Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule.

As with many other institutions, our company maintains many quantitative structure-activity relationship (QSAR) models of absorption, distribution, metabolism, excretion, and toxicity (ADMET) end points and updates the models regularly. We recently examined version-to-version predictivity for these models over a period of 10 years. In this approach we monitor the goodness of prediction of new molecules relative to the training set of model version V before they are incorporated in the updated model V+1. Using a cell-based permeability assay (Papp) as an example, we illustrate how the QSAR models made from this data are generally predictive and can be utilized to enrich chemical designs and synthesis. Despite the obvious utility of these models, we turned up unexpected behavior in Papp and other ADMET activities for which the explanation is not obvious. One such behavior is that the apparent predictivity of the models as measured by root-mean-square-error can vary greatly from version to version and is sometimes very poor. One intuitively appealing explanation is that the observed activities of the new molecules fall outside the bulk of activities in the training set. Alternatively, one may think that the new molecules are exploring different regions of chemical space than the training set. However, the real explanation has to do with activity cliffs. If the observed activities of the new molecules are different than expected based on similar molecules in the training set, the predictions will be less accurate. This is true for all our ADMET end points.

Absolute configuration assignment of (+)-fluralaner using vibrational circular dichroism.

The absolute configurations of the separated enantiomers of fluralaner, a racemic animal health product used to prevent fleas and ticks, have been assigned using vibrational circular dichroism (VCD). The crystallographic structure of the active enantiomer (+)-fluralaner has previously been shown to have the (S) configuration using small molecule crystallography. We sought a faster analytical method to determine the absolute configuration of the separated enantiomers. When comparing the measured IR (infrared) and VCD spectra, it is apparent that the amide carbonyl groups appear in the IR but are nearly absent in the VCD. Computational work to calculate the VCD and IR using in vacuo models, implicit solvation, and explicitly solvated complexes has implicated conformational averaging of the carbonyl VCD intensities.

Iterative Focused Screening with Biological Fingerprints Identifies Selective Asc-1 Inhibitors Distinct from Traditional High Throughput Screening.

N-methyl-d-aspartate receptors (NMDARs) mediate glutamatergic signaling that is critical to cognitive processes in the central nervous system, and NMDAR hypofunction is thought to contribute to cognitive impairment observed in both schizophrenia and Alzheimer's disease. One approach to enhance the function of NMDAR is to increase the concentration of an NMDAR coagonist, such as glycine or d-serine, in the synaptic cleft. Inhibition of alanine-serine-cysteine transporter-1 (Asc-1), the primary transporter of d-serine, is attractive because the transporter is localized to neurons in brain regions critical to cognitive function, including the hippocampus and cortical layers III and IV, and is colocalized with d-serine and NMDARs. To identify novel Asc-1 inhibitors, two different screening approaches were performed with whole-cell amino acid uptake in heterologous cells stably expressing human Asc-1: (1) a high-throughput screen (HTS) of 3 M compounds measuring 35S l-cysteine uptake into cells attached to scintillation proximity assay beads in a 1536 well format and (2) an iterative focused screen (IFS) of a 45 000 compound diversity set using a 3H d-serine uptake assay with a liquid scintillation plate reader in a 384 well format. Critically important for both screening approaches was the implementation of counter screens to remove nonspecific inhibitors of radioactive amino acid uptake. Furthermore, a 15 000 compound expansion step incorporating both on- and off-target data into chemical and biological fingerprint-based models for selection of additional hits enabled the identification of novel Asc-1-selective chemical matter from the IFS that was not identified in the full-collection HTS.

Comparison of topological, shape, and docking methods in virtual screening.

Virtual screening benchmarking studies were carried out on 11 targets to evaluate the performance of three commonly used approaches: 2D ligand similarity (Daylight, TOPOSIM), 3D ligand similarity (SQW, ROCS), and protein structure-based docking (FLOG, FRED, Glide). Active and decoy compound sets were assembled from both the MDDR and the Merck compound databases. Averaged over multiple targets, ligand-based methods outperformed docking algorithms. This was true for 3D ligand-based methods only when chemical typing was included. Using mean enrichment factor as a performance metric, Glide appears to be the best docking method among the three with FRED a close second. Results for all virtual screening methods are database dependent and can vary greatly for particular targets.

Scoring of KDR kinase inhibitors: using interaction energy as a guide for ranking.

Within a congeneric series of ATP-competitive KDR kinase inhibitors, we determined that the IC(50) values, which span four orders of magnitude, correlated best with the calculated ligand-protein interaction energy using the Merck Molecular Force Field (MMFFs(94)). Using the ligand-protein interaction energy as a guide, we outline a workflow to rank order virtual KDR kinase inhibitors prior to synthesis. When structural information of the target is available, the ability to score molecules a priori can be used to rationally select reagents. Our implementation allows one to select thousands of readily available reagents, enumerate compounds in multiple poses and score molecules in the active site of a protein within a few hours. In our experience, virtual library enumeration is best used when a correlation between computed descriptors/properties and IC(50) or K (i) values has been established.

Molecular transformations as a way of finding and exploiting consistent local QSAR.

The idea of a "transformation", making a small change to a chemical structure, for instance removing or replacing a substituent, is familiar to chemists. We suggest two ways of representing a transformation in silico, as a substructure descriptor difference vector, and as the set of atoms remaining once a maximum common substructure is eliminated. Such transformations can be filtered sensibly, and it is easy to compare one transformation to another. These representations have two applications. First, we can use these methods to automatically organize and display sets of closely related compounds such that any consistent local QSAR in a data set can be easily seen, the T-ANALYZE application. Second, we can suggest to a chemist how to change a molecule "on hand" to a more active one based on local QSAR for that activity, the T-MORPH application.

Binding model for nonpeptide antagonists of alpha(v)beta(3) integrin.

A binding model for nonpeptide antagonists of integrin alpha(v)beta(3) has been developed through docking analyses utilizing the MMFFs force field and the recently published crystal structure, 1JV2. Results of this docking study have led to the identification of a novel binding model for selective antagonists of alpha(v)beta(3) over alpha(IIb)beta(3) integrins. Four different chemical classes are shown to bind in a similar fashion providing a measure of confidence in the proposed model. All alpha(v)beta(3) and alpha(IIb)beta(3) antagonists have a basic nitrogen separated some distance from a carboxylic acid to mimic RGD. For the alpha(v)beta(3) antagonists under present consideration, these charged ends are separated by twelve bonds. The basic nitrogen of the active alpha(v)beta(3) ligands are shown to interact with D150 of alpha(v) and the ligands' carboxylic acid interact with R214 of beta(3) while adopting an extended conformation with minimal protein induced internal strain. In addition, an energetically favorable interaction is found with all of the active alpha(v)beta(3) molecules with Y178 of alpha(v) when docked to the crystallographically determined structure. This novel interaction may be characterized as pi-pi stacking for the most active of the alpha(v)beta(3) selective antagonists. The proposed model is consistent with observed activity as well as mutagenicity and photoaffinity cross-linking studies of the alpha(v)beta(3) integrin.

Synthesis of 1,19-Aza-1,19-desoxy-avermectin B(1a): The First Avermectin Macrolactam.

The first synthesis of 1,19-aza-1,19-desoxy-avermectin B(1a) (2) is described. This new macrolactam, prepared efficiently from avermectin B(1a) (1a) in seven steps, was designed to form an intramolecular hydrogen bond between the amide carbonyl and the adjacent C7 tertiary hydroxyl via a six-center hydrogen bonding network. The presence of this intramolecular hydrogen bond is anticipated to confer additional conformational rigidity to the 16-membered macrocycle.