Molecular Simulation of αvß6 Integrin Inhibitors.

The urgent need for new treatments for the chronic lung disease idiopathic pulmonary fibrosis (IPF) motivates research into antagonists of the RGD binding integrin αvß6, a protein linked to the initiation and progression of the disease. Molecular dynamics (MD) simulations of αvß6 in complex with its natural ligand, pro-TGF-ß1, show the persistence over time of a bidentate Arg-Asp ligand-receptor interaction and a metal chelate interaction between an aspartate on the ligand and an Mg2+ ion in the active site. This is typical of RGD binding ligands. Additional binding site interactions, which are not observed in the static crystal structure, are also identified. We investigate an RGD mimetic, which serves as a framework for a series of potential αvß6 antagonists. The scaffold includes a derivative of the widely utilized 1,8-naphthyridine moiety, for which we present force field parameters, to enable MD and relative free energy perturbation (FEP) simulations. The MD simulations highlight the importance of hydrogen bonding and cation-π interactions. The FEP calculations predict relative binding affinities, within 1.5 kcal mol-1, on average, of experiments.

Late-Stage Functionalization by Chan-Lam Amination: Rapid Access to Potent and Selective Integrin Inhibitors.

A late-stage functionalization of the aromatic ring in amino acid derivatives is described. The key step is a copper-catalysed diversification of a boronate ester by amination (Chan-Lam reaction) that can be carried out on a complex ß-aryl-ß-amino acid scaffold. This not only considerably extends the substrate scope of amination partners, but also delivers an array of potent and selective integrin inhibitors as potential treatment agents of idiopathic pulmonary fibrosis (IPF). This versatile chemical strategy, which is amenable to high-throughput-array protocols, allows the installation of pharmaceutically valuable heteroaromatic fragments at a late stage by direct coupling to NH heterocycles, leading to compounds with drug-like attributes. It thus constitutes a useful addition to the medicinal chemist's repertoire.

Active Search for Computer-aided Drug Design.

We consider lead discovery as active search in a space of labelled graphs. In particular, we extend our recent data-driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an αv integrin, the target protein that belongs to a group of Arg-Gly-Asp integrin receptors. This group of integrin receptors is thought to play a key role in idiopathic pulmonary fibrosis, a chronic lung disease of significant pharmaceutical interest. As an in silico proxy of the binding affinity, we use a molecular docking score to an experimentally determined αvß6 protein structure. The search is driven by a probabilistic surrogate of the activity of all molecules from that space. As the process evolves and the algorithm observes the activity scores of the previously designed molecules, the hypothesis of the activity is refined. The algorithm is guaranteed to converge in probability to the best hypothesis from an a priori specified hypothesis space. In our empirical evaluations, the approach achieves a large structural variety of designed molecular structures for which the docking score is better than the desired threshold. Some novel molecules, suggested to be active by the surrogate model, provoke a significant interest from the perspective of medicinal chemistry and warrant prioritization for synthesis. Moreover, the approach discovered 19 out of the 24 active compounds which are known to be active from previous biological assays.