Cheminformatics and Computational Chemical Biology

Biography

Biography: Xiche Hu

Abstract

What makes a molecule a drug, i.e., drug-likeness, is a question of great theoretical and practical importance in drug design. Pharmacokinetics and drug-target binding affinity are two equally important aspects of drug-likeness. The former was elegantly profiled by the Lipinski’s role-of-five which had a major influence on both the selection of compounds for high-throughput screening and the design of lead generation libraries over the last two decades. The latter has the promise to profoundly impact lead optimization in drug discovery. However, currently there exist no systematic guidelines that clearly delineate the molecular determinants for drug-target binding. We have carried out a cheminformatics analysis and a large-scale data mining of the Protein Data Bank to decipher molecular determinants for recognition of the FDA approved drugs by proteins. Non-bonded intermolecular interactions (hydrogen bonding,  stacking interactions, XH(X=Cinteractionsand cation interactions) between each drug and surrounding residues in its binding pocket were systematically profiled for 187 non-redundant, high-resolution crystal structures of drug-binding proteins. Furthermore, a high-level quantum chemical calculation was performed to quantify energetics of binding. In addition to confirming the importance of the widely known hydrogen bonding, it was discovered that the aromatic  moieties of drugs played a crucial role in drug-target binding through  stacking and CH interactions. This work represents a challenging undertaking at a historical moment when a large number of X-ray crystallographic structures of drugs bound with their target proteins are available, and when high level quantum chemical analysis of intermolecular interactions of large biomolecules become feasible.