MolGenie is developing and applying proprietary machine learning technologies to design novel small molecule compounds as new drug candidates. “Form ever follows function” – this Louis Sullivan principle is our guiding principle for compound design and engineering. Using these rules and based on smart data extraction of proprietary structure-property-relationship datasets we create molecules that realize desired functions, are based on novel scaffolds and can provide the foundation for a broad intellectual property estate.
Our compound design process includes 3 principal design stages:
First, pharmacophoric fingerprints are generated for a desired biological activity profile by 2 possible mechanisms:
– the protein binding pocket is identified and a complementary imprint is calculated, holding properties, such as charge, polarizability, etc.
– alternatively, known structure-activity relationships (SAR) from known biologically active molecules are used to create flexible 2D or 3D pharmacophoric fingerprints.Second, molecules that satisfy the needs of the binding pocket or the SAR pharmacophores are designed – this includes the selection of intellectual property generating chemistries and building blocks from large libraries of chemical reaction transforms.
Third, molecules that have a good predicted bioavailability, solubility and managed metabolic stability are selected from the space of molecules designed in the second step. Furthermore, a broad screening for potential side and off-target effects is performed, excluding molecules with unwanted properties.
To proof the value our technologies, we have selected several high-value “hot” drug targets, aiming at the generation of drug-like and selective small molecules that exhibit a clear structure-activity relationship and have the promise of good tolerability and oral bioavailability.