube [-1, 1](d) with Lipschitz-regular profile g suffers from the curse of dimensionality when the recovery error is measured in the L-infinity-norm, even if we allow randomized algorithms. If a limited number of components of a is substantially larger than the others, then the curse of dimensionality is not present and the problem is weakly tractable, provided the profile g is sufficiently regular. (C) 2020 Elsevier Inc. All rights reserved.

关键词： High-dimensional function approximation Ridge functions Randomized algorithms Lower bounds Information-based complexity

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Interatomic-Potential-Free, Data-Driven Molecular Dynamics

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023年 415卷

作者： Bulin, J. Hamaekers, J. Ariza, M. P. Ortiz, M. Fraunhofer Inst Algorithms & Sci Comp Schloss Birlinghoven D-53757 St Augustin Germany Univ Seville Escuela Tecn Super Ingn Seville 41092 Spain Univ Bonn Hausdorff Ctr Math Endenicher Allee 60 D-53115 Bonn Germany CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

We present a Data-Driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e. g., from ab initio calculations, thereby eschewing the conventional step of modeling the data by empirical interatomic potentials entirely. The data required by the DD solvers consists of local atomic configurations and corresponding atomic forces and is, therefore, fundamental, i. e., it is not beholden to any particular model. The resulting DD solvers, including a fully explicit DD-Verlet algorithm, are provably convergent and exhibit robust convergence with respect to the data in selected test cases. We present an example of application to C60 buckminsterfullerenes that showcases the feasibility, range and scope of the DD molecular dynamics paradigm.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Data-Driven computing Molecular dynamics Optimal control Game theory Wasserstein metric

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Generalization of algebraic multiscale to algebraic multigrid

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COMPUTATIONAL GEOSCIENCES 2020年第2期24卷 683-696页

作者： Ehrmann, Silvia Gries, Sebastian Schweitzer, Marc Alexander Fraunhofer Inst Algorithms & Sci Comp SCAI Schloss Birlinghoven D-53757 St Augustin Germany Rhein Friedrich Wilhelms Univ Inst Numer Simulat Endenicher Allee 19b D-53115 Bonn Germany

Algebraic multiscale (AMS) is a recent development for the construction of efficient linear solvers in reservoir simulations. It employs upscaling ideas to coarsen the respective linear system and provides a high amount of inherent parallelism. However, it has the drawback that it can so far only be applied to scalar problems, e.g., a pressure sub-problem. Moreover, AMS relies on the availability of information on the physics and the grid to construct a two-level scheme. Generalizing the AMS approach to overcome these limitations requires substantial efforts and is not straightforward. To exploit the benefits of AMS, however, we integrate its core ideas in an algebraic multigrid (AMG) method. Thus, all results and techniques from the well-established AMG are directly available in conjunction with (core ingredients of) AMS. This holds regarding multilevel usage as well as the independence of geometric and physical information. But it also holds for the System-AMG approach that allows us to consider additional thermal and mechanical unknowns.

关键词： Linear solver Algebraic multigrid Multiscale methods Algebraic multiscale System-AMG Iterative solver Reservoir simulation Geomechanics Big data

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Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk

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DATA 2024年第2期9卷 25页

作者： Khatami, Sepehr Golriz Sargsyan, Astghik Russo, Maria Francesca Domingo-Fernandez, Daniel Zaliani, Andrea Kaladharan, Abish Sethumadhavan, Priya Mubeen, Sarah Gadiya, Yojana Karki, Reagon Gebel, Stephan Surulinathan, Ram Kumar Ruppa Lage-Rupprecht, Vanessa Archipovas, Saulius Mingrone, Geltrude Jacobs, Marc Claussen, Carsten Hofmann-Apitius, Martin Kodamullil, Alpha Tom Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53757 St Augustin Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT D-53113 Bonn Germany Fdn Policlin Univ Agostino Gemelli IRCCS I-00168 Rome Italy Fraunhofer Ctr Machine Learning D-53757 St Augustin Germany Fraunhofer Inst Translat Med & Pharmacol ITMP D-22525 Hamburg Germany Causal Biomodels Kinfra Hitech Pk Cochin 683503 Kerala India Fraunhofer Inst Digital Med MEVIS D-28359 Bremen Germany Kings Coll London Fac Life Sci & Med Sch Life Course Sci Dept Diabet Res London WC2R 2LS England Univ Cattolica Sacro Cuore Dipartimento Med & Chirurg Traslaz Med & Chirurg A Gemelli I-00168 Rome Italy

Although hundreds of datasets have been published since the beginning of the coronavirus pandemic, there is a lack of centralized resources where these datasets are listed and harmonized to facilitate their applicability and uptake by predictive modeling approaches. Firstly, such a centralized resource provides information about data owners to researchers who are searching datasets to develop their predictive models. Secondly, the harmonization of the datasets supports simultaneously taking advantage of several similar datasets. This, in turn, does not only ease the imperative external validation of data-driven models but can also be used for virtual cohort generation, which helps to overcome data sharing impediments. Here, we present that the COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications, and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize, and store studies, datasets, and metadata related to COVID-19 in a comprehensive repository. We believe that our work will facilitate future research and development of personalized predictive risk models for COVID-19.

关键词： COVID-19 data catalogue personalized risk model predictive models

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Development of an early alert model for pandemic situations in Germany

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SCIENTIFIC REPORTS 2023年第1期13卷 1-13页

作者： Wang, Danqi Lentzen, Manuel Botz, Jonas Valderrama, Diego Deplante, Lucille Perrio, Jules Genin, Marie Thommes, Edward Coudeville, Laurent Froehlich, Holger Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat Sch Birlinghoven D-53757 St Augustin Germany Univ Bonn Bonn Aachen Int Ctr IT Friedrich Hirzebruch Allee 6 D-53115 Bonn Germany Quinten Hlth 8 Rue Vernier F-75017 Paris France Sanofi Paris France

The COVID-19 pandemic has pointed out the need for new technical approaches to increase the preparedness of healthcare systems. One important measure is to develop innovative early warning systems. Along those lines, we first compiled a corpus of relevant COVID-19 related symptoms with the help of a disease ontology, text mining and statistical analysis. Subsequently, we applied statistical and machine learning (ML) techniques to time series data of symptom related Google searches and tweets spanning the time period from March 2020 to June 2022. In conclusion, we found that a long-short-term memory (LSTM) jointly trained on COVID-19 symptoms related Google Trends and Twitter data was able to accurately forecast up-trends in classical surveillance data (confirmed cases and hospitalization rates) 14 days ahead. In both cases, F1 scores were above 98% and 97%, respectively, hence demonstrating the potential of using digital traces for building an early alert system for pandemics in Germany.

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Determination of relevant mechanical properties for the production process of polyethylene by using mesoscale molecular simulation techniques

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SOFT MATERIALS 2020年第2-3期18卷 242-261页

作者： Grommes, Dirk Reith, Dirk Bonn Rhein Sieg Univ Appl Sci Inst Technol Resource & Energy Efficient Engn TRE St Augustin Germany Dr Reinold Hagen Stiftung Kautexstr 53 D-53229 Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI St Augustin Germany

In this study, we determine the strain rate and temperature-dependent mechanical material behavior as well as the glass transition temperature and coefficient of thermal expansion of polyethylene melts using molecular dynamics simulation. In order to achieve realistic chain lengths polyethylene was simulated by three different coarse-grained models of various bead sizes. All simulations are performed by using the simulation package ESPResSo++, which we extended with a regulation procedure for the simulation of tensile tests on the micro-scale. The process-relevant observables, such as the elastic modulus, yield stress, and Poisson's ratio are investigated at the meso-level. The chain orientation and entanglement behavior show effects that precisely illuminate the experimental stress strain response, giving important hints for production process control. Summarized, we are able to successfully reproduce the characteristic stress strain response for polyethylene as observed in experiments. Thus, we establish a closer link between microscopic and macroscopic system descriptions in order to provide a deeper understanding of material properties in their production process, i.e. for changing external conditions.

关键词： Meso-scale simulation polyethylene tensile test thermo-mechanical properties orientation behavior

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Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities

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PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY 2023年 122卷 110688页

作者： Bharadhwaj, Vinay Srinivas Mubeen, Sarah Sargsyan, Astghik Jose, Geena Mariya Geissler, Stefan Hofmann-Apitius, Martin Domingo-Fernandez, Daniel Kodamullil, Alpha Tom Fraunhofer Inst Algorithms & Sci Comp Dept Bioinformat D-53757 St Augustin Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT D-53115 Bonn Germany Fraunhofer Ctr Machine Learning St Augustin Germany Kinfra Hitech Pk Causal Biomodels Cochin 683503 Kerala India Kairntech Chemin Vieux Chene F-38240 Meylan France Enveda Biosci Boulder CO 80301 USA

Schizophrenia and bipolar disorder are characterized by highly similar neuropsychological signatures, implying shared neurobiological mechanisms between these two disorders. These disorders also have comorbidities, such as type 2 diabetes mellitus (T2DM). To date, an understanding of the mechanisms that mediate the link between these two disorders remains incomplete. In this work, we identify and investigate shared patterns across multiple schizophrenia, bipolar disorder and T2DM gene expression datasets through multiple strategies. Firstly, we investigate dysregulation patterns at the gene-level and compare our findings against disease-specific knowledge graphs (KGs). Secondly, we analyze the concordance of co-expression patterns across datasets to identify disease -specific as well as common pathways. Thirdly, we examine enriched pathways across datasets and disorders to identify common biological mechanisms between them. Lastly, we investigate the correspondence of shared genetic variants between these two disorders and T2DM as well as the disease-specific KGs. In conclusion, our work reveals several shared candidate genes and pathways, particularly those related to the immune system, such as TNF signaling pathway, IL-17 signaling pathway and NF-kappa B signaling pathway and nervous system, such as dopaminergic synapse and GABAergic synapse, which we propose mediate the link between schizo-phrenia and bipolar disorder and its shared comorbidity, T2DM.

关键词： Transcriptomic Psychiatric disorders Schizophrenia Bipolar disorder Gene expression

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Pharmaceutical patent landscaping: A novel approach to understand patents from the drug discovery perspective

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ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2023年 3卷

作者： Gadiya, Yojana Gribbon, Philip Hofmann-Apitius, Martin Zaliani, Andrea Fraunhofer Inst Translat Med & Pharmacol ITMP Schnackenburgallee 114 D-22525 Hamburg Germany Fraunhofer Cluster Excellence Immune Mediated Dis Theodor Stern Kai 7 D-60590 Frankfurt Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT D-53113 Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat Schloss Birlinghoven D-53757 St Augustin Germany

Patents play a crucial role in the drug discovery process by providing legal protection for discoveries and incentivising investments in research and development. By identifying patterns within patent data resources, researchers can gain insight into the market trends and priorities of the pharmaceutical and biotechnology industries, as well as provide additional perspectives on more fundamental aspects such as the emergence of potential new drug targets. In this paper, we used the patent enrichment tool, PEMT, to extract, integrate, and analyse patent literature for rare diseases (RD) and Alzheimer's disease (AD). This is followed by a systematic review of the underlying patent landscape to decipher trends and applications in patents for these diseases. To do so, we discuss prominent organisations involved in drug discovery research in AD and RD. This allows us to gain an understanding of the importance of AD and RD from specific organisational (pharmaceutical or university) perspectives. Next, we analyse the historical focus of patents in relation to individual therapeutic targets and correlate them with market scenarios allowing the identification of prominent targets for a disease. Lastly, we identified drug repurposing activities within the two diseases with the help of patents. This resulted in identifying existing repurposed drugs and novel potential therapeutic approaches applicable to the indication areas. The study demonstrates the expanded applicability of patent documents from legal to drug discovery, design, and research, thus, providing a valuable resource for future drug discovery efforts. Moreover, this study is an attempt towards understanding the importance of data underlying patent documents and raising the need for preparing the data for machine learning-based applications.

关键词： Patent analysis Drug discovery Patent landscape Rare disease Alzheimer's disease Drug repurposing Target importance

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2.5-Connectivity: Unique Components, Critical Graphs, and Applications 46th

2.5-Connectivity: Unique Components, Critical Graphs, and Ap...

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46th International Workshop on Graph-Theoretic Concepts in Computer Science (WG)

作者： Heinrich, Irene Heller, Till Schmidt, Eva Streicher, Manuel TU Kaiserslautern Algorithms & Complex Grp D-67663 Kaiserslautern Germany Fraunhofer Inst Techno & Wirtschaftsmath Fraunhofer Pl 1 D-67663 Kaiserslautern Germany TU Kaiserslautern Optimizat Res Grp D-67663 Kaiserslautern Germany

ISBN: (纸本)9783030604394;9783030604400

If a biconnected graph stays connected after the removal of an arbitrary vertex and an arbitrary edge, then it is called 2.5-connected. We prove that every biconnected graph has a canonical decomposition into 2.5-connected components. These components are arranged in a tree-structure. We also discuss the connection between 2.5-connected components and triconnected components and use this to present a linear time algorithm which computes the 2.5-connected components of a graph. We show that every critical 2.5-connected graph other than K-4 can be obtained from critical 2.5-connected graphs of smaller order using simple graph operations. Furthermore, we demonstrate applications of 2.5-connected components in the context of cycle decompositions and cycle packings.

关键词： Mixed connectivity Triconnected components Critical graphs

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