J. Christopher Culberson

Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compounds quantitative or categorical biological activity based on a quantitative description of the compounds molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.

Comparison of knowledge-based and distance geometry approaches for generation of molecular conformations.

A knowledge-based approach for generating conformations of molecules has been developed. The method described here provides a good sampling of the molecules conformational space by restricting the generated conformations to those consistent with the reference database. The present approach, internally named et for enumerate torsions, differs from previous database-mining approaches by employing a library of much larger substructures while treating open chains, rings, and combinations of chains and rings in the same manner. In addition to knowledge in the form of observed torsion angles, some knowledge from the medicinal chemist is captured in the form of which substructures are identified. The knowledge-based approach is compared to Blaney et al.s distance geometry (DG) algorithm for sampling the conformational space of molecules. The structures of 113 protein-bound molecules, determined by X-ray crystallography, were used to compare the methods. The present knowledge-based approach (i) generates conformations closer to the experimentally determined conformation, (ii) generates them sooner, and (iii) is significantly faster than the DG method.

Discovery of triarylethanolamine inhibitors of the Kv1.5 potassium channel.

A series of triarylethanolamine inhibitors of the Kv1.5 potassium channel have been prepared and evaluated for their effects in vitro and in vivo. The structure-activity relationship (SAR) studies described herein led to the development of potent, selective and orally active inhibitors of Kv1.5.

Novel oxazolidinone calcitonin gene-related peptide (CGRP) receptor antagonists for the acute treatment of migraine

In our efforts to develop CGRP receptor antagonists as backups to MK-3207, 2, we employed a scaffold hopping approach to identify a series of novel oxazolidinone-based compounds. The development of a structurally diverse, potent (20, cAMP+HS IC50=0.67 nM), and selective compound (hERG IC50=19 μM) with favorable rodent pharmacokinetics (F=100%, t1/2=7h) is described. Key to this development was identification of a 3-substituted spirotetrahydropyran ring that afforded a substantial gain in potency (10 to 35-fold).

Synthesis and Evaluation of 5-Fluoro-2-aryloxazolo[5,4-b]pyridines as β-Amyloid PET Ligands and Identification of MK-3328.

5-Fluoro-2-aryloxazolo[5,4-b]pyridines were synthesized and investigated as potential (18)F containing β-amyloid PET ligands. In competition binding assays using human AD brain homogenates, compounds 14b, 16b, and 17b were identified as having favorable potency versus human β-amyloid plaque and were radiolabeled for further evaluation in in vitro binding and in vivo PET imaging experiments. These studies led to the identification of 17b (MK-3328) as a candidate PET ligand for the clinical assessment of β-amyloid plaque load.

Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles

High-throughput screening (HTS) has enabled millions of compounds to be assessed for biological activity, but challenges remain in the prioritization of hit series. While biological, absorption, distribution, metabolism, excretion, and toxicity (ADMET), purity, and structural data are routinely used to select chemical matter for further follow-up, the scarcity of historical ADMET data for screening hits limits our understanding of early hit compounds. Herein, we describe a process that utilizes a battery of in-house quantitative structure-activity relationship (QSAR) models to generate in silico ADMET profiles for hit series to enable more complete characterizations of HTS chemical matter. These profiles allow teams to quickly assess hit series for desirable ADMET properties or suspected liabilities that may require significant optimization. Accordingly, these in silico data can direct ADMET experimentation and profoundly impact the progression of hit series. Several prospective examples are presented to substantiate the value of this approach.

Reagent Selector: using Synthon Analysis to visualize reagent properties and assist in combinatorial library design.

Reagent Selector is an intranet-based tool that aids in the selection of reagents for use in combinatorial library construction. The user selects an appropriate reagent group as a query, for example, primary amines, and further refines it on the basis of various physicochemical properties, resulting in a list of potential reagents. The results of this selection process are, in turn, converted into synthons: the fragments or R-groups that are to be incorporated into the combinatorial library. The Synthon Analysis interface graphically depicts the chemical properties for each synthon as a function of the topological bond distance from the scaffold attachment point. Displayed in this fashion, the user is able to visualize the property space for the universe of synthons as well as that of the synthons selected. Ultimately, the reagent list that embodies the selected synthons is made available to the user for reagent procurement. Application of the approach to a sample reagent list for a G-protein coupled receptor targeted library is described.