The Drug Discovery Team use a distributed and parallelized computing environment for many of our modeling and data analysis procedures. 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.
- BioAssay Ontology: Knowledge-based description of chemical biology assays and screening results
- LIFE: LINCS Information FramEwork
- Protein-Drug Interactions: Protein family- and proteome-wide characterization of drug binding interactions for polypharmacology
- Chemical Space: Property-based and functional characterization of chemical space
- Modeling of Chemical Transformations: Knowledge-based description and simulation of synthetically accessible chemistry space
- Collaboration Infrastructure: Sharing and exchanging chemoinformatics data
The Drug Discovery program has a number of specific, cheminformatics-related projects with focus in these areas:
- Ontology Development
- Structure-Based Drug Design
- Biological Categorization and Analysis of Screening Data
- Systematic Data Modeling and Analysis of very large Chemical Structure-Activity Data Sets
- Library Diversity Analysis and Design
- Structure-Activity Data Visualization, Modeling of Adverse Drug Reactions and Toxicity
- In-Silico Synthetic Chemistry
- Machine Learning
- Algorithm Development
- Chemoinformatics Software Development and Integration
Projects are suitable for graduate- and undergraduate-level research for students with an interest in pharmacology, biology, biochemistry, computer science and engineering, computational chemistry, or synthetic chemistry.
Join Mark Musen (Stanford), Janice Kranz and Stephan Schürer (University of Miami) for CDD’s Q3 Town Hall Webinar, “Human vs. Machine-Enhanced Scientific Discovery” Listen Here We’re seeing ‘Big Data’ everywhere: healthcare, business, sports, traffic control, biology, drug discovery. The ‘behind-the-scenes’ tool enabling the power of Big Data – searching, sharing, visualizing, querying, analyzing – is […]
Summer Research Training Program in Biomedical Big Data Science Program Dates: June 1 – August 7, 2015 The 2015 Summer Research Training Program in Biomedical Big Data Science sponsored by the Big Data to Knowledge (BD2K) Data Coordination and Integration Center (DCIC) for the Library of Integrated Network-based Cellular Signatures (LINCS) is a research intensive ten-week […]
UM reserach team will create a Data Coordination and Integration Center for the LINCS program.