Research Areas
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Biography
With over three decades of experience in artificial intelligence and knowledge management for target and drug discovery, Dr. Tudor I. Oprea is a digital drug hunter who proposed key concepts like ChemGPS, the "lead-like approach," systems chemical biology, and the knowledge-based classification of human proteins. Oprea's drug discovery contributions include the co-discovery of the first GPER agonist (IND in 2019 and orphan drug designation in 2021), several GPER antagonists and GLUT transporter inhibitors. Three drugs he co-invented progressed to Phase I clinical trials: Raltegravir (NCT01275183) and R-Ketorolac (NCT01670799) as anti-cancer agents; and LNS8801, a GPER agonist, has also received IND clearance and orphan drug designation for metastatic uveal melanoma (NCT04130516). His validated machine learning models encompass diseases, targets, and chemicals, with significant impact in disease and chemical biology. Following a six-year tenure at AstraZeneca Gothenburg, he held Professorship appointments at the University of New Mexico School of Medicine and the Technical University of Denmark, and guest professorships at the Universities of Perugia, Copenhagen, and Gothenburg. Currently, he serves as the CEO at Expert Systems Inc in San Diego, and Professor Emeritus at UNM School of Medicine. Dr. Oprea has co-authored over 400 publications and book chapters, holds 12 granted US patents, and served as the Principal Investigator for the NIH project "Illuminating the Druggable Genome Knowledge Management Center" from 2014 to 2022. This project resulted in the development of Pharos (pharos.nih.gov) and DrugCentral (drugcentral.org). Recipient of the Hansch Award in 2002 and the Fujita Award in 2026 (www.qsar.org). He continues to pursue his interest in machine learning and artificial intelligence for drug discovery, repurposing, and the study of disease and target biology.
Keywords
Funding (6)
ILLUMINATING THE DRUGGABLE GENOME KNOWLEDGE MANAGEMENT CENTER (IDG KMC) ✓ NIH
NIH Office of the Director (MD, MD, US)
Homepage URL: http://commonfund.nih.gov/idg/fundedresearch
GRANT_NUMBER: 1U54CA189205
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📄 Project Abstract (from NIH)
DESCRIPTION: The overall goal of the Illuminating the Druggable Genome Knowledge Management Center (IDG KMC) is to evaluate and organize (via the Data Organizing Core, DOC), present and visualize (via the User Interface Portal, UIP) and rank (in cooperation with the IDG Consortium) all prospective disease-linked proteins, as potential druggable targets for four protein superfamilies: G-protein-coupled receptors (GPCRs), nuclear receptors (NRs), ion channels (IC) and kinases. By combining data extracted from multiple sources, coupled with algorithmic processing, prediction and human curation, the emerging knowledge will be associated with the appropriate proteins. The KMC will link disease, pathway, protein, gene, chemical, bioactivity, drug discovery and clinical information elements from databases, literature, patents and other documents in the DOC "Target Central" Resource Database. TCRD will serve as primary source for the IDG Query Platform, the UlP-developed system that will enable scientists to access, visualize and analyze IDG-specific data. Coordinating DOC and UIP activities, the Administrative Core, AC, will assist with human curation by organizing class-specific External Target Panels to categorize proteins into 4 classes (Tclin - clinical; Tchem
- manipulated by chemicals; Tmacro - manipulated by macromolecules; and Tdark - the genomic "dark
matter"). Tissue and cellular localization for both disease and protein will serve as central filtes for ranking. The specific aims of the KMC are based on the demonstrated experience of the Oprea-Sklar team at the University of New Mexico (data capture, processing, mining and modeling), and the Simeonov-led team at NCATS (software development, visualization and modeling), supported by teams based in Denmark, Florida and UK. Using automated tools, we performed disease-protein associations for each protein superfamily, obtained preliminary stratification (e.g., Tclin 22%, Tdark 30%), and designed Specific Aims that enable us to further annotate this genome subset. It is expected that within 12 months, the TCRD-based IDG Querly Platform will be operational, which may dramatically improve the target prioritization process for the research community at large and the IDG Consortium, in exploring "dark matter" for GPCRs, NRs, ICs and kinases.
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End: 2016-07-31T00:00:00
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Chemical Pattern Detection and Visualization in Biological Networks ✓ NIH
National Institute of General Medical Sciences (Bethesda, US)
Homepage URL: http://grants.uberresearch.com/100000057/R21GM095952/Chemical-Pattern-Detection-and-Visualization-in-Biological-Networks
GRANT_NUMBER: 5R21GM095952-02
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DESCRIPTION (provided by applicant): While the massive amount of molecular bioactivity data creates new opportunities, it also hinders the way scientists conduct biomedical research due to the inherent difficulty of processing many separate and heterogeneous data sources. The quality and type of data input often limits the project outcome. To improve research outcome, access to all available data and multiple alternative hypothesis testing are essential. Targeting less experienced end-users, we will develop tools that facilitate "jumps" in the small molecule / bioactivity / biomedical data area, leading from one potential solution to another, encouraging users to explore multiple, alternate hypotheses. We will integrate data from multiple bioactivity databases, including PubChem, ChemBank, ChEMBL, PDSP and WOMBAT, into one centralized system. We will develop advanced chemical pattern recognition algorithms and deliver a Cytoscape-based visualization tool for the global exploration of relationships between chemical patterns and biological activities/targets. We will achieve this via three Specific Aims: 1. Create one simple unified interface for many heterogeneous databases, CARLSBAD (Confederated Annotated Research Libraries of Small molecule Biological Activity Data); the data will reconcile small molecule bioactivity data across multiple sources for human, rat and mouse targets. 2. Develop advanced algorithms for chemical pattern detection and annotation; we will detect the Maximum Overlapping Set (MOS) and HierS (hierarchical scaffolds) and annotate chemicals in CARSLBAD accordingly. 3. Develop a Cytoscape plugin for the visualization and exploration of chemical pattern bioactivity networks. Via MOS/HierS patterns, users will be able to identify target specific chemical signatures (determinants for activity and selectivity); in the absence of specific signals, these patterns will serve as rationale for off- target and promiscuous bioactivity prediction. Storing unique target-ligand bioactivity data as well as chemical patterns, CARLSBAD will be designed, implemented and maintained on an enterprise platform for use by the scientific community. The new Cytoscape plugin will integrate with existing core components and plugins to bridge across chemistry and biology in a multi-disciplinary manner.
PUBLIC HEALTH RELEVANCE: The proposed research aims to empower the chemistry and biology research community with an innovative, network-based tool for mining vast amounts of chemical and biological data. It will provide an effective and improved way for researchers to evaluate, visualize and explore small molecule bioactivity data in a multi-disciplinary manner, thus leading to improved output in human health research.
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End: 2013-11-30T00:00:00
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UNIVERSITY OF NEW MEXICO CENTER FOR MOLECULAR DISCOVERY ✓ NIH
NIH Office of the Director (MD, MD, US)
GRANT_NUMBER: 1U54MH084690
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DESCRIPTION (provided by applicant): The University of New Mexico Center for Molecular Discovery (UNMCMD), a specialty center focusing on multiplexed, HT flow cytometry, continues the New Mexico Molecular Libraries Screening Center (U54MH074425) and a Biomedical Research Partnership (R24EB000264). We invented the HT flow cytometry platform HyperCyt(tm) and introduced it to the MLSCN. Our discovery tools enable homogeneous analysis of ligand binding and/or protein-protein interaction (PPI), multiparameter or high content analysis, and real-time measurements of cell response or binding. We analyze a 384 well plate in 11 min. Completed screens on the MLSMR have produced probes for cell (molecular and phenotypic) and bead-based targets. Experience indicates that most targets can be displayed in a flow cytometry compatible format. By creating a suspension array of particles, targets can be highly multiplexed or performed on complex cell populations
without loss of throughput. Our team has produced >100 publications, >20 inventions, books on Flow
Cytometry for Biotechnology and Virtual Screening, and >100 oral outreach presentations world-wide. We have >150 years of flow cytometry experience that includes applications in biochemistry, and cell and molecular biology compatible with a dual specialization in yeast targets. One of us has >20 years of experience in industrial HTS for >150 novel targets. We have core capabilities that include 1) effective outreach and partnership; 2) identifying and developing innovative targets; 3) implementing external primary and secondary assays; 4) production mode screening of multiplexed targets; and 5) data upload to PubChem. We are uniquely positioned to integrate imaging agents and isotopes into the MLPCN. We intend to innovate in all of our activities. Through outreach, we will create and maintain a pipeline of multiplex assays for the MLPCN. Assay development will include, but not be limited to, yeast, eukaryotic, and profiling targets. We will create mechanisms to prioritize and implement partnerships for profiling targets and probes, including compound solubility, for the MLPCN. We will create tools to analyze and visualize multiplex HTS data sets. We will enhance and maintain collaborative tools for networking with target providers and Chemistry Specialty Centers for screening and follow up, and within the MLPCN for collaborative profiling and compound solubility. One Center Driven Project focuses on increasing throughput (from 384 to 1536 well format) and improving the performance of HT flow cytometry by exploiting recent design and engineering breakthroughs in our Center along with commercial partners. Through collaborative outreach, we will evaluate new discovery technologies emerging from the Los Alamos National Labs P41 National Flow Cytometry Resource. A second Center Driven Project develops a toolbox for yeast targets such as PPI, Pathway analysis, protein-DNA interactions, and a universal, multiplex yeast two-hybrid discovery platform. PUBLIC HEALTH RELEVANCE Novel high throughput flow cytometry technology will be used for the discovery of small molecules that can serve as probes, imaging agents and leads in discovery for multiplexed biological targets. A pipeline of targets will developed through active outreach and consortium building efforts.
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End: 2014-05-31T00:00:00
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Development of GPR30-Selective Ligands ✓ NIH
National Cancer Institute (Bethesda, US)
Homepage URL: http://grants.uberresearch.com/100000054/R01CA127731/Development-of-GPR30-Selective-Ligands
GRANT_NUMBER: 5R01CA127731-05
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End: 2015-05-31T00:00:00
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NEW MEXICO MOLECULAR LIBRARIES SCREENING CENTER ✓ NIH
NIH Office of the Director (MD, MD, US)
Homepage URL: http://nmmlsc.health.unm.edu/
GRANT_NUMBER: 1U54 MH074425
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DESCRIPTION (provided by applicant): We have developed innovative flow cytometric tools for discovery research that enable homogeneous analysis of ligand binding and protein-protein interaction, HT sample handling, high content analysis, and real-time measurements of cell response. We have already achieved delivery rates of ?l sized samples from multiwell plates at rates up to 100 samples/min end point assays and multiplex rates up to 1000/min. We have completed screens on several small molecule libraries, discovering novel small molecules that bind to a GPCR peptide receptor. Our experience indicates that virtually any molecular assembly or cell response can be displayed in a format compatible with flow cytometry. Moreover, by creating a suspension array of particles, assays and responses can be highly multiplexed or performed on complex cell populations without loss of throughput. Our novel sampling approach (HyperCyt(r)) makes flow cytometry an attractive platform for drug discovery, proteomics, and real-time analysis of molecular interactions. Flow cytometry is particularly convenient for alternately assessing both cellular and molecular activities of small molecules. To our knowledge, there is no single competing technology that offers the versatility of flow cytometry for Molecular Library Initiative screening or that has the potential of being available to such a large number of laboratories that house flow cytometers (20,000 world-wide). Our team brings together expertise that spans biomedical, biophysical, chemical, computational, instrumentation and engineering disciplines. The team represents an established group already working together through 1R24EB00264, a BRP previously funded to develop high throughput flow cytometry. The BRP is currently applied to our own targets in GPCR signaling pathways. Our screening center will be composed of three scientific teams (Core 1, Assay Optimization; Core 2, Screening and Automation; Core 3, Cheminformatics and Chemistry) and an Integrating core lead by PI, Larry Sklar, who will oversee the Center. Core 1, led by Co-PI Eric Prossnitz, will optimize NIH target assays for high throughput flow cytometry. Core 2, led by Co-PIs Bruce Edwards and
Herbert Tanner will perform HT screens and automate the flow cytometry platform. Core 3, led by Co-PIs Tudor Oprea and Jeffrey Arterburn will integrate cheminformatics and synthetic chemistry teams to increase the overall efficiency of the discovery process.
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End: 2008-06-30T00:00:00
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Acquisition of a High Performance Shared-Memory Computer for Computational Science and Engineering at the University of New Mexico
National Science Foundation (n/a, US)
GRANT_NUMBER: 0420513
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