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| |  | | | | | The company was founded in January 2002 to develop and market robust in silico predictors for ADMET properties to service professionals working in the drug discovery area.
Founders of ChemSilico have a long history in QSAR modeling, neural net analysis and data mining techniques, and in scientific application software development culminating in award winning products. Our primary focus is the development of fast and accurate predictors for a host of physiochemical parameters vital to the drug discovery process as well as providing associated services for customers in the development of specialized predictors to meet their enterprise-wide needs. | | Since its inception the company has developed seven validated predictors and completed a new family of proprietary, molecular descriptors, to complement commonly used topological and E-state descriptors. Our new products are only a start. Over the coming months, they will be joined with three more members in the areas of metabolic stability, permeability, and DMSO solubility. | | ChemSilico consulting provides a highly skilled and dedicated team with years of experience in the QSAR related drug design area. | | Development of specialized biopharmaceutical ADMET property predictors based on a client's in-house datasets. Understanding what's important leads to the discovery of what may be better. Many organizations do not have the resources or the time for such an undertaking so important to their success. | | In Silico Drug Design Solutions: | | Develop libraries to enhance desired properties of drug candidates based on our internal databases of ADMET compound properties coupled to development of specialized predictors covering the chemical-descriptor space of new chemical entities (NCEs). | | Develop specialized predictors on results from in vitro testing. It is one thing to develop QSAR models based on a few hundred data points, developing models covering thousands of datapoints is a vastly different kind of environment. It calls for advanced data selection techniques and neural net analysis to achieve validated-predictors. Ones you trust to give accurate predictions on NCEs. | | | | |
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