Matched Molecular Pair Research Articles

Flexible Analog Search with Kernel PCA Embedded Molecule Vectors

Studying analog series to find structural transformations that enhance the activity and ADME properties of lead compounds is an important part of drug development. Matched molecular pair (MMP) search is a powerful tool for analog analysis that imitates researchers' ability to select pairs of compounds that differ only by small well-defined transformations. Abstraction is a challenge for existing MMP search algorithms, which can result in the omission of relevant, inexact MMPs, and inclusion of irrelevant, contextually dissimilar MMPs. In this work, we present a new method for MMP search that returns approximate results and enables flexible control over abstraction of contextual information. We illustrate the concepts and mechanics of our method with a series of exemplar MMP queries, and then benchmark search accuracy using MMPs found by fragment indexing. We show that we can search for MMPs in a context dependent manner, and accurately approximate context independent fragment index based MMP search over a range of fingerprint and dataset conditions. Our method can be used to search for pairwise correspondences among analog sets and bolster MMP datasets where data is missing or incomplete.

The use of matched molecular series networks for cross target structure activity relationship translation and potency prediction.

Matched molecular series (MMS) analysis is an extension of matched molecular pair (MMP) analysis where all of the MMPs belong to the same chemical series. An MMS within a biological assay is able to capture specific structure activity relationships resulting from chemical substitution at a single location in the molecule. Under this convention, an MMS has the ability to capture one specific interaction vector between the compounds in a series and their therapeutic target. MMS analysis has the potential to translate the SAR from one series to another even across different protein targets or assays. A significant limitation of this approach is the lack of chemical series with a sufficient number of overlapping fragments to establish a statistically strong SAR in most databases. This results in either an inability to perform MMS analysis altogether or a potentially high proportion of spurious matches from chance correlations when the MMS compound count is low. This paper presents the novel concept of an MMS Network, which captures the SAR relationships between a set of related MMSs and significantly enhances the performance of MMS analysis by reducing the number of spurious matches leading to the identification of unexpected and potentially transferable SAR across assays. The results of a full retrospective leave-one-out analysis and randomization simulation are provided, and examples of pharmaceutically relevant programs will be presented to demonstrate the potential of this method.

Fluorine multipolar interaction: Toward elucidating its energetics in binding recognition

Multipolar interactions have gained attention due to their roles in molecular recognition events of chemical and biological systems. Structural evidence suggests that the fluorine multipolar interaction is a favorable molecular interaction in the paradigm of protein-ligand recognition. With an aim to understand its propensity and energetics in ligand-protein assembly, we mined the “Pfizer protein-ligand X-ray structure database” with three-dimensional constraints for the presence of such interactions. A set of transformation rules were applied to generate their corresponding matched molecular pairs (MMPs) that bear hydrogen(s) in place of the interacting fluorine(s). Biological activities were retrieved from our internal data warehouse for the MMPs with and without the ability to form the multipolar interaction. On the basis of the observed potency differences we determined that the free energy gain associated with the fluorine multipolar interaction is relatively modest, ∼0.3–0.6kcal/mol, and the net impact on lipophilic efficiency (LipE) is essentially neutral. A general guideline for medicinal chemists has emerged from this study, enabling a more rational employment of this type of interaction in molecular design.

A multi-endpoint matched molecular pair (MMP) analysis of 6-membered heterocycles

Aromatic rings, ubiquitous in pharmaceutical compounds, are often exchanged with another ring during the optimization process of drug discovery. Inevitably, the preferred ring system for one endpoint may prove detrimental to another, thus necessitating a holistic, multiple endpoint optimization approach for finding the ideal replacement. Accordingly, we conducted an extensive matched molecular pair (MMP) analysis of common 6-membered aromatic rings across 4 endpoints critical for drug discovery (logD lipophilicity, microsomal metabolism, P-gp efflux and passive permeability). We also investigated the effect of context by considering the connecting atom. Heat maps were created as a simple yet comprehensive way to view and analyze the vast amount of interrelated data. Paired difference statistical tests were used to identify transforms with changes that were significantly different from zero. We conclude that the heat maps of transforms provide a unique and powerful approach for multiparameter optimization.

Hierarchical Analysis of Bioactive Matched Molecular Pairs, Encoded Chemical Transformations, and Associated Substructures

The matched molecular pair (MMP) concept has become popular to assess molecular property changes resulting from small chemical modifications and study structure-activity relationships (SARs). In this study, we further extend MMP analysis by introducing an MMP-based hierarchical analysis scheme. Specifically, we report a large-scale analysis of MMPs derived from bioactive compounds following a defined "MMP-transformation-substructure" hierarchy. This makes it also possible to categorize transformations and corresponding substructures on the basis of activity information. MMPs were systematically generated for compounds active against current pharmaceutical targets and stepwise decomposed into transformations and substructures. Surprisingly, most chemical transformations were only associated with single MMPs. Hence, the structural context of transformations was unexpectedly narrow. In addition, nearly half of all substructures were found to exclusively form single-target transformations. Taken together, the results of our analysis provide a detailed view of MMP-transformation-substructure hierarchy and further increase the knowledge base of the MMP approach.

Mining Chromatographic Enantioseparation Data Using Matched Molecular Pair Analysis.

We apply matched molecular pair (MMP) analysis to data from ChirBase, which contains literature reports of chromatographic enantioseparations. For the 19 chiral stationary phases we examined, we were able to identify 289 sets of pairs where there is a statistically significant and consistent difference in enantioseparation due to a small chemical change. In many cases these changes highlight enantioselectivity differences between pairs or small families of closely related molecules that have for many years been used to probe the mechanisms of chromatographic chiral recognition; for example, the comparison of N-H vs. N-Me analytes to determine the criticality of an N-H hydrogen bond in chiral molecular recognition. In other cases, statistically significant MMPs surfaced by the analysis are less familiar or somewhat puzzling, sparking a need to generate and test hypotheses to more fully understand. Consequently, mining of appropriate datasets using MMP analysis provides an important new approach for studying and understanding the process of chromatographic enantioseparation.

Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression.

Activity cliffs (ACs) are formed by structurally similar compounds with large differences in activity. Accordingly, ACs are of high interest for the exploration of structure-activity relationships (SARs). ACs reveal small chemical modifications that result in profound biological effects. The ability to foresee such small chemical changes with significant biological consequences would represent a major advance for drug design. Nevertheless, only few attempts have been made so far to predict whether a pair of analogues is likely to represent an AC-and even fewer went further to quantitatively predict how "deep" a cliff might be. This might be due to the fact that such predictions must focus on compound pairs. Matched molecular pairs (MMPs), defined as pairs of structural analogs that are only distinguished by a chemical modification at a single site, are a preferred representation of ACs. Herein, we report new strategies for AC prediction that are based upon two different approaches: (i) condensed graphs of reactions, which were originally introduced for modeling of chemical reactions and were here adapted to encode MMPs, and, (ii) plain descriptor recombination-a strategy used for quantitative structure-property relationship (QSPR) modeling of nonadditive mixtures (MQSPR). By applying these concepts, ACs were encoded as single descriptor vectors used as input for support vector machine (SVM) classification and support vector regression (SVR), yielding accurate predictions of AC status (i.e., cliff vs noncliff) and potency differences, respectively. The latter were predicted in a compound order-sensitive manner returning the signed value of expected potency differences between AC compounds.

Computational Method for the Systematic Identification of Analog Series and Key Compounds Representing Series and Their Biological Activity Profiles.

A computational methodology is introduced for detecting all unique series of analogs in large compound data sets, regardless of chemical relationships between analogs. No prior knowledge of core structures or R-groups is required, which are automatically determined. The approach is based upon the generation of retrosynthetic matched molecular pairs and analog networks from which distinct series are isolated. The methodology was applied to systematically extract more than 17 000 distinct series from the ChEMBL database. For comparison, analog series were also isolated from screening compounds and drugs. Known biological activities were mapped to series from ChEMBL, and in more than 13 000 of these series, key compounds were identified that represented substitution sites of all analogs within a series and its complete activity profile. The analog series, key compounds, and activity profiles are made freely available as a resource for medicinal chemistry applications.

Cheminformatics analysis of the AR agonist and antagonist datasets in PubChem.

Background As one of the largest publicly accessible databases for hosting chemical structures and biological activities, PubChem has been processing bioassay submissions from the community since 2004. With the increase in volume for the deposited data in PubChem, the diversity and wealth of information content also grows. Recently, the Tox21 program, has deposited a series of pairwise data in PubChem regarding to different mechanism of actions (MOA), such as androgen receptor (AR) agonist and antagonist datasets, to study cell toxicity. To the best of our knowledge, little work has been reported from cheminformatics study for these especially pairwise datasets, which may provide insight into the mechanism of actions of the compounds and relationship between chemical structures and functions, as well as guidance for lead compound selection and optimization. Thus, to fill the gap, we performed a comprehensive cheminformatics analysis, including scaffold analysis, matched molecular pair (MMP) analysis as well as activity cliff analysis to investigate the structural characteristics and discontinued structure–activity relationship of the individual dataset (i.e., AR agonist dataset or AR antagonist dataset) and the combined dataset (i.e., the common compounds between the AR agonist and antagonist datasets).ResultsScaffolds associated only with potential agonists or antagonists were identified. MMP-based activity cliffs, as well as a small group of compounds with dual MOA reported were recognized and analyzed. Moreover, MOA-cliff, a novel concept, was proposed to indicate one pair of structurally similar molecules which exhibit opposite MOA.ConclusionsCheminformatics methods were successfully applied to the pairwise AR datasets and the identified molecular scaffold characteristics, MMPs as well as activity cliffs might provide useful information when designing new lead compounds for the androgen receptor.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0150-6) contains supplementary material, which is available to authorized users.

Discovery of InhA inhibitors with anti-mycobacterial activity through a matched molecular pair approach

The Mycobacterium tuberculosis (M. tuberculosis) enoyl-acyl carrier protein reductase (mtInhA) is an attractive enzyme and a thoroughly studied target for tuberculosis therapy. In this study, to identify novel structure-activity relationships (SARs) of mtInhA inhibitors, a series of diphenyl ether derivatives were designed based on the matched molecular pair (MMP) method, and the binding energies of these compounds were subsequently estimated by in silico structure-based drug screening (SBDS) to provide more useful data. Consequently, the 10 unique candidate compounds (KEM1-KEM10) were identified and assessed for the inhibition of mtInhA enzymatic activity, in vitro antibiotic effects against model mycobacteria and toxicity level on both intestinal bacteria and mammalian cells. Among the compounds tested, phenyl group (KEM4) and 2-fluorobenzyl group (KEM7) substitutions produced preferable inhibitory effects on mtInhA enzymatic activity relative to those provided by a furyl group (KES4: base compound) at the terminal of the compound, and KEM7 inhibited the growth of the mycobacteria strain with a lower IC50 value. Moreover, most of the candidate compounds exhibited neither inhibition of the growth of enterobacteria nor toxic effects on mammalian cells, though KEM10 exhibited toxicity against cultured MDCK cells. The structural and experimental information concerning these mtInhA inhibitors identified through MMP-based in silico screening will likely contribute to the lead optimisation of novel antibiotics for M. tuberculosis.

Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process.

BackgroundQSAR is an established and powerful method for cheap in silico assessment of physicochemical properties and biological activities of chemical compounds. However, QSAR models are rather complex mathematical constructs that cannot easily be interpreted. Medicinal chemists would benefit from practical guidance regarding which molecules to synthesize.Another possible approach is analysis of pairs of very similar molecules, so-called matched molecular pairs (MMPs). Such an approach allows identification of molecular transformations that affect particular activities (e.g. toxicity). In contrast to QSAR, chemical interpretation of these transformations is straightforward. Furthermore, such transformations can give medicinal chemists useful hints for the hit-to-lead optimization process.ResultsThe current study suggests a combination of QSAR and MMP approaches by finding MMP transformations based on QSAR predictions for large chemical datasets. The study shows that such an approach, referred to as prediction-driven MMP analysis, is a useful tool for medicinal chemists, allowing identification of large numbers of “interesting” transformations that can be used to drive the molecular optimization process. All the methodological developments have been implemented as software products available online as part of OCHEM (http://ochem.eu/).ConclusionsThe prediction-driven MMPs methodology was exemplified by two use cases: modelling of aquatic toxicity and CYP3A4 inhibition. This approach helped us to interpret QSAR models and allowed identification of a number of “significant” molecular transformations that affect the desired properties. This can facilitate drug design as a part of molecular optimization process.Graphical Molecular matched pairs and transformation graphs facilitate interpretable molecular optimisation process.

OOMMPPAA: a tool to aid directed synthesis by the combined analysis of activity and structural data.

There is an ever increasing resource in terms of both structural information and activity data for many protein targets. In this paper we describe OOMMPPAA, a novel computational tool designed to inform compound design by combining such data. OOMMPPAA uses 3D matched molecular pairs to generate 3D ligand conformations. It then identifies pharmacophoric transformations between pairs of compounds and associates them with their relevant activity changes. OOMMPPAA presents this data in an interactive application providing the user with a visual summary of important interaction regions in the context of the binding site. We present validation of the tool using openly available data for CDK2 and a GlaxoSmithKline data set for a SAM-dependent methyl-transferase. We demonstrate OOMMPPAA’s application in optimizing both potency and cell permeability and use OOMMPPAA to highlight nuanced and cross-series SAR. OOMMPPAA is freely available to download at http://oommppaa.sgc.ox.ac.uk/OOMMPPAA/.

Prediction of compound potency changes in matched molecular pairs using support vector regression.

Matched molecular pairs (MMPs) consist of pairs of compounds that are transformed into each other by a substructure exchange. If MMPs are formed by compounds sharing the same biological activity, they encode a potency change. If the potency difference between MMP compounds is very small, the substructure exchange (chemical transformation) encodes a bioisosteric replacement; if the difference is very large, the transformation encodes an activity cliff. For a given compound activity class, MMPs comprehensively capture existing structural relationships and represent a spectrum of potency changes for structurally analogous compounds. We have aimed to predict potency changes encoded by MMPs. This prediction task principally differs from conventional quantitative structure-activity relationship (QSAR) analysis. For the prediction of MMP-associated potency changes, we introduce direction-dependent MMPs and combine MMP analysis with support vector regression (SVR) modeling. Combinations of newly designed kernel functions and fingerprint descriptors are explored. The resulting SVR models yield accurate predictions of MMP-encoded potency changes for many different data sets. Shared key structure context is found to contribute critically to prediction accuracy. SVR models reach higher performance than random forest (RF) and MMP-based averaging calculations carried out as controls. A comparison of SVR with kernel ridge regression indicates that prediction accuracy has largely been a consequence of kernel characteristics rather than SVR optimization details.

Three decades of structure- and property-based molecular design.

Roche has pioneered structure- and property-based molecular design to drug discovery. While this is an ongoing development, the past three decades feature key events that have revolutionized the way drug discovery is conducted in Big Pharma industry. It has been a great privilege to have been involved in this transformation process, to have been able to collaborate with, direct, guide, or simply encourage outstanding experts in various disciplines to build and further develop what has become a major pillar of modern small-molecule drug discovery. This article is an account of major events that took place since the early decision of Roche to implement computer-assisted molecular modeling 32 years ago and is devoted to the key players involved. It highlights the internal build-up of structural biology, with protein X-ray structure determination at its core, and the early setup of bioinformatics. It describes the strategic shift to large compound libraries and high-throughput screening with the development of novel compound storage and ultra-high-throughput screening facilities, as well as the strategic return to focused screening of small motif-based compound libraries. These developments were accompanied by the rise of miniaturized parallel compound property analytics which resulted in a major paradigm shift in medicinal chemistry from linear to multi-dimensional lead optimization. The rapid growth of huge collections of property data stimulated the development of various novel data mining concepts with 'matched molecular pair' analysis and novel variants thereof playing crucial roles. As compound properties got more prominent in molecular design, exploration of specific structural motifs for property modulation became a research activity complementary to target-oriented medicinal chemistry. The exploration of oxetane is given as an example. For the sake of brevity, this account cannot detail all further developments that have taken place in each individual area of structure- and property-based drug discovery and it can only hint at important developments in other disciplines that have equally contributed to major paradigm shifts in Roche's small-molecule drug discovery efforts.

The 'SAR Matrix' method and its extensions for applications in medicinal chemistry and chemogenomics.

We describe the 'Structure-Activity Relationship (SAR) Matrix' (SARM) methodology that is based upon a special two-step application of the matched molecular pair (MMP) formalism. The SARM method has originally been designed for the extraction, organization, and visualization of compound series and associated SAR information from compound data sets. It has been further developed and adapted for other applications including compound design, activity prediction, library extension, and the navigation of multi-target activity spaces. The SARM approach and its extensions are presented here in context to introduce different types of applications and provide an example for the evolution of a computational methodology in pharmaceutical research.

The ‘SAR Matrix’ method and its extensions for applications in medicinal chemistry and chemogenomics

We describe the 'Structure-Activity Relationship (SAR) Matrix' (SARM) methodology that is based upon a special two-step application of the matched molecular pair (MMP) formalism. The SARM method has originally been designed for the extraction, organization, and visualization of compound series and associated SAR information from compound data sets. It has been further developed and adapted for other applications including compound design, activity prediction, library extension, and the navigation of multi-target activity spaces. The SARM approach and its extensions are presented here in context to introduce different types of applications and provide an example for the evolution of a computational methodology in pharmaceutical research.

Quantitative Structure–Activity Relationship Models of Chemical Transformations from Matched Pairs Analyses

The concepts of activity cliffs and matched molecular pairs (MMP) are recent paradigms for analysis of data sets to identify structural changes that may be used to modify the potency of lead molecules in drug discovery projects. Analysis of MMPs was recently demonstrated as a feasible technique for quantitative structure-activity relationship (QSAR) modeling of prospective compounds. Although within a small data set, the lack of matched pairs, and the lack of knowledge about specific chemical transformations limit prospective applications. Here we present an alternative technique that determines pairwise descriptors for each matched pair and then uses a QSAR model to estimate the activity change associated with a chemical transformation. The descriptors effectively group similar transformations and incorporate information about the transformation and its local environment. Use of a transformation QSAR model allows one to estimate the activity change for novel transformations and therefore returns predictions for a larger fraction of test set compounds. Application of the proposed methodology to four public data sets results in increased model performance over a benchmark random forest and direct application of chemical transformations using QSAR-by-matched molecular pairs analysis (QSAR-by-MMPA).

Fuzzy Matched Pairs: A Means To Determine the Pharmacophore Impact on Molecular Interaction

Within this work, a methodological extension of the matched molecular pair analysis is presented. The method is based on a pharmacophore retyping of the molecular graph and a consecutive matched molecular pair analysis. The features of the new methodology are exemplified using a large data set on CYP inhibition. We show that Fuzzy Matched Pairs can be used to extract activity and selectivity determining pharmacophoric features. Based on the fuzzy pharmacophore description, the method clusters molecular transfers and offers new opportunities for the combination of data from different sources, namely public and industry datasets.

Systematic Identification of Matching Molecular Series and Mapping of Screening Hits.

Matching molecular series (MMS) have originally been introduced as an extension of the matched molecular pair (MMP) concept to facilitate the design of substructure-based structure-activity relationship (SAR) networks. An MMP is defined as a pair of compounds that only differ by a structural change at a single site. In addition, an MMS is defined as an MMP-based series of compounds that have a conserved structural core and are distinguished by modifications at a single site. Systematic generation of MMS from specifically active compounds generalizes the search for series of structural analogs. Potency-ordered MMS provide series associated with SAR information. We have systematically extracted MMS from publicly available compounds with well-defined activity measurements and generated a large database with approx. 40 000 single- and 13 600 multi-target series, which provide a rich source of SAR information. As an application, we introduce MMP-based mapping of screening hits to MMS to search for initial SAR information and determine all SAR environments available for such hits. The MMS database is made freely available to the scientific community.

Fuzzy context specific matched molecular pairs

Matched molecular pairs (MMPs) are commonly used to assess the importance of chemical modifications on small molecules versus a particular property [1]. One major advantage of MMPs is the direct interpretation by medicinal chemists. Despite the popularity of MMPs several drawbacks hinder their systematic applications in drug discovery projects. First, in order to derive sufficiently representative statistics for extracting design rules rather large datasets of molecules and activities are needed. Second, MMPs are often used without context specificity, likely because of the lack of sufficient data. This results in weak statistics. One transformation could result to be beneficial, but is detrimental in reality once the context taken into account [2]. Context specific MMP analysis is clearly advantageous for in depth understanding of SAR, but hindered due to lack of data. Here we present a novel methodology for providing robust statistics for fuzzy context specific MMPs even on medium sized data-sets. Molecules are transformed to a reduced graph using the Discngine Pharmacophore Graph methodology [3], a molecular graph reduction method closely related to classical reduced graphs [4]. Next, molecular fragmentation and MMP detection is directly performed on the pharmacophore graph. The herein used pharmacophore graph representation allows to group together very similar contexts and/or fragments and thus increase population size compared to classical MMP analysis. Validation of the fuzzy context specific MMP (fcsMMP) is presented and outcome is compared to classical MMP analysis. Last the Discngine Network framework is used to organize the derived design rules for efficient large scale mining and results extraction in a real world Med Chem context and applications are shown.