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W Patrick Walters

Publications and source records attributed to W Patrick Walters.

8 recordsLinked to original sources
PubMed2005

Feature selection in quantitative structure-activity relationships.

A key component of building quantitative structure-activity relationship (QSAR) models is the selection of an appropriate set of molecular features or descriptors. Feature selection can affect the accuracy, stability and interpretability of a model. There are thousands of molecular descriptors currently available, and the selection of an appropriate descriptor set for a particular model can be a daunting task. While there are no absolute rules for selecting appropriate sets of descriptors, a number of recent publications describe automated methods for identifying optimal feature sets. This review provides an overview of a number of the methods described in these publications.

Animals↗
PubMed2004

CORES: an automated method for generating three-dimensional models of protein/ligand complexes.

We describe a new, automated method for building 3D models of small-molecule ligands complexed with proteins. Modeling templates are constructed from frameworks (i.e., ring systems and linkers) of ligands extracted from 3D structures of ligands complexed with proteins that are structurally related to the target protein. These templates are typically substructures of the target ligand and are used to build models that constrain the ligand's conformation and binding orientation in the active site of the target protein. The practical utility of the method is shown by demonstrating that most ligands containing related frameworks bind protein kinases in the same orientation. Moreover, models for 15 of 19 cdk2/ligand complexes in the protein data bank built using our method deviate from the X-ray structure by less than 2 A (rms). Finally, we show that over 70% of small-molecule protein kinase inhibitors published in J. Med. Chem. since 1993 can be modeled using a template extracted from a 3D protein kinase structure in the protein data bank.

Binding Sites↗
PubMed2004

A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance.

A thorough evaluation of some of the most advanced docking and scoring methods currently available is described, and guidelines for the choice of an appropriate protocol for docking and virtual screening are defined. The generation of a large and highly curated test set of pharmaceutically relevant protein-ligand complexes with known binding affinities is described, and three highly regarded docking programs (Glide, GOLD, and ICM) are evaluated on the same set with respect to their ability to reproduce crystallographic binding orientations. Glide correctly identified the crystallographic pose within 2.0 A in 61% of the cases, versus 48% for GOLD and 45% for ICM. In general Glide appears to perform most consistently with respect to diversity of binding sites and ligand flexibility, while the performance of ICM and GOLD is more binding site-dependent and it is significantly poorer when binding is predominantly driven by hydrophobic interactions. The results also show that energy minimization and reranking of the top N poses can be an effective means to overcome some of the limitations of a given docking function. The same docking programs are evaluated in conjunction with three different scoring functions for their ability to discriminate actives from inactives in virtual screening. The evaluation, performed on three different systems (HIV-1 protease, IMPDH, and p38 MAP kinase), confirms that the relative performance of different docking and scoring methods is to some extent binding site-dependent. GlideScore appears to be an effective scoring function for database screening, with consistent performance across several types of binding sites, while ChemScore appears to be most useful in sterically demanding sites since it is more forgiving of repulsive interactions. Energy minimization of docked poses can significantly improve the enrichments in systems with sterically demanding binding sites. Overall Glide appears to be a safe general choice for docking, while the choice of the best scoring tool remains to a larger extent system-dependent and should be evaluated on a case-by-case basis.

Algorithms↗
PubMed2003

Designing screens: how to make your hits a hit.

The basic goal of small-molecule screening is the identification of chemically 'interesting' starting points for elaboration towards a drug. A number of innovative approaches for pursuing this goal have evolved, and the right approach is dictated by the target class being pursued and the capabilities of the organization involved. A recent trend in high-throughput screening has been to place less emphasis on the number of data points that can be produced, and to focus instead on the quality of the data obtained. Several computational and technological advances have aided in the selection of compounds for screening and widened the variety of assay formats available for screening. The effect on the efficiency of the screening process is discussed.

Drug Design↗
PubMed2002

Prediction of 'drug-likeness'.

Recent developments in combinatorial chemistry and high-throughput screening have dramatically increased the scale on which drug discovery programs are carried out. Along with these advances has come a need for automated methods of determining which compounds from a library should be synthesized and screened. These methods range from simple counting schemes to sophisticated machine learning techniques such as neural networks. While many of these methods have performed well in validation studies, the field is still in its formative stage. This paper reviews a number of computational techniques for identifying drug-like molecules and examines challenges facing the field.

Artificial Intelligence↗
PubMed2002

Guiding molecules towards drug-likeness.

This review discusses computational methods for the prediction of drug-likeness. The coverage of published works include the assessment of historical practices of lead generation and optimization, surveys of the properties of known drugs and their constituent fragments and scaffolds, methods for delineating drug space, optimization techniques for simultaneously enhancing multiple properties and drug-like characteristics, similarity metrics and the application of more advanced pattern recognition algorithms for the prediction of drug-likeness. Areas which could be improved in this field are the scope of the datasets used to build models, the chemical interpretability of models, the use of multivariate optimization methods for drug design and the application of underappreciated statistical methods proven to work in other fields.

Animals↗