The Computational Chemistry 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.
It is our vision 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 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 fromsingle‐target selective “blockbuster” drugs towards developing multi‐target drugs (polypharmacology) and individualized medicines.
The Center’s goals 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 Chemoinformatics group use 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 Computational Chemistry Services and Resources.
- Cell publication: Allosteric Inhibition of the IRE1α RNase Preserves Cell Viability and Function during Endoplasmic Reticulum Stress.
- New Release! The LINCS Information FramEwork (LIFE) software.
- Small Molecule Profiling: Dealing with the Data. Substantial public datasets emerging and being made available for cross analysis.
- Stephan Schurer speaks at European Lab Automation 2013 on BioAssay Ontology (BAO) and the LINCS Information FramEwork (LIFE) to Integrate and Analyze Diverse High Throughput and Cellular Profiling Assay Data
- GPCR ontology: development and application of a G protein-coupled receptor pharmacology knowledge framework. Chemoinformatics group reports the first GPCR ontology to facilitate integration and aggregation of GPCR-targeting drugs and demonstrate its application to classify and analyze a large subset of the PubChem database.