Verseon's drug design platform achieves its consistency, speed, and cost-effectiveness by exploring a vast novel chemical space of drug-like compounds using a highly accurate computational modeling engine that identifies the best compounds for any target, and tightly coupling the computational output to the laboratory.
Molecule Design Engine
Verseon's proprietary molecule design engine spans a chemical space of hundreds of millions to billions of drug-like, synthesizable molecules, all of which are available for in-silico modeling. The chemical space is highly diverse consisting of tens of millions of novel scaffolds. This technology provides Verseon's medicinal chemists a very large pool of potential drug candidates.
The molecule design engine also provides a synthetic recipe for every molecule, thus reducing uncertainty and cost when a molecule is ultimately selected for synthesis.
Computational Modeling Engine
At the heart of Verseon's drug discovery platform is Verseon's proprietary computational modeling engine. The engine is based on advances in calculating intermolecular interactions between drug candidate molecules and a given protein target in their aqueous environment.
To apply a computational approach on the scale of hundreds of millions of drug candidates, Verseon employs its own custom built supercomputing cluster. Unprecedented accuracy combined with massive computational resources makes it possible to screen hundreds of millions of potential drug candidates and select the most promising few for synthesis, laboratory testing, and optimization.
Laboratory Integration
The best molecules selected by the Computational Design Engine are synthesized and tested for relevant chemical and biological properties. The molecules that are found to have the best properties in laboratory testing are further optimized.
The optimization process generates small variations of the best molecules using the Molecule Design Engine, selects the top few using the Computational Modeling Engine, and sends them again for further laboratory verification. Within a few such optimization cycles, multiple candidates can be readied for completion of pre-clinical studies and further clinical development.