Why use konnektor ?

Why might you need access to different network generation approaches? As an example, imagine you are given a set of drug candidates that to be ranked with relative binding free energies. In theory, you could calculate all the possible network transformations to get your ligand ranking (we call this a Maximal Network). Though robust, a Maximal Network approach leads to explosion in time and compute cost, and so more efficient networks are needed.

From a thermodynamic perspective, not all the transformations in a Maximal Network are actually required to retrieve a ranking. In fact, the opposite extreme - a minimally connected network such as a Star Network or a Minimal Spanning Tree (MST) Networks - is actually needed to compute rankings. However, these very efficient networks are highly sensitive to transformation failures, and so network algorithms that add a degree of redundancy are needed to improve the network’s robustness.

konnektor enables you to construct and analyze the multitude of possible networks that fall between these extremes to find an appropriate network generation scheme for a given set of ligands.

See the next section for how to get started generating ligand networks with konnektor.