| 1. |
- Bajorath, J., et al.
(författare)
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Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds
- 2022
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Ingår i: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15–17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/.
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| 2. |
- Muratov, E. N., et al.
(författare)
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QSAR without borders
- 2020
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Ingår i: Chemical Society Reviews. - : Royal Society of Chemistry (RSC). - 0306-0012 .- 1460-4744. ; 49:11, s. 3525-3564
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Tidskriftsartikel (refereegranskat)abstract
- Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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| 3. |
- Naumov, V., et al.
(författare)
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COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity
- 2021
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Ingår i: Plos Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 17:7
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Tidskriftsartikel (refereegranskat)abstract
- Author summary This article introduces COVIDomic, a new integrative multi-omics online platform designed to facilitate the analysis of the large amount of health data collected from COVID-19 patients. The COVIDomic platform includes a user-friendly interface and provides a set of bioinformatics tools for the analysis of multi-modal metatranscriptomic data to determine the origin of the coronavirus strain and the expected severity of the disease. An analytical workflow includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification. These features allow studying the presence of common microbial organisms, their antibiotic resistance and the severity of the infection, as well as obtaining insights on the geographical locations from which the strain could have originated. Such openly distributed multi-modal platform will greatly accelerate the ongoing COVID-19 research and improve our readiness to respond to other infectious outbreaks. Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks.
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| 4. |
- Oprea, Tudor I, et al.
(författare)
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Unexplored therapeutic opportunities in the human genome
- 2018
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Ingår i: Nature Reviews Drug Discovery. - : Springer Science and Business Media LLC. - 1474-1776 .- 1474-1784. ; 17:5, s. 317-332
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Tidskriftsartikel (refereegranskat)abstract
- A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.
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| 5. |
- Zahoranszky-Kohalmi, G., et al.
(författare)
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Workflow of Integrated Resources to Catalyze Network Driven COVID-19 Research
- 2022
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 62:3, s. 718-729
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Tidskriftsartikel (refereegranskat)abstract
- In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host- host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.
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| 6. |
- Avram, S., et al.
(författare)
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DrugCentral 2021 supports drug discovery and repositioning
- 2021
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 49:D1
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Tidskriftsartikel (refereegranskat)abstract
- DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for similar to 1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the `drugs in news' feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download fromthe DrugCentral web portal.
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| 7. |
- Avram, S., et al.
(författare)
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Off-Patent Drug Repositioning
- 2020
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 60:12, s. 5746-5753
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Tidskriftsartikel (refereegranskat)abstract
- Drug repositioning aims to reuse "old" drugs to treat diseases outside their approved indication(s). Composition-of-matter patents and FDA exclusivities can hinder the immediate availability of some drugs to be repositioned (repurposed). Here, we analyze data from the FDA Orange Book and use current on-market patent validity and exclusivities to classify drugs into on-patent (ONP), off-patent (OFP), and off-market (OFM) sets. In the absence of an unanimously accepted definition for small molecules, these sets include organic molecules and peptides with molecular weight between 100 and 1250, which resulted in 237 ONP drugs, 320 OFM, and 996 OFP drugs, respectively. We discuss the differences between the three categories in terms of primary molecular properties, chemical diversity, mechanism-of-action target classes, and therapeutic areas and comment on the enrichment of OFP drugs in the near future. Given the intellectual property landscape, and in the absence of specific property rights, we suggest that drugs should be prioritized as follows, to improve the repositioning strategy: (i) OFP, (ii) OFM, and (iii) ONP, respectively.
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| 8. |
- Binder, J. L., et al.
(författare)
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AlphaFold illuminates half of the dark human proteins
- 2022
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Ingår i: Current Opinion in Structural Biology. - : Elsevier BV. - 0959-440X. ; 74
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Forskningsöversikt (refereegranskat)abstract
- We investigate the use of confidence scores to evaluate the accuracy of a given AlphaFold (AF2) protein model for drug discovery. Prediction of accuracy is improved by not considering confidence scores below 80 due to the effects of disorder. On a set of recent crystal structures, 95% are likely to have accurate folds. Conformational discordance in the training set has a much more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening. Based on these criteria, AF2 provides models for half of understudied (dark) human proteins and two-thirds of residues in those models. © 2022
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| 9. |
- Binder, J., et al.
(författare)
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Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity
- 2022
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- With increased research funding for Alzheimer's disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1 beta-TNF alpha, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD. Jessica Binder et al. developed a machine learning model to discover potential drug targets for Alzheimer's disease. They validated their 20 top candidates in several in vitro models, and highlight FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2 as potential AD risk genes.
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| 10. |
- Bocci, G., et al.
(författare)
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Can BDDCS illuminate targets in drug design?
- 2019
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Ingår i: Drug Discovery Today. - : Elsevier BV. - 1359-6446. ; 24:12, s. 2299-2306
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Tidskriftsartikel (refereegranskat)abstract
- The fact that pharmacokinetic (PK) properties of drugs influence their interaction with protein targets is a principle known for decades. The same cannot be said for the opposite, namely that targets influence the PK properties of drugs. Evidence confirming this possibility is introduced here for the first time, as we show that certain protein families have a clear preference for drugs with specific PK properties. We investigate this by cross-referencing 'druggable target' annotations for >1000 US Food and Drug Administration (FDA)-approved drugs with their PK profile, as defined by the Biopharmaceutics Drug Disposition Classification System (BDDCS) criteria, and then examine the BDDCS preference for several major target protein families and therapeutic categories. Our findings suggest a novel way to conduct drug discovery by focusing PK profiles at the very early stage of target selection.
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