Drug Discovery



Drug Discovery
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The Drug Discovery Program addresses a range of problems at the interface of chemistry, biology, modeling, data mining, engineering, and medicine, including medicinal chemistry and chemical biology. The Program focuses on research questions relevant to the development of functional small molecules with specific physicochemical or biological properties; including protein-ligand interaction, property prediction, electronic structure modeling, chemical synthesis, materials, dynamics and kinetics, and basic drug discovery. The Program also addresses chemical information analysis, data mining, and knowledge generation via semantic integration.

 

researchers in lab coats in front of a computer

Vision

The Drug Discovery Program’s vision is to develop a translational drug informatics platform as a foundation to address the complex challenges in the development of chemical probes and human therapeutics, including accelerating the process and increasing the probability of success. Such a system must provide in-silico-analogous functionality of all aspects of an optimization cycle (testing, hypothesis development / refinement, synthesis, testing) in the different stages of (preclinical) development. It is built on various computational components, algorithms, data sources, ontologies, etc., which are integrated in a flexible modular architecture. The goal is to derive, capture, and effectively utilize knowledge from all accessible relevant internal and external data sources and tools, and from expert scientists.

 

Chemoinformatics Overview

Chemoinformatics and computer‐aided drug design methods play increasingly important roles in preclinical and translational drug research. To develop small molecule therapeutics and chemical probes, computational chemistry approaches are important to gain mechanistic insights and build models related to efficacy, ADME (absorption, distribution, metabolism, excretion), PK (pharmacokinetics), pharmacology, and toxicity. Chemoinformatics tools are also needed to analyze and extract knowledge from the enormous data sets that are generated by high‐throughput screening methods in the pharmaceutical industry, and also in the public domain (such as PubChem). The complexity of the drug discovery process manifests itself in high rates of clinical attrition primarily due to lack of efficacy and clinical safety (toxicity). Advances in systems biology and our increased knowledge in human genetics, functional genomics, and molecular biology hold the promise to expand the drug discovery paradigm from single‐target selective “blockbuster” drugs towards developing multi‐target drugs (polypharmacology) and individualized medicines.

The Center for Computational Science’s goal is to integrate chemoinformatics, computational biology, and bioinformatics methods to develop a translational drug‐informatics platform as a necessary component to address these complex problems. The Drug Discovery Program uses a distributed and parallelized computing environment for many of our modeling and data analysis procedures. On a number of projects, the team is working on innovative computational‐driven approaches and technologies that are relatively broadly targeted at the analysis and modeling at life science data with the goal towards developing small molecule chemical probes and human therapeutics.

For information on services and resources available through this program, please visit the Drug Discovery Resources & Services page.

 

Program Director

Stephan Schurer, Center for Computational Science

Stephan C. Schürer, PhD

 

Team

Maykel Cruz-Monteagudo

 

 

Phone: 305.243.8869
Office: Room 6112A, Rosenstiel Medical Science Building (RMSB), 1600 NW 10th Avenue, Miami, FL 33136

 
Maykel Cruz-Monteagudo is a Postdoctoral Researcher at the Department of Chemistry and Biochemistry, Faculty of Sciences of the University of Porto.  Currently he is collaborating with the Drug Discovery Program at the Center for Computational Science lead by Stephan Schürer.  He received the BSc degree in Pharmaceutical Sciences from the Central University of Las Villas, Cuba, in 2003; and the PhD degree (Toxicology) in Pharmaceutical Sciences from the Faculty of Pharmacy, University of Porto, in 2010. His current research is devoted to the development and application of chemoinformatics approaches to drug discovery, focusing on the application of system chemical biology concepts to multi-target/multi-objective drug discovery. He authored more than 40 publications in peer reviewed journals and 4 international book chapters.

Derek Essegian, Graduate Student, UM Center for Computational Science Drug Discovery Program

 

 

Phone: 305.243.8869
Office: Room 6112A, Rosenstiel Medical Science Building (RMSB), 1600 NW 10th Avenue, Miami, FL 33136

Derek Essegian is a third year MD/PhD student in the Department of Molecular and Cellular Pharmacology. His previous chemistry training at New York University under the mentorship of Dr. David I. Schuster had introduced him to the field of organic synthesis. Since then, Derek has pursued other areas of research, including mycobacterial genetics at Howard Hughes Medical Institute. He finds himself back in the chemistry lab, where he belongs, using computational methods and analyses to drive syntheses of rationally designed compounds. His clinical interests include oncology and dermatology.

Michele Forlin University of Miami Center for Computational Science Drug Discovery Program

 

 

Phone: 305.243.8869
Office: Room 6112A, Rosenstiel Medical Science Building (RMSB), 1600 NW 10th Avenue, Miami, FL 33136

I am a computational scientist working on the development of methods and algorithms for the experimental design, analysis and modeling of biological systems. I received B.S. and M.S. in Statistics from the University of Venice (Italy), and a Ph.D. from The Microsoft Research – University of Trento Centre for Computational and Systems Biology (Italy). I’m fascinated by the systemic approach in trying to understand biology, and more recently, I brought it to the synthetic level, trying to build artificial cells that can interact with natural ones.

I joined the University of Miami in February 2016, and am working on the challenging but fascinating LINCS project (Library of Integrated Network-based Cellular Signatures) as part of the BD2K-LINCS Data Coordination and Integration Center.

Tanya Tae Kelley University of Miami Center for Computational Science Drug Discovery Team

 

 

Phone: 305.243.8869
Office: Room 6112A, Rosenstiel Medical Science Building (RMSB), 1600 NW 10th Avenue, Miami, FL 33136

I am a Medicinal Chemist in the PhD program at the University of Miami Miller School of Medicine in the department of Molecular and Cellular Pharmacology. My projects are focused on the rational design and synthesis of small libraries for the treatment of disease with “dark” targets or multiple targets through polypharmacology. Some of this work has been a part of the NIH common fund project “illuminating the Druggable Genome”. Currently, I am developing novel pre-clinical compounds for psuedokinase NACK inhibition (chemotheraputic), Wnt inhibition (addiction) and dual kinase/BRD4 inhibition (epigenetic chemotheraputic).

Our approach to lead compound identification involves the incorporation of pheno and genotypic cell-based screening data sets, large-scale data analysis, machine learning techniques, virtual library design and computational docking studies. With these powerful tools in hand, there’s a much better sense of what compounds should be synthesized.

I am also fortunate to be involved in the NIH common fund project Library of Integrated Network of Cell-Based Signatures (LINCS) where I lend my knowledge of organic chemistry towards small-molecule compound standardization pipelines for the LINCS data portal. This incredible opportunity has also facilitated a collaboration with The Broad Institute of MIT and Harvard on the design of very unique libraries for in-vitro screening.

My research interests include biomimetic synthesis, dearomatization strategies, photochemical methodologies, natural product fragment-based drug design, drug design for neuropsychiatric disease, and chemical optogenetics.

Vasileios ("Vas") Stathias, Graduate Student, University of Miami Center for Computational Studies Drug Discovery Program

 

 

Phone: 305.243.8869
Office: Room 6112A, Rosenstiel Medical Science Building (RMSB), 1600 NW 10th Avenue, Miami, FL 33136

 
Vas is a third year PhD student in the Department of Human Genetics and Genomics studying the Epigenetics of Brain Tumors. He earned his BA in Molecular Biology & Genetics from Democritus University in Greece. Since then he is an active part of the UM Graduate Community and looks forward to make UM the best it can be. In his spare time, he enjoys watching Michael Bay movies, playing volleyball and going out!

Human Genetics and Genomics Graduate Students- L-R: Sara Linker, Sicen Liu, Vasileios Stathias, Brook DeRosa, Crystal Humphries, Kinsley Belle, Horacio Ramirez, Tania Arguello, Michael Gonzalez, Patrice Persad, YoSon Park, Zhi Liu, James Hicks, Hemalatha Raju, Monique Courtenay, Lei Cao, Chong Li, Athena Hadjixenofontos, Sathish Srinivasan

Human Genetics and Genomics Graduate Students- L-R: Sara Linker, Sicen Liu, Vasileios Stathias, Brook DeRosa, Crystal Humphries, Kinsley Belle, Horacio Ramirez, Tania Arguello, Michael Gonzalez, Patrice Persad, YoSon Park, Zhi Liu, James Hicks, Hemalatha Raju, Monique Courtenay, Lei Cao, Chong Li, Athena Hadjixenofontos, Sathish Srinivasan

Raymond Terryn, PhD, University of Miami Center for Computational Science, Drug Discovery program

 

 

Phone: 305.243.8869
Office: Room 6112A, Rosenstiel Medical Science Building (RMSB), 1600 NW 10th Avenue, Miami, FL 33136

 

As a member of Dr. Stephan Schurer’s research group, Dr. Terryn holds management, engineering, and research positions within several projects under the National Institutes of Health Big Data to Knowledge (NIH-BD2K) initiative, including the Library of Integrated Network Cellular Signatures Data Coordination and Integration Center (BD2K-LINCS DCIC) and the smartAPI working group.

Dr. Terryn is a computational chemist and cheminformatician with a background in algorithm development and implementation for describing intermolecular interactions. Early work includes development of novel approaches to quantum mechanical descriptions of pi-stacking and quantum tunneling phenomena. Subsequent construction of a quantum tunneling simulator enabled the discovery of novel molecular descriptors for Quantitative Structure Activity Relationships (QSAR). Primary research interests continue a focus on computational methods for describing molecular mechanisms of action/interaction with an emphasis on data driven drug discovery/design. Notable projects within this focus include the development, application, and refinement of reduced dimensionality descriptors for small molecule-protein docking and machine learning techniques to predict small molecule binding targets and drug/pesticide efficacy. Expanded research interests are focused on fundamental aspects of Computer Science and Data Science in a more broad sense, including: large-scale predictive modeling; ontological standards for semantics; and data curation, structuring, and validation.

 

Noted Publications

Amrapali Zaveri, Shima Dastgheib, Trish Whetzel, Ruben Verborgh, Paul Avillach, Gabor Korodi, Raymond Terryn, Kathleen Jagodnik, Pedro Assis, Chunlei Wu and Michel Dumontier. smartAPI: Towards a more intelligent network of Web APIs http://2017.eswc-conferences.org/program/accepted-papers

Raymond J. Terryn III, Krishnan Sriraman, Joel A. Olson, and J. Clayton Baum. In silico simulations of tunneling barrier measurements for molecular orbital-mediated junctions: A molecular orbital theory approach to scanning tunneling microscopy. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 34, 051402 (2016). http://avs.scitation.org/doi/10.1116/1.4959826

Terryn RJ III, German HW, Kummerer TM, Sinden RR, Baum JC, Novak MJ. Novel computational study on π-stacking to understand mechanistic interactions of Tryptanthrin analogues with DNA. Toxicol Mech Methods. 2014 Jan;24(1):73-9. https://www.ncbi.nlm.nih.gov/pubmed/24156546

 

 

John Turner

 

 

Phone: 305.243.8869
Office: Room 6112A, Rosenstiel Medical Science Building (RMSB), 1600 NW 10th Avenue, Miami, FL 33136

John is a computational biochemist interested in cancer pathways and application of computational methods to biological pathways and drug development. He received his B.S. in Biology and Chemistry at the University of North Florida. He is currently working on the LINCS project, assisting with the generation of drug and medical based ontologies and applying machine learning to drug discovery methods and optimization.

 

 
Phone: 305.243.8869
Office: Room 6112A, Rosenstiel Medical Science Building (RMSB), 1600 NW 10th Avenue, Miami, FL 33136

Afoma C. Umeano is an MD/PhD at the University of Miami Miller School of Medicine. Her PhD is focused on medicinal and computational chemistry applications in Molecular and Cellular Pharmacology. Her projects are based on the rational design and synthesis of small libraries targeting epigenetic modulators and kinases for the treatment of various cancers. Also, she employs the use of computational chemistry and various analytics towards the discovery of protein targets for known active compounds. This work is partially funded by the NIH common-fund project “Illuminating the Druggable Genome” and the “Library of Integrated Network of Cell-Based Signatures (LINCS), where she lends her knowledge of chemistry towards standardization of pipelines for the LINCS data portal.

Her research interests include natural product synthesis, fragment-based drug design, drug design for oncological diseases, target-based drug design, and diversity-oriented medicinal chemistry. Her clinical interests include pediatric oncology, immunology, and pediatric surgery.

Dusica Vidovic, Center for Computational Science

 

 

Phone: 305.243.8869
Office: Room 6112A, Rosenstiel Medical Science Building (RMSB), 1600 NW 10th Avenue, Miami, FL 33136

Pharmacophore Model

 

Dr. Dušica Vidović joined the Chemoinformatics team at CCS  in September 2008. Before she joined, she was a Research Associate at The Scripps Research Institute and The Computer-Chemistry Centrum in the field of chemoinformatics.

Dr. Vidović has experience in HTS data analysis, structure-based and ligand-based drug design, homology modeling, docking and scoring, virtual screening, pharmacophore modeling, lead optimization, ADMET modeling, QSAR/QSPR prediction, physicochemical properties prediction, topological indices. She received her PhD degree in chemistry from the University of Kragujevac (Kragujevac, Serbia).

 

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