David J. Diller

High throughput docking for library design and library prioritization

The prioritization of the screening of combinatorial libraries is an extremely important task for the rapid identification of tight binding ligands and ultimately pharmaceutical compounds. When structural information for the target is available, molecular docking is an approach that can be used for prioritization. Here, we present the initial validation of a new rapid approach to molecular docking developed for prioritizing combinatorial libraries. The algorithm is tested on 103 individual cases from the protein data bank and in nearly 90% of these cases docks the ligand to within 2.0 Å of the observed binding mode. Because the mean CPU time is <5 s/mol, this approach can process hundreds of thousands of compounds per week. Furthermore, if a somewhat less thorough search is performed, the search time drops to 1 s/mol, thus allowing millions of compounds to be docked per week and tested for potential activity. Proteins 2001;43:113–124.

Insights for predicting blood-brain barrier penetration of CNS targeted molecules using QSPR approaches.

Due to the high attrition rate of central nervous system drug candidates during clinical trials, the assessment of blood-brain barrier (BBB) penetration in early research is particularly important. A genetic approximation (GA)-based regression model was developed for predicting in vivo blood-brain partitioning data, expressed as logBB (log[brain]/[blood]). The model was built using an in-house data set of 193 compounds assembled from 22 different therapeutic projects. The final model (cross-validated r(2) = 0.72) with five molecular descriptors was selected based on validation using several large internal and external test sets. We demonstrate the potential utility of the model by applying it to a set of literature reported secretase inhibitors. In addition, we describe a rule-based approach for rapid assessment of brain penetration with several simple molecular descriptors.

Computation of the physio-chemical properties and data mining of large molecular collections

Very large data sets of molecules screened against a broad range of targets have become available due to the advent of combinatorial chemistry. This information has led to the realization that ADME (absorption, distribution, metabolism, and excretion) and toxicity issues are important to consider prior to library synthesis. Furthermore, these large data sets provide a unique and important source of information regarding what types of molecular shapes may interact with specific receptor or target classes. Thus, the requirement for rapid and accurate data mining tools became paramount. To address these issues Pharmacopeia, Inc. formed a computational research group, The Center for Informatics and Drug Discovery (CIDD). * In this review we cover the work done by this group to address both in silico ADME modeling and data mining issues faced by Pharmacopeia because of the availability of a large and diverse collection (over 6 million discrete compounds) of drug‐like molecules. In particular, in the data mining arena we discuss rapid docking tools and how we employ them, and we describe a novel data mining tool based on a 1D representation of a molecule followed by a molecular sequence alignment step. For the ADME area we discuss the development and application of absorption, blood–brain barrier (BBB) and solubility models. Finally, we summarize the impact the tools and approaches might have on the drug discovery process.

Technique for Generating Three-Dimensional Alignments of Multiple Ligands from One-Dimensional Alignments

We describe and demonstrate a method for the simultaneous, fully flexible alignment of multiple molecules with a common biological activity. The key aspect of the algorithm is that the alignment problem is first solved in a lower dimensional space, in this case using the one-dimensional representations of the molecules. The three-dimensional alignment is then guided by constraints derived from the one-dimensional alignment. We demonstrate using 10 hERG channel blockers, with a total of 72 rotatable bonds, that the one-dimensional alignment is able to effectively isolate key conserved pharmacophoric features and that these conserved features can effectively guide the three-dimensional alignment. Further using 10 estrogen receptor agonists and 5 estrogen receptor antagonists with publicly available cocrystal structures we show that the method is able to produce superpositions comparable to those derived from crystal structures. Finally, we demonstrate, using examples from peptidic CXCR3 agonists, that the method is able to generate reasonable binding hypotheses.

Targeting signal transduction with large combinatorial collections

The large-scale application of combinatorial chemistry to drug discovery is an endeavor that is now more than ten years old. The growth of chemical libraries together with the influx of novel genomic targets has led to a reconstruction of the drug-screening paradigm. The drug discovery industry faces a post-genomic world where the interplay between tens-of-thousands of proteins must be addressed. To compound this complexity, there now exists the ability to screen millions of compounds against a single target. This review focuses on the practice and use of selecting individual compounds from large chemical libraries that act on targets relevant to signal transduction.

A structural informatics approach to mine kinase knowledge bases

In this paper, we describe a combination of structural informatics approaches developed to mine data extracted from existing structure knowledge bases (Protein Data Bank and the GVK database) with a focus on kinase ATP-binding site data. In contrast to existing systems that retrieve and analyze protein structures, our techniques are centered on a database of ligand-bound geometries in relation to residues lining the binding site and transparent access to ligand-based SAR data. We illustrate the systems in the context of the Abelson kinase and related inhibitor structures.

VSViewer3D: A Tool for Interactive Data Mining of Three-Dimensional Virtual Screening Data

The VSviewer3D is a simple Java tool for visual exploration of three-dimensional (3D) virtual screening data. The VSviewer3D brings together the ability to explore numerical data, such as calculated properties and virtual screening scores, structure depiction, interactive topological and 3D similarity searching, and 3D visualization. By doing so the user is better able to quickly identify outliers, assess tractability of large numbers of compounds, visualize hits of interest, annotate hits, and mix and match interesting scaffolds. We demonstrate the utility of the VSviewer3D by describing a use case in a docking based virtual screen.