Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding.

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This novel methodology in detailed retrospective and prospective testing succeeded to determine protein-ligand binding modes with a root-mean-square deviation within 2.5 Å in over 90% of cross-docking cases.

AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling.

Aim: We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models.

Methodology/results: The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach.

Exploring conformational search protocols for ligand-based virtual screening and 3-D QSAR modeling.

3-D ligand conformations are required for most ligand-based drug design methods, such as pharmacophore modeling, shape-based screening, and 3-D QSAR model building. Many studies of conformational search methods have focused on the reproduction of crystal structures (i.e.

Kernel-based partial least squares: application to fingerprint-based QSAR with model visualization.

Numerous regression-based and machine learning techniques are available for the development of linear and nonlinear QSAR models that can accurately predict biological endpoints. Such tools can be quite powerful in the hands of an experienced modeler, but too frequently a disconnect remains between the modeler and project chemist because the resulting QSAR models are effectively black boxes. As a result, learning methods that yield models that can be visualized in the context of chemical structures are in high demand.

Hole filling and library optimization: application to commercially available fragment libraries.

Compound libraries comprise an integral component of drug discovery in the pharmaceutical and biotechnology industries. While in-house libraries often contain millions of molecules, this number pales in comparison to the accessible space of drug-like molecules. Therefore, care must be taken when adding new compounds to an existing library in order to ensure that unexplored regions in the chemical space are filled efficiently while not needlessly increasing the library size.

Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring.

Shape-based methods for aligning and scoring ligands have proven to be valuable in the field of computer-aided drug design. Here, we describe a new shape-based flexible ligand superposition and virtual screening method, Phase Shape, which is shown to rapidly produce accurate 3D ligand alignments and efficiently enrich actives in virtual screening. We describe the methodology, which is based on the principle of atom distribution triplets to rapidly define trial alignments, followed by refinement of top alignments to maximize the volume overlap.

Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods.

Virtual screening is a widely used strategy in modern drug discovery and 2D fingerprint similarity is an important tool that has been successfully applied to retrieve active compounds from large datasets. However, it is not always straightforward to select an appropriate fingerprint method and associated settings for a given problem. Here, we applied eight different fingerprint methods, as implemented in the new cheminformatics package Canvas, on a well-validated dataset covering five targets.

Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments.

A systematic virtual screening study on 11 pharmaceutically relevant targets has been conducted to investigate the interrelation between 8 two-dimensional (2D) fingerprinting methods, 13 atom-typing schemes, 13 bit scaling rules, and 12 similarity metrics using the new cheminformatics package Canvas. In total, 157 872 virtual screens were performed to assess the ability of each combination of parameters to identify actives in a database screen. In general, fingerprint methods, such as MOLPRINT2D, Radial, and Dendritic that encode information about local environment beyond simple linear paths outperformed other fingerprint methods.

PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results.

We introduce PHASE, a highly flexible system for common pharmacophore identification and assessment, 3D QSAR model development, and 3D database creation and searching. The primary workflows and tasks supported by PHASE are described, and details of the underlying scientific methodologies are provided. Using results from previously published investigations, PHASE is compared directly to other ligand-based software for its ability to identify target pharmacophores, rationalize structure-activity data, and predict activities of external compounds.

Induction of decision trees via evolutionary programming.

Decision trees have been used extensively in cheminformatics for modeling various biochemical endpoints including receptor-ligand binding, ADME properties, environmental impact, and toxicity. The traditional approach to inducing decision trees based upon a given training set of data involves recursive partitioning which selects partitioning variables and their values in a greedy manner to optimize a given measure of purity. This methodology has numerous benefits including classifier interpretability and the capability of modeling nonlinear relationships.

In silico models for the prediction of dose-dependent human hepatotoxicity.

The liver is extremely vulnerable to the effects of xenobiotics due to its critical role in metabolism. Drug-induced hepatotoxicity may involve any number of different liver injuries, some of which lead to organ failure and, ultimately, patient death. Understandably, liver toxicity is one of the most important dose-limiting considerations in the drug development cycle, yet there remains a serious shortage of methods to predict hepatotoxicity from chemical structure.

Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition.

A new in silico model is developed to predict cytochrome P450 2D6 inhibition from 2D chemical structure. Using a diverse training set of 100 compounds with published inhibition constants, an ensemble approach to recursive partitioning is applied to create a large number of classification trees, each of which yields a yes/no prediction about inhibition for a given compound. These binary classifications are combined to provide an overall prediction, which answers the yes/no question about inhibition and provides a measure of confidence about that prediction.

Quantum mechanical models correlating structure with selectivity: predicting the enantioselectivity of beta-amino alcohol catalysts in aldehyde alkylation.

Quantitative structure selectivity relationship (QSSR) models are described that provide consistently reliable predictions for the asymmetric addition of Et2Zn to PhCHO catalyzed by beta-amino alcohols. Statistically valid two-variable linear regression models that correlate the structures of the chiral catalysts with their enantioselectivities are obtained from three-dimensional physical property grids. The strength of the present method is that statistical models obtained from a small set of experimentally determined selectivities and relatively simple theoretical calculations yield selectivity predictions that are as accurate as those derived from higher-level calculations of transition-structure energies.

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.