Molecular Similarity Calculations Research Articles

ROSHAMBO: Open-Source Molecular Alignment and 3D Similarity Scoring.

Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and offers an approach complementary to structure-based virtual screening. Our package employs the PAPER software for optimizing molecular alignments based on Gaussian volume overlaps. GPU acceleration is utilized to significantly reduce computational time and resource requirements. After obtaining the optimal alignments between the target and the query molecules, both shape and color (based on pharmacophore features) scores are computed to assess molecular similarity, with aligned molecules optionally being output in sdf format. The package was benchmarked using the DUDE-Z public data sets. Results demonstrated the package's near-state-of-the-art performance and robustness across multiple target classes, with speed that enables many routine ligand-based drug discovery workflows. As an open-source and freely available resource (github.com/molecularinformatics/roshambo) with both a convenient Python API and command line interface, our package also addresses the need for accessible and efficient virtual screening tools in drug discovery.

Comprehensive Similarity Algorithm and Molecular Dynamics Simulation-Assisted Terahertz Spectroscopy for Intelligent Matching Identification of Quorum Signal Molecules (N-Acyl-Homoserine Lactones).

To investigate the mechanism of aquatic pathogens in quorum sensing (QS) and decode the signal transmission of aquatic Gram-negative pathogens, this paper proposes a novel method for the intelligent matching identification of eight quorum signaling molecules (N-acyl-homoserine lactones, AHLs) with similar molecular structures, using terahertz (THz) spectroscopy combined with molecular dynamics simulation and spectral similarity calculation. The THz fingerprint absorption spectral peaks of the eight AHLs were identified, attributed, and resolved using the density functional theory (DFT) for molecular dynamics simulation. To reduce the computational complexity of matching recognition, spectra with high peak matching values with the target were preliminarily selected, based on the peak position features of AHL samples. A comprehensive similarity calculation (CSC) method using a weighted improved Jaccard similarity algorithm (IJS) and discrete Fréchet distance algorithm (DFD) is proposed to calculate the similarity between the selected spectra and the targets, as well as to return the matching result with the highest accuracy. The results show that all AHL molecular types can be correctly identified, and the average quantization accuracy of CSC is 98.48%. This study provides a theoretical and data-supported foundation for the identification of AHLs, based on THz spectroscopy, and offers a new method for the high-throughput and automatic identification of AHLs.

Mebendazole's Conformational Space and Its Predicted Binding to Human Heat-Shock Protein 90.

Recent experimental evidence suggests that mebendazole, a popular antiparasitic drug, binds to heat shock protein 90 (Hsp90) and inhibits acute myeloid leukemia cell growth. In this study we use quantum mechanics (QM), molecular similarity, and molecular dynamics (MD) calculations to predict possible binding poses of mebendazole to the adenosine triphosphate (ATP) binding site of Hsp90. Extensive conformational searches and minimization of the five mebendazole tautomers using the MP2/aug-cc-pVTZ theory level resulted in 152 minima. Mebendazole-Hsp90 complex models were subsequently created using the QM optimized conformations and protein coordinates obtained from experimental crystal structures that were chosen through similarity calculations. Nine different poses were identified from a total of 600 ns of explicit solvent, all-atom MD simulations using two different force fields. All simulations support the hypothesis that mebendazole is able to bind to the ATP binding site of Hsp90.

Computational Approach to Combat COVID-19 Infection: Emerging Tools for Accelerating Drug Research.

The process of drug discovery and development is expensive, complex, timeconsuming, and risky. There are different techniques involved in the process of drug development, including random screening, computational approaches, molecular manipulation, and serendipitous research. Among these methods, the computational approach is considered an efficient strategy to accelerate and economize the drug discovery process. This approach is mainly applied in various phases of the drug discovery process, including target identification, target validation, lead identification, and lead optimization. Due to the increase in the availability of information regarding various biological targets of different disease states, computational approaches such as molecular docking, de novo design, molecular similarity calculation, virtual screening, pharmacophore-based modeling, and pharmacophore mapping have been applied extensively. Various drug molecules can be designed by applying computational tools to explore the drug candidates for the treatment of Coronavirus infection. The World Health Organization announced the coronavirus disease as COVID-19 and declared it a global pandemic on 11 February 2020. Therefore, it is thought of interest to the scientific community to apply computational methods to design and optimize the pharmacological properties of various clinically available and FDA-approved drugs such as remdesivir, ribavirin, favipiravir, oseltamivir, ritonavir, arbidol, chloroquine, hydroxychloroquine, carfilzomib, baraticinib, prulifloxacin, etc., for effective treatment of COVID-19 infection. Further, various survey reports suggest that extensive studies are carried out by various research communities to find out the safety and efficacy profile of these drug candidates. This review is focused on the study of various aspects of these drugs related to their target sites on the virus, binding interactions, physicochemical properties, etc.

Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure.

Extended reduced graphs provide summary representations of chemical structures using pharmacophore-type node descriptions to encode the relevant molecular properties. Commonly used similarity measures using reduced graphs convert these graphs into 2D vectors like fingerprints, before chemical comparisons are made. This study investigates the effectiveness of a graph-only driven molecular comparison by using extended reduced graphs along with graph edit distance methods for molecular similarity calculation as a tool for ligand-based virtual screening applications, which estimate the bioactivity of a chemical on the basis of the bioactivity of similar compounds. The results proved to be very stable and the graph editing distance method performed better than other methods previously used on reduced graphs. This is exemplified with six publicly available data sets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were also used. In the experiments, our method performed better than other molecular similarity methods which use array representations in most cases. Overall, it is shown that extended reduced graphs along with graph edit distance is a combination of methods that has numerous applications and can identify bioactivity similarities in a structurally diverse group of molecules.

ESHAFTS: Integrated and graphical drug design software based on 3D molecular similarity.

With the development of computer technology, computer-aided drug design (CADD) has become an important means for drug research and development, and its representative method is virtual screening. Various virtual screening platforms have emerged in an endless stream and play great roles in the field of drug discovery. With the increasing number of compound molecules, virtual screening platforms face two challenges: low fluency and low visibility of software operations. In this article, we present an integrated and graphical drug design software eSHAFTS based on three-dimensional (3D) molecular similarity to overcome these shortcomings. Compared with other software, eSHAFTS has four main advantages, which are integrated molecular editing and drawing, interactive loading and analysis of proteins, multithread and multimode 3D molecular similarity calculation, and multidimensional information visualization. Experiments have verified its convenience, usability, and reliability. By using eSHAFTS, various tasks can be submitted and visualized in one desktop software without locally installing any dependent plug-ins or software. The software installation package can be downloaded for free at http://lilab.ecust.edu.cn/home/resource.html. © 2019 Wiley Periodicals, Inc.

Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm

Natural products represent a prominent source of pharmaceutically and industrially important agents. Calculating the chemical similarity of two molecules is a central task in cheminformatics, with applications at multiple stages of the drug discovery pipeline. Quantifying the similarity of natural products is a particularly important problem, as the biological activities of these molecules have been extensively optimized by natural selection. The large and structurally complex scaffolds of natural products distinguish their physical and chemical properties from those of synthetic compounds. However, no analysis of the performance of existing methods for molecular similarity calculation specific to natural products has been reported to date. Here, we present LEMONS, an algorithm for the enumeration of hypothetical modular natural product structures. We leverage this algorithm to conduct a comparative analysis of molecular similarity methods within the unique chemical space occupied by modular natural products using controlled synthetic data, and comprehensively investigate the impact of diverse biosynthetic parameters on similarity search. We additionally investigate a recently described algorithm for natural product retrobiosynthesis and alignment, and find that when rule-based retrobiosynthesis can be applied, this approach outperforms conventional two-dimensional fingerprints, suggesting it may represent a valuable approach for the targeted exploration of natural product chemical space and microbial genome mining. Our open-source algorithm is an extensible method of enumerating hypothetical natural product structures with diverse potential applications in bioinformatics.

Molecular similarity-based predictions of the Tox21 screening outcome

To assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo testing for many toxic endpoints, resulting in millions of animal experiments conducted each year. However, following the Replace, Reduce, Refine (3R) principle, the development and optimization of alternative methods, in particular in silico methods, has been put into focus in the recent years. It is generally acknowledged that the more complex a toxic endpoint, the more difficult it is to model. Therefore, computational toxicology is shifting from modelling general and complex endpoints to the investigation and modelling of pathways of toxicity and the underlying molecular effects. The U.S. Toxicology in the 21st Century (Tox21) initiative has screened a large library of compounds, including approximately 10K environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects, and made the results publicly available. Through the Tox21 Data Challenge, the consortium has established a platform for computational toxicologists to develop and validate their predictive models. Here, we present a fast and successful method for the prediction of different outcomes of the nuclear receptor and stress response pathway screening from the Tox21 Data Challenge 2014. The method is based on the combination of molecular similarity calculations and a naïve Bayes machine learning algorithm and has been implemented as a KNIME pipeline. Molecules are represented as binary vectors consisting of a concatenation of common two-dimensional molecular fingerprint types with topological compound properties. The prediction method has been optimized individually for each modelled target and evaluated in a cross-validation as well as with the independent Tox21 validation set. Our results show that the method can achieve good prediction accuracies and rank among the top algorithms submitted to the prediction challenge, indicating its broad applicability in toxicity prediction.

Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

BackgroundCheminformaticians are equipped with a very rich toolbox when carrying out molecular similarity calculations. A large number of molecular representations exist, and there are several methods (similarity and distance metrics) to quantify the similarity of molecular representations. In this work, eight well-known similarity/distance metrics are compared on a large dataset of molecular fingerprints with sum of ranking differences (SRD) and ANOVA analysis. The effects of molecular size, selection methods and data pretreatment methods on the outcome of the comparison are also assessed.ResultsA supplier database (https://mcule.com/) was used as the source of compounds for the similarity calculations in this study. A large number of datasets, each consisting of one hundred compounds, were compiled, molecular fingerprints were generated and similarity values between a randomly chosen reference compound and the rest were calculated for each dataset. Similarity metrics were compared based on their ranking of the compounds within one experiment (one dataset) using sum of ranking differences (SRD), while the results of the entire set of experiments were summarized on box and whisker plots. Finally, the effects of various factors (data pretreatment, molecule size, selection method) were evaluated with analysis of variance (ANOVA).ConclusionsThis study complements previous efforts to examine and rank various metrics for molecular similarity calculations. Here, however, an entirely general approach was taken to neglect any a priori knowledge on the compounds involved, as well as any bias introduced by examining only one or a few specific scenarios. The Tanimoto index, Dice index, Cosine coefficient and Soergel distance were identified to be the best (and in some sense equivalent) metrics for similarity calculations, i.e. these metrics could produce the rankings closest to the composite (average) ranking of the eight metrics. The similarity metrics derived from Euclidean and Manhattan distances are not recommended on their own, although their variability and diversity from other similarity metrics might be advantageous in certain cases (e.g. for data fusion). Conclusions are also drawn regarding the effects of molecule size, selection method and data pretreatment on the ranking behavior of the studied metrics.Graphical A visual summary of the comparison of similarity metrics with sum of ranking differences (SRD).Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0069-3) contains supplementary material, which is available to authorized users.

Quantifying the Fingerprint Descriptor Dependence of Structure–Activity Relationship Information on a Large Scale

It is well-known that different molecular representations, e.g., graphs, numerical descriptors, fingerprints, or 3D models, change the numerical results of molecular similarity calculations. Because the assessment of structure-activity relationships (SARs) requires similarity and potency comparisons of active compounds, this representation dependence inevitably also affects SAR analysis. But to what extent? How exactly does SAR information change when alternative fingerprints are used as descriptors? What is the proportion of active compounds with substantial changes in SAR information induced by different fingerprints? To provide answers to these questions, we have quantified changes in SAR information across many different compound classes using six different fingerprints. SAR profiling was carried out on 128 target-based data sets comprising more than 60,000 compounds with high-confidence activity annotations. A numerical measure of SAR discontinuity was applied to assess SAR information on a per compound basis. For ~70% of all test compounds, changes in SAR characteristics were detected when different fingerprints were used as molecular representations. Moreover, the SAR phenotype of ~30% of the compounds changed, and distinct fingerprint-dependent local SAR environments were detected. The fingerprints we compared were found to generate SAR models that were essentially not comparable. Atom environment and pharmacophore fingerprints produced the largest differences in compound-associated SAR information. Taken together, the results of our systematic analysis reveal larger fingerprint-dependent changes in compound-associated SAR information than would have been anticipated.

Incorporating Green Chemistry Concepts into Mobile Chemistry Applications and Their Potential Uses

Green Chemistry related information is generally proprietary, and papers on the topic are commonly behind pay walls that limit their accessibility. Several new mobile applications (apps) have been recently released for the Apple iOS platform, which incorporate green chemistry concepts. Because of the large number of people who now own a mobile device across all demographics, this population represents a highly novel way to communicate green chemistry, which has not previously been appreciated. We have made the American Chemical Society Green Chemistry Institute (ACS GCI) Pharmaceutical Roundtable Solvent Selection Guide more accessible and have increased its visibility by creating a free mobile app for the Apple iOS platform called Green Solvents. We have also used this content for molecular similarity calculations using additional solvents to predict potential environmental and health categories, which could help in solvent selection. This approach predicted the correct waste or health class for over 60%...

Softened electrostatic molecular potentials

Electrostatic molecular potentials (EMPs) are studied from two points of view. First, a softened EMP (SEMP) approach is proposed, consisting in the substitution of a positive point charge as the entity with which an electronic density function (DF) interacts electrostatically to generate a classical EMP for a Gaussian charge distribution. Second, the performance of this SEMP approach under the Atomic Shell Approximation (ASA) is described and compared with classical EMP at the same ASA level. Several sample applications are presented to describe the general features of this new method of studying electrostatic interactions in molecules. The net result is a family of SEMPs that encompass EMPs as special cases but do not possess their infinite discontinuities. The features of SEMPs are quite similar to those of EMPs distant from nuclei, and the absence of infinity values makes them good candidates to be employed in molecular similarity calculations.

Computational drug discovery.

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.

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.

Accelerating Two Algorithms for Large-Scale Compound Selection on GPUs

Compound selection procedures based on molecular similarity and diversity are widely used in drug discovery. Current algorithms are often time consuming when applied to very large compound sets. This paper describes the acceleration of two selection algorithms (the leader and the spread algorithms) on graphical processing units (GPUs). We first parallelized the molecular similarity calculation based on Daylight fingerprints and the Tanimoto index and then implemented the two algorithms on GPU hardware using the open source Thrust library. Experiments show that the GPU leader algorithm is 73-120 times faster than the CPU version, and the GPU spread algorithm is 78-143 times faster than the CPU version.

ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics

We present ElectroShape, a novel ligand-based virtual screening method, that combines shape and electrostatic information into a single, unified framework. Building on the ultra-fast shape recognition (USR) approach for fast non-superpositional shape-based virtual screening, it extends the method by representing partial charge information as a fourth dimension. It also incorporates the chiral shape recognition (CSR) method, which distinguishes enantiomers. It has been validated using release 2 of the Directory of useful decoys (DUD), and shows a near doubling in enrichment ratio at 1% over USR and CSR, and improvements as measured by Receiver Operating Characteristic curves. These improvements persisted even after taking into account the chemotype redundancy in the sets of active ligands in DUD. During the course of its development, ElectroShape revealed a difference in the charge allocation of the DUD ligand and decoy sets, leading to several new versions of DUD being generated as a result. ElectroShape provides a significant addition to the family of ultra-fast ligand-based virtual screening methods, and its higher-dimensional shape recognition approach has great potential for extension and generalisation.

Structuring Chemical Space: Similarity-Based Characterization of the PubChem Database.

The ensemble of conceivable molecules is referred to as the Chemical Space. In this article we describe a hierarchical version of the Affinity Propagation (AP) clustering algorithm and apply it to analyze the LINGO-based similarity matrix of a 500 000-molecule subset of the PubChem database, which contains more than 19 million compounds. The combination of two highly efficient methods, namely the AP clustering algorithm and LINGO-based molecular similarity calculations, allows the unbiased analysis of large databases. Hierarchical clustering generates a numerical diagonalization of the similarity matrix. The target-independent, intrinsic structure of the database , derived without any previous information on the physical or biological properties of the compounds, maps together molecules experimentally shown to bind the same biological target or to have similar physical properties.

Pattern recognition based on color-coded quantum mechanical surfaces for molecular alignment

A pattern recognition algorithm for the alignment of drug-like molecules has been implemented. The method is based on the calculation of quantum mechanical derived local properties defined on a molecular surface. This approach has been shown to be very useful in attempting to derive generalized, non-atom based representations of molecular structure. The visualization of these surfaces is described together with details of the methodology developed for their use in molecular overlay and similarity calculations. In addition, this paper also introduces an additional local property, the local curvature (C (L)), which can be used together with the quantum mechanical properties to describe the local shape. The method is exemplified using some problems representing common tasks encountered in molecular similarity.

Diversity Space and Its Application to Library Selection and Design

To promote more productive combinatorial endeavors, the Diversity Space methodology introduced here enables similarity comparisons at the library level. Particularly at an early screening stage, when little or no information is available regarding the pharmacophoric entities necessary for binding, it is more efficient to select or discard an entire ensemble of molecules at once, rather than focus on individual compounds. Also described are applications of the methodology to a form of scaffold hopping, herein categorized as "soft" scaffold hopping, and to a newly introduced approach called surrogate synthesis, both of which are furthered by library-level information that is absent in more traditional molecular similarity calculations.

The centroidal algorithm in molecular similarity and diversity calculations on confidential datasets

Chemical structure provides exhaustive description of a compound, but it is often proprietary and thus an impediment in the exchange of information. For example, structure disclosure is often needed for the selection of most similar or dissimilar compounds. Authors propose a centroidal algorithm based on structural fragments (screens) that can be efficiently used for the similarity and diversity selections without disclosing structures from the reference set. For an increased security purposes, authors recommend that such set contains at least some tens of structures. Analysis of reverse engineering feasibility showed that the problem difficulty grows with decrease of the screen's radius. The algorithm is illustrated with concrete calculations on known steroidal, quinoline, and quinazoline drugs. We also investigate a problem of scaffold identification in combinatorial library dataset. The results show that relatively small screens of radius equal to 2 bond lengths perform well in the similarity sorting, while radius 4 screens yield better results in diversity sorting. The software implementation of the algorithm taking SDF file with a reference set generates screens of various radii which are subsequently used for the similarity and diversity sorting of external SDFs. Since the reverse engineering of the reference set molecules from their screens has the same difficulty as the RSA asymmetric encryption algorithm, generated screens can be stored openly without further encryption. This approach ensures an end user transfers only a set of structural fragments and no other data. Like other algorithms of encryption, the centroid algorithm cannot give 100% guarantee of protecting a chemical structure from dataset, but probability of initial structure identification is very small-order of 10(-40) in typical cases.