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Computational Platform Technology
CMDInventus is our proprietary, physics-based and knowledge-based in silico platform for computer-enabled, hypothesis-driven peptide drug design. With CMDInventus, CMDBioscience scientists can efficiently and meaningfully mine massive biological data sets, develop novel peptide design hypotheses, and perform in silico proof-of-concept studies, ultimately enabling more rational peptide synthesis and experimental testing decisions.

Unlike other computational drug design platforms and software applications, CMDInventus has been developed from the ground-up to empower end users to solve the peptide drug design problem, not the small molecule or protein/antibody drug design problems. Hence, all force fields, solvent models, scoring functions, chemical and conformational sampling algorithms, descriptors and training and test data sets have been selected and optimized according to the best available peptide science. This pre-tuning for peptide design enables us to efficiently and fruitfully apply CMDInventus to diverse peptide drug discovery problems and produce meaningful results in weeks.




Importantly, the development and application of CMDInventus is always guided by fundamental physical and theoretical considerations and a commitment to addressing structure-based, ligand-based and ADME/Tox peptide modeling, simulation and design - thus ensuring rigor and general applicability across all stages of peptide drug discovery.


Similarly, CMDInventus has been developed to exploit biological "big data" sets. Hence, the development and application of CMDInventus is always rooted in and shaped by intelligently mined biological and chemical data sets.

It should thus come as no surprise to learn that CMDInventus has been rigorously validated in retrospective and prospective studies and has been leveraged to design multiple novel peptide leads series for diverse protein targets. 

In closing, CMDInventus provides a unique, proprietary, efficient, rigorous and validated path to novel peptide drug candidates.