Chemical customization through lead optimization is a vital stage in the new drug discovery pipeline. It involves iteratively modifying the molecular structures of promising lead compounds to enhance their drug-like properties and pharmacological profiles. Computational methods like molecular modeling and structure-based drug design can facilitate this process by predicting structural changes that may improve target binding affinity and selectivity. These in silico approaches narrow down the number of synthetic modifications and structure-activity evaluations required in the lead optimization phase.
The goal of chemical customization is to strike the optimal balance between the lead molecule's potency against the therapeutic target and its absorptive, distributive, metabolic, and excretory (ADME) properties. For instance, additions of polar functional groups may enhance solubility and bioavailability but reduce cell membrane permeability and target site access simultaneously. Medicinal chemists carefully tune hydrophobic, electronic, and steric parameters to boost target affinity without compromising ADME characteristics. They further modify the lead scaffold to avoid off-target interactions that may lead to adverse effects.