Out-of-the-box Solutions

Hazard and Risk Assessment

Computational methods for hazard and risk assessment and evaluation of toxicity of chemicals have become of increasing interest. In particular, the REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) initiative has put heavy emphasis on non-testing methods to improve the protection of human health and the environment through the better and earlier identification of the intrinsic properties of chemical substances.

  • CRAFT for modeling and evaluating the chemical reactivity, persistence and bio-degradation of chemicals (including their degradation products) in the environment
  • METIS for input and storage of information about metabolism and degradation reactions in databases
  • Drug Design and Property Prediction

    During the past decades, in silico methodologies have become standard technology to support the drug discovery and design process. Ligand-based virtual screening, similarity searching, scaffhold hopping, predictive QSAR and QSPR, such as modeling and prediction of ADME-Tox properties and biological activities, or the analysis of HTS results are some of the methods that are used routinely to identify and optimize new lead structures for a certain biological target. Robust statistical and machine learning methods and proper numerical representations of chemical structures, called molecular descriptors, play a crucial role for all these techniques.

    • CORINA Classic for generating low-energy 3D molecular models
    • ROTATE Classic for generating conformational ensembles
    • CORINA Symphony for managing, manipulating and profiling of chemical data sets
    • SONNIA for analyzing and modeling chemical data
    • Prediction of Chemical Reactivity

      The synthesis of new chemical entities is a difficult task that requires the expert knowledge of a well-trained chemist. Over the last decade, the increasing demand for novel chemical compounds is challenging chemists to increase their productivity. Therefore, the analysis and prediction of chemical reactivity of a moleculae can provide a valuable support to experts in order to prioritize compounds according to their synthetic feasibility.

    • SYLVIA for estimating the ease of synthesis of organic compounds

    Analysis and Prediction of Endogenous Metabolism

    The study of biochemical pathways, i.e., the endogenous metabolism, in living species is of central importance in many disciplines of modern life sciences. The systematic analysis of the intracellular transformations and pathways of metabolic, enzyme-catalyzed reactions can contribute an enormous value to address challenges in the areas of metabolomics, drug discovery, modeling of biological systems or "green chemistry" and metabolic engineering.

    • BioPath.Database containing molecules, reactions and pathways involved in the endogenous metabolism
    • BioPath.Explore for the retrieval, analysis and exploration of BioPath.Database

    Metabolism of Xenobiotics

    In silico prediction of ADMET (absorption, distribution, metabolism, excretion, toxicity) properties is of special interest in the drug discovery and design process in order to detect and eliminate compounds with inappropriate pharmacokinetic properties at an early stage. A central step in the ADMET profiling of potential drug candidates is the evaluation of drug metabolism. Some enzymes involved in the detoxification process show polymorphism and have multimodal binding sites. The majority of the oxidation reactions in phase I metabolism are catalyzed by cytochrome P450 enzymes.

    • isoCYP for predicting the predominant isoform of human CYP P450 substrates
    • Building and Enriching Chemical Databases

      Chemical databases are one of the core technologies and of central importance in any computationally-based research and development effort in life sciences and chemistry. Building and maintaining databases requires robust and reliable powerful tools when chemical compounds from different sources and origin have to be converted into normalized, high-quality structural data. Enriching a database with additional data, such as 3D structures and multiple conformations, stereoisomeric and tautomeric forms or physicochemical properties, drastically increases its value as well as the likelihood of the R&D project's success.