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Ingo Muegge

Publications and source records attributed to Ingo Muegge.

8 recordsLinked to original sources
PubMed2006

PMF scoring revisited.

Knowledge-based scoring functions have become accepted choices for fast scoring putative protein-ligand complexes according to their binding affinities. Since their introduction 5 years ago, the knowledge base of protein-ligand complexes has grown to the point were rederiving potentials of mean force becomes meaningful for statistical reasons. Revisiting potential of mean force (PMF) scoring (J. Med. Chem. 1999, 42, 791), we present an updated PMF04 scoring function that is based on 7152 protein-ligand complexes from the PDB. This constitutes an increase of about 10-fold compared to the knowledge base of the original PMF99 score (697 complexes). Because of the increased statistical basis of the PMF04 score, potentials for metal ions have been derived for the first time. In addition, potentials for halogens have reached statistical significance and are included also. Comparison of scoring accuracies between PMF99 and PMF04 shows an increased performance of the new score for many well-established test sets. Extending the testing of PMF scoring to the recently introduced PDBbind database containing the large number of 800 protein-ligand complexes illustrates the current limits of the approach.

Binding Sites↗
PubMed2006

Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring.

The ability to find novel bioactive scaffolds in compound similarity-based virtual screening experiments has been studied comparing Tanimoto-based, ranking-based, voting, and consensus scoring protocols. Ligand sets for seven well-known drug targets (CDK2, COX2, estrogen receptor, neuraminidase, HIV-1 protease, p38 MAP kinase, thrombin) have been assembled such that each ligand represents its own unique chemotype, thus ensuring that each similarity recognition event between ligands constitutes a scaffold hopping event. In a series of virtual screening studies involving 9969 MDDR compounds as negative controls it has been found that atom pair descriptors and 3D pharmacophore fingerprints combined with ranking, voting, and consensus scoring strategies perform well in finding novel bioactive scaffolds. In addition, often superior performance has been observed for similarity-based virtual screening compared to structure-based methods. This finding suggests that information about a target obtained from known bioactive ligands is as valuable as knowledge of the target structures for identifying novel bioactive scaffolds through virtual screening.

Cyclin-Dependent Kinase 2↗
PubMed2004

A molecular docking study of estrogenically active compounds with 1,2-diarylethane and 1,2-diarylethene pharmacophores.

Numerous selective estrogen receptor modulators (SERMs) have been synthesized and assayed in recent years. The focus of this study is to apply coarse-grain molecular docking procedures coupled with fine-grain all-atom force field optimization strategies to shed light on the binding mechanisms of currently available estrogen receptor-active compounds. Although the mechanics of ligand binding in estrogen receptors is generally well understood, there is room for surprises. In this paper computational evidence corroborating the experimentally observed type I agonistic binding mode for estradiol (E2) and diethylstilbesterol (DES) and the type II antagonistic binding mode for 4-hydroxytamoxifen and raloxifen is presented. Included in this type I agonistic mode are the DES derivatives, transstilbene and 1,2-diaryldiaminoethane. In addition, a novel 'type II agonistic' binding mode for 2,3-diarylimidazolines, 4,5-diarylimidazoles, 2,3-diarylpiperazines is introduced. This mode is stabilized by suggesting alternative hydrogen bond anchor points in the ligand binding domain as potential leads for future drug design.

Computer Simulation↗
PubMed2004

Virtual screening for kinase targets.

Kinases have become a major area of drug discovery and structure-based design. Hundreds of 3D structures for more than thirty different kinases are available to the public. High structural and sequence homology within the kinase gene family makes the remaining kinases ideal targets for homology modeling and virtual screening. Somewhat surprisingly, however, the number of publications about virtual screening of kinases is very low. Therefore, rather than reviewing the field of virtual screening for kinases, we attempt here a hybrid approach of presenting what is known and common practice together with new studies on CDK2 and SRC kinase. To illustrate the challenges and pitfalls of virtual screening for kinase targets we focus on the question of how ranking is influenced by the database screened, the docking scheme, the scoring function, the activity of the compounds used for testing, and small changes in the binding pocket. In addition, a case study of finding irreversible inhibitors of ErbB2 through in silico screening is presented.

Binding Sites↗
PubMed2003

Selection criteria for drug-like compounds.

The fast identification of quality lead compounds in the pharmaceutical industry through a combination of high throughput synthesis and screening has become more challenging in recent years. Although the number of available compounds for high throughput screening (HTS) has dramatically increased, large-scale random combinatorial libraries have contributed proportionally less to identify novel leads for drug discovery projects. Therefore, the concept of 'drug-likeness' of compound selections has become a focus in recent years. In parallel, the low success rate of converting lead compounds into drugs often due to unfavorable pharmacokinetic parameters has sparked a renewed interest in understanding more clearly what makes a compound drug-like. Various approaches have been devised to address the drug-likeness of molecules employing retrospective analyses of known drug collections as well as attempting to capture 'chemical wisdom' in algorithms. For example, simple property counting schemes, machine learning methods, regression models, and clustering methods have been employed to distinguish between drugs and non-drugs. Here we review computational techniques to address the drug-likeness of compound selections and offer an outlook for the further development of the field.

Algorithms↗
PubMed2002

Solid-phase synthesis and investigation of benzofurans as selective estrogen receptor modulators.

A library of benzofurans was prepared by solid-phase synthesis methods, and several analogues were identified as potent ligands for the estrogen receptors ER-alpha and ER-beta, with some compounds having selectivity for ER-alpha. Analogues designed to more closely mimic Raloxifene were less effective. Certain benzofurans were effective in a bone pit assay, but were characterized as agonists in a MCF-7 breast tumor cell proliferation assay.

Benzofurans↗
PubMed2002

Pharmacophore features of potential drugs.

Drug discovery efforts rely increasingly on the identification of quality lead compounds through high-throughput synthesis and screening. However, large-scale random libraries have yielded only a low number of quality lead molecules. To address this shortcoming researchers have paid more attention to the concept of "drug-likeness" of molecules in combinatorial and screening libraries. Database profiling and analysis methods have been employed to identify the structural features of known drug molecules. Neural networks and machine learning methods help to distinguish between drugs and nondrugs. More recently, database-independent pharmacophore filters have been introduced that provide simple intuitive rules to classify potential drugs.

Combinatorial Chemistry Techniques↗