Beyond the MEK-pocket: can current MEK kinase inhibitors be utilized to synthesize novel type III NCKIs? Does the MEK-pocket exist in kinases other than MEK?

An approach and preliminary results for utilizing legacy MEK inhibitors as templates for a reiterative structural based design and synthesis of novel, type III NCKIs (non-classical kinase inhibitors) is described. Evidence is provided that the MEK-pocket or pockets closely related to it may exist in kinases other than MEK.

Feature-map vectors: a new class of informative descriptors for computational drug discovery.

In order to develop robust machine-learning or statistical models for predicting biological activity, descriptors that capture the essence of the protein-ligand interaction are required. In the absence of structural information from X-ray or NMR experiments, deriving informative descriptors can be difficult. We have developed feature-map vectors (FMVs), a new class of descriptors based on chemical features, to address this challenge. FMVs, which are derived from the conformational models of a few actives, are low dimensional, problem specific, and highly interpretable. By using shape-based alignments and scoring with chemical features, FMVs can combine information about a molecule's shape and the pharmacophores it can match. In five validation studies, bag classifiers built using FMVs have shown high enrichments for identifying actives for five diverse targets: CDK2, 5-HT(3), DHFR, thrombin, and ACE. The interpretability of these descriptors has been demonstrated for CDK2 and 5-HT(3), where the method automatically discovers the standard literature pharmacophore.

Conformation mining: an algorithm for finding biologically relevant conformations.

Discovering essential features shared by active compounds, an important step in drug-design, is complicated by conformational flexibility. We present a new algorithm to efficiently mine the conformational space of multiple actives and find small subsets of conformations likely to be biologically relevant. The approach identifies chemical and steric similarities between actives, providing insight into features important for binding when structural data are absent. Validation studies (thrombin and CDK2 data) produce alignments similar to protein-based alignments.

N-phenylphenylglycines as novel corticotropin releasing factor receptor antagonists.

Screening of a computationally designed synthetic library led to the discovery of the N-phenylphenylglycines (NPPGs) as a novel class of human corticotropin releasing factor (h-CRF(1)) antagonists. Several NPPGs with greater potency than the original hit 1 were rapidly identified, and resolution of the racemate demonstrated that only the R-enantiomer displays activity. This structural class represents the first example of a non-peptide CRF(1) antagonist with a stereochemically distinct receptor binding affinity.

Building predictive ADMET models for early decisions in drug discovery.

This review discusses the current challenges facing researchers developing computational models to predict absorption, distribution, metabolism, excretion and toxicity (ADMET) for early drug discovery. The strengths and weaknesses of different modeling approaches are reviewed and a survey of recent strategies to model several key ADMET parameters, including intestinal permeability, blood-brain barrier penetration, metabolism, bioavailability and drug toxicities, is presented.

Predicting ADME properties and side effects: the BioPrint approach.

Computational methods are increasingly used to streamline and enhance the lead discovery and optimization process. However, accurate prediction of absorption, distribution, metabolism and excretion (ADME) and adverse drug reactions (ADR) is often difficult, due to the complexity of underlying physiological mechanisms. Modeling approaches have been hampered by the lack of large, robust and standardized training datasets. In an extensive effort to build such a dataset, the BioPrint database was constructed by systematic profiling of nearly all drugs available on the market, as well as numerous reference compounds. The database is composed of several large datasets: compound structures and molecular descriptors, in vitro ADME and pharmacology profiles, and complementary clinical data including therapeutic use information, pharmacokinetics profiles and ADR profiles. These data have allowed the development of computational tools designed to integrate a program of computational chemistry into library design and lead development. Models based on chemical structure are strengthened by in vitro results that can be used as additional compound descriptors to predict complex in vivo endpoints. The BioPrint pharmacoinformatics platform represents a systematic effort to accelerate the process of drug discovery, improve quantitative structure-activity relationships and develop in vitro/in vivo associations. In this review, we will discuss the importance of training set size and diversity in model development, the implementation of linear and neighborhood modeling approaches, and the use of in silico methods to predict potential clinical liabilities.

Early mechanistic events in biotin dissociation from streptavidin.

The streptavidin-biotin system has provided a unique opportunity to investigate the molecular details of ligand dissociation pathways. An underlying mechanistic question is whether ligand dissociation proceeds with a relatively ordered process of bond breaking and ligand escape. Here we report a joint computational and crystallographic study of the earliest events in biotin dissociation. In molecular dynamics potential of mean force simulations, a water molecule from a defined access channel intercalated into the hydrogen bond between Asp 128 and biotin, bridging them and stabilizing an intermediate state. In forced biotin dissociation simulations, this event led to subsequent bond breaking steps and ligand escape. In equilibrium simulations, the water molecule was sometimes observed to move back to the access channel with re-formation of the biotin hydrogen bond. Analysis of streptavidin crystal structures revealed a close overlap of crystallographically defined and simulated waters in the water access channel. These results suggest that biotin dissociation is initiated by stochastic coupling of water entry with lengthening of a specific biotin hydrogen-bonding interaction.

A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein.

P-glycoprotein (P-gp) functions as a drug efflux pump, mediating multidrug resistance and limiting the efficacy of many drugs. Clearly, identification of potential P-gp substrate liability early in the drug discovery process would be advantageous. We describe a multiple-pharmacophore model that can discriminate between substrates and nonsubstrates of P-gp with an accuracy of 63%. The application of this filter allows large virtual libraries to be screened efficiently for compounds less likely to be transported by P-gp.