Ultrafast shape recognition to search compound databases for similar molecular shapes

Finding a set of molecules, which closely resemble a given lead molecule, from a database containing potentially billions of chemical structures is an important but daunting problem. Similar molecular shapes are particularly important, given that in biology small organic molecules frequently act by binding into a defined and complex site on a macromolecule. Here, we present a new method for molecular shape comparison, named ultrafast shape recognition (USR), capable of screening billions of compounds for similar shapes using a single computer and without the need of aligning the molecules before testing for similarity. Despite its extremely fast comparison rate, USR will be shown to be highly accurate at describing, and hence comparing, molecular shapes.     

A shape- and chemistry-based docking method and its use in the design of HIV-1 protease inhibitors

Summary The program DOCK [1,2] has been used successfully to identify molecules which will bind to a specified receptor [3]. The original method ranks molecules based on their shape complementarity to the receptor site and relies on the chemist to bring the appropriate electrostatic or hydrogen bond properties into the molecular skeletons obtained in the search. This is useful when screening a small database of compounds, where it is not likely that molecules with both the correct shape and electrostatic properties will be found. As large databases are more likely to have redundant molecular shapes with a variety of functionality (e.g., members of a congeneric series), it would be useful to have a method which identifies molecules with both the correct shape and functionality. To this end we have modified the DOCK 1.0 method to target user-specified atom types to selected positions in the receptor site. The target sites can be chosen based on structural evidence, calculation or inspection. Targeted-DOCK improves the ability of the DOCK method to find the crystallographically determined binding mode of a ligand. Additionally, targeted-DOCK searches a database of small molecules at 100–1000 times the rate of DOCK 1.0, allowing more molecules to be screened and more sophisticated scoring schemes to be employed. Targeted-DOCK has been used successfully in the design of a novel non-peptide inhibitor of HIV-1 protease.     

Toward a global maximization of the molecular similarity function: Superposition of two molecules

A quantum similarity measure between two molecules is normally identified with the maximum value of the overlap of the corresponding molecular electron densities. The electron density overlap is a function of the mutual positioning of the compared molecules, requiring the measurement of similarity, a solution of a multiple-maxima problem. Collapsing the molecular electron densities into the nuclei provides the essential information toward a global maximization of the overlap similarity function, the maximization of which, in this limit case, appears to be related to the so-called assignment problem. Three levels of approach are then proposed for a global search scanning of the similarity function. In addition, atom—atom similarity Lorentzian potential functions are defined for a rapid completion of the function scanning. Performance is tested among these three levels of simplification and the Monte Carlo and simplex methods. Results reveal the present algorithms as accurate, rapid, and unbiased techniques for density-based molecular alignments. © 1997 by John Wiley & Sons, Inc. J Comput Chem 18: 826–846, 1997     

Pharmacophore alignment search tool: influence of scoring systems on text-based similarity searching

The text-based similarity searching method Pharmacophore Alignment Search Tool is grounded on pairwise comparisons of potential pharmacophoric points between a query and screening compounds. The underlying scoring matrix is of critical importance for successful virtual screening and hit retrieval from large compound libraries. Here, we compare three conceptually different computational methods for systematic deduction of scoring matrices: assignment-based, alignment-based, and stochastic optimization. All three methods resulted in optimized pharmacophore scoring matrices with significantly superior retrospective performance in comparison with simplistic scoring schemes. Computer-generated similarity matrices of pharmacophoric features turned out to agree well with a manually constructed matrix. We introduce the concept of position-specific scoring to text-based similarity searching so that knowledge about specific ligand-receptor binding patterns can be included and demonstrate its benefit for hit retrieval. The approach was also used for automated pharmacophore elucidation in agonists of peroxisome proliferator activated receptor gamma, successfully identifying key interactions for receptor activation.     

Improving VEGFR-2 docking-based screening by pharmacophore postfiltering and similarity search postprocessing

Conventional docking-based virtual screening (VS) of chemical databases is based on the ranking of compounds according to the values retrieved by a scoring function (typically, the binding affinity estimation). However, using the most suitable scoring function for each kind of receptor pocket is not always an effective process to rank compounds, and sometimes neither to distinguish between correct binding modes from incorrect ones. To improve actives from decoys selection, here we propose a three-step VS protocol, which includes the conventional docking step, a pharmacophore postfilter step, and a similarity search postprocess. This VS protocol is retrospectively applied to VEGFR-2 (Kdr-kinase) inhibitors. The resulting docking poses calculated using the Alpha HB scoring function implemented in MOE are postfiltered according to defined pharmacophore interactions (structure based). The selected poses are again ranked according to their molecular similarity (MACCS fingerprint) to the cognate ligand. Results show that both the overall and early VS performance improve the application of this protocol.     

Shape‐based virtual screening with volumetric aligned molecular shapes

Shape‐based virtual screening is an established and effective method for identifying small molecules that are similar in shape and function to a reference ligand. We describe a new method of shape‐based virtual screening, volumetric aligned molecular shapes (VAMS). VAMS uses efficient data structures to encode and search molecular shapes. We demonstrate that VAMS is an effective method for shape‐based virtual screening and that it can be successfully used as a prefilter to accelerate more computationally demanding search algorithms. Unique to VAMS is a novel minimum/maximum shape constraint query for precisely specifying the desired molecular shape. Shape constraint searches in VAMS are particularly efficient and millions of shapes can be searched in a fraction of a second. We compare the performance of VAMS with two other shape‐based virtual screening algorithms a benchmark of 102 protein targets consisting of more than 32 million molecular shapes and find that VAMS provides a competitive trade‐off between run‐time performance and virtual screening performance. © 2014 Wiley Periodicals, Inc.     

RigFit: A new approach to superimposing ligand molecules

If structural knowledge of a receptor under consideration is lacking, drug design approaches focus on similarity or dissimilarity analysis of putative ligands. In this context the mutual ligand superposition is of utmost importance. Methods that are rapid enough to facilitate interactive usage, that allow to process sets of conformers and that enable database screening are of special interest here. The ability to superpose molecular fragments instead of entire molecules has proven to be helpful too. The RigFit approach meets these requirements and has several additional advantages. In three distinct test applications, we evaluated how closely we can approximate the observed relative orientation for a set of known crystal structures, we employed RigFit as a fragment placement procedure, and we performed a fragment-based database screening. The run time of RigFit can be traded off against its accuracy. To be competitive in accuracy with another state-of-the-art alignment tool, with which we compare our method explicitly, computing times of about 6s per superposition on a common day workstation are required. If longer run times can be afforded the accuracy increases significantly. RigFit is part of the flexible superposition software FlexS which can be accessed on the WWW [http://cartan.gmd.de/FlexS].     

A novel subshape molecular descriptor

Molecules with similar shapes and features often have similar biological activity. Several computational approaches search chemical databases for new leads or templates based on overall molecular shape similarity. However, active molecules often present critical subshapes that are required for binding, which may be missed by comparing overall shape similarity. We present a new approach to compare molecular shapes of different sizes and to calculate subshape similarity. We developed a skeletal representation of the shape which is topologically unrelated to covalent chemical connectivity. This simplifies rotational and translational sampling. We test initial possible alignments by matching similar triangles. This triangle-matching filter rapidly eliminates most geometrically impossible matches. Surviving matches are filtered further in successive stages. These stages involve direction, feature, and shape matching procedures. Our approach is applied to several situations demonstrating lead discovery and evolution.     

SHAFTS: a hybrid approach for 3D molecular similarity calculation. 1. Method and assessment of virtual screening

We developed a novel approach called SHAFTS (SHApe-FeaTure Similarity) for 3D molecular similarity calculation and ligand-based virtual screening. SHAFTS adopts a hybrid similarity metric combined with molecular shape and colored (labeled) chemistry groups annotated by pharmacophore features for 3D similarity calculation and ranking, which is designed to integrate the strength of pharmacophore matching and volumetric overlay approaches. A feature triplet hashing method is used for fast molecular alignment poses enumeration, and the optimal superposition between the target and the query molecules can be prioritized by calculating corresponding "hybrid similarities". SHAFTS is suitable for large-scale virtual screening with single or multiple bioactive compounds as the query "templates" regardless of whether corresponding experimentally determined conformations are available. Two public test sets (DUD and Jain's sets) including active and decoy molecules from a panel of useful drug targets were adopted to evaluate the virtual screening performance. SHAFTS outperformed several other widely used virtual screening methods in terms of enrichment of known active compounds as well as novel chemotypes, thereby indicating its robustness in hit compounds identification and potential of scaffold hopping in virtual screening.     

Introduction of the conditional correlated Bernoulli model of similarity value distributions and its application to the prospective prediction of fingerprint search performance

A statistical approach named the conditional correlated Bernoulli model is introduced for modeling of similarity scores and predicting the potential of fingerprint search calculations to identify active compounds. Fingerprint features are rationalized as dependent Bernoulli variables and conditional distributions of Tanimoto similarity values of database compounds given a reference molecule are assessed. The conditional correlated Bernoulli model is utilized in the context of virtual screening to estimate the position of a compound obtaining a certain similarity value in a database ranking. Through the generation of receiver operating characteristic curves from cumulative distribution functions of conditional similarity values for known active and random database compounds, one can predict how successful a fingerprint search might be. The comparison of curves for different fingerprints makes it possible to identify fingerprints that are most likely to identify new active molecules in a database search given a set of known reference molecules.     

Homology model-based virtual screening for GPCR ligands using docking and target-biased scoring

The current study investigates the combination of two recently reported techniques for the improvement of homology model-based virtual screening for G-protein coupled receptor (GPCR) ligands. First, ligand-supported homology modeling was used to generate receptor models that were in agreement with mutagenesis data and structure-activity relationship information of the ligands. Second, interaction patterns from known ligands to the receptor were applied for scoring and rank ordering compounds from a virtual library using ligand-receptor interaction fingerprint-based similarity (IFS). Our approach was evaluated in retrospective virtual screening experiments for antagonists of the metabotropic glutamate receptor (mGluR) subtype 5. The results of our approach were compared to the results obtained by conventional scoring functions (Dock-Score, PMF-Score, Gold-Score, ChemScore, and FlexX-Score). The IFS lead to significantly higher enrichment rates, relative to the competing scoring functions. Though using a target-biased scoring approach, the results were not biased toward the chemical classes of the reference structures. Our results indicate that the presented approach has the potential to serve as a general setup for successful structure-based GPCR virtual screening.     

New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.     

Identification of New Potential Inhibitors of Quorum Sensing through a Specialized Multi-Level Computational Approach

Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self-produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum-sensing (QS), an important process of cell-to-cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum-sensing, using CviR—the quorum-sensing receptor from Chromobacterium violaceum—as a model target. This in silico approach combines protein-ligand docking (with 7 different docking programs/scoring functions), receptor-based virtual screening, molecular dynamic simulations, and free energy calculations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target-specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA-Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repurposing towards QS inhibition.     

Theoretical approach to the pharmacophoric pattern of GABAB analogs

In order to determine the structural requirements that are important for GABA_B binding affinity, a quantum-chemical-based conformational study has been performed, followed by a similarity analysis which includes 12 GABA_B analogs. Due to the flexibility of the structures, a semigrid GABA_B analog [2RS-(5,5-dimethyl) morpholinyl-acetic acid] has been used as a template for the amonium moiety in order to help to identify the active conformation. Both in vacuo, and solvent-simulated calculations, for the physiological media modeled as water molecules, have been compared, for this analog, at ab initio (G94, 6-31+G(d,p)) and semiempirical (PM3) levels, respectively. On the basis of this comparison, the results of in vacuo PM3 calculations have been chosen for the similarity analysis. We have included, in the calculations, a group of molecules heterogeneous enough to become representative of the different families that can bind to the GABA_B receptor site. Following their comparison we report the leading characteristics that can be related to their binding capability and define a pharmacophoric pattern for GABA_B analogs. The latter is compared with the one previously found for the binding affinity at the GABA_A receptor site. © 1998 John Wiley & Sons, Inc. Int J Quant Chem 70: 1195–1208, 1998