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.
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 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.
Office: Room 600.02, Gables One Tower
Bryce Allen is a PhD student in the Department of Molecular & Cellular Pharmacology at the University of Miami Miller School of Medicine. Bryce received his Bachelor of Science in Biological Sciences from Virginia Tech in 2013 with a minor in Interdisciplinary Engineering & Science. His interests include Polypharmacology, Computational Chemistry, and Drug Discovery.
Office: Room 600.17, Gables One Tower
Dr. Qiong Cheng joined the University of Miami’s Center for Computational Science in February 2014. Dr. Cheng graduated from Georgia State University in December 2009 with a PhD in Computer Science. Upon graduating, She spent a little over a year training in CCS and then began work as a researcher with the Department of Computer Science at the University of Illinois in Urbana-Champaign.
Dr. Cheng’s research papers cover diverse fields such as modeling and simulation, parallel and distributed computing, and bioinformatics. Her interdisciplinary projects were published in PLOS Genetics, Bioinformatics, and Genome Research with a broad range of topics such as modeling and analysis of gene regulatory mechanisms, metabolic network analysis and comparison, conflict-sensitive dynamics prediction of phospherylation networks and protein-protein interaction networks. One of her first-author papers has been voted in “top 10 papers in regulatory genomics for 2012-13″. She was also awarded the GSU Molecular Basis of Disease Fellowship in 2007, 2008, and 2009.
Her primary research interest lie in the application of machine learning, data mining, graph algorithms, and parallel and distributed computing in designing and analysis of algorithms for solving the problems in molecular biology and disease studies.
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.
Office: Room 600.13, Gables One Tower
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!
Office: Room 600.30, Gables One Tower
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).