Molecular recognition is a central biochemical process. It defines drug interaction with biomolecules. This recognition is largely driven by hydrophobicity: hydrophobic areas of drug compounds tend to match hydrophobic areas of binding sites and cavities of macromolecules.
Existing in-silico methodologies are not properly representing hydrophobicity, which is often neglected. Such methods tend to focus their mathematical representations on electrostatic, hydrogen bond, and steric components. The consequence of this flaw is dual: chemical space is not properly mined (subsequently, meaningful chemical entities are neglected) and new chemical structures proposed by drug discovery in-silico tools tend to be constrained and repetitive.
Pharmacelera offers a robust solution to this problem. We have developed new molecular descriptors based on hydrophobicity, complemented by electrostatic and steric properties. These differential descriptors overcome the above mentioned drawbacks and lead to clear improvements when using in silico techniques. Precisely, we achieve an optimal and original description of chemical space which enables finding more chemical diversity.
