A novel method for exploring macrocycle conformational space, Prime macrocycle conformational sampling (Prime-MCS), is introduced and evaluated in the context of other available algorithms (Molecular Dynamics, LowModeMD in MOE, and MacroModel Baseline Search). The algorithms were benchmarked on a data set of 208 macrocycles which was curated for diversity from the Cambridge Structural Database, the Protein Data Bank, and the Biologically Interesting Molecule Reference Dictionary. The algorithms were evaluated in terms of accuracy (ability to reproduce the crystal structure), diversity (coverage of conformational space), and computational speed. Prime-MCS most reliably reproduced crystallographic structures for RMSD thresholds >1.0 Å, most often produced the most diverse conformational ensemble, and was most often the fastest algorithm. Detailed analysis and examination of both typical and outlier cases were performed to reveal characteristics, shortcomings, expected performance, and complementarity of the methods.

A kinome-wide selectivity screen of >20000 compounds with a rich representation of many structural classes has been completed. Analysis of the selectivity patterns for each class shows that a broad spectrum of structural scaffolds can achieve specificity for many kinase families. Kinase selectivity and potency are inversely correlated, a trend that is also found in a large set of kinase functional data. Although selective and nonselective compounds are mostly similar in their physicochemical characteristics, we identify specific features that are present more frequently in compounds that bind to many kinases. Our results support a scaffold-oriented approach for building compound collections to screen kinase targets.

Affinity selection screening of macrocycle libraries derived from DNA-programmed chemistry identified XIAP BIR2 and BIR3 domain inhibitors that displace bound pro-apoptotic caspases. X-ray cocrystal structures of key compounds with XIAP BIR2 suggested potency-enhancing structural modifications. Optimization of dimeric macrocycles with similar affinity for both domains were potent pro-apoptotic agents in cancer cell lines and efficacious in shrinking tumors in a mouse xenograft model.

The toll-like receptor (TLR) family is an evolutionarily conserved component of the innate immune system, responsible for the early detection of foreign or endogenous threat signals. In the context of autoimmunity, the unintended recognition of self-motifs as foreign promotes initiation or propagation of disease. Overactivation of TLR7 and TLR9 have been implicated as factors contributing to autoimmune disorders such as psoriasis, arthritis, and lupus. In our search for small molecule antagonists of TLR7/9, 7f was identified as possessing excellent on-target potency for human TLR7/9 as well as for TLR8, with selectivity against other representative TLR family members. Good pharmacokinetic properties and a relatively balanced potency against TLR7 and TLR9 in mouse systems (systems which lack functional TLR8) made this an excellent in vivo tool compound, and efficacy from oral dosing in preclinical models of autoimmune disease was demonstrated.

We describe an extension to the matched molecular pairs approach that merges pairwise activity differences with three-dimensional contextual information derived from X-ray crystal structures and binding pose predictions. The incorporation of 3D binding poses allows the direct comparison of structural changes to diverse chemotypes in particular binding pockets, facilitating the transfer of SAR from one series to another. Integrating matched pair data with the receptor structure can also highlight activity patterns within the binding site--for example, "hot spot" regions can be visualized where changes in the ligand structure are more likely to impact activity. The method is illustrated using P38α structural and activity data to generate novel hybrid ligands, identify SAR transfer networks, and annotate the receptor binding site.