Target family-directed exploration of scaffolds with different SAR profiles

The scaffold concept is widely applied in chemoinformatics and medicinal chemistry to organize bioactive compounds according to common core structures or associate compound classes with specific biological activities. A variety of scaffold analyses have been carried out to derive statistics for scaffold distributions, generate structural organization schemes, or identify scaffolds that preferentially occur in given compound activity classes. Herein we further extend scaffold analysis by identifying scaffolds that display defined SAR profiles consisting of multiple properties. A structural relationship-based scaffold network has been designed as the basic data structure underlying our analysis. From network representations of scaffolds extracted from compounds active against 32 different target families, scaffolds with different SAR profiles have been extracted on the basis of decision trees that capture structural and functional characteristics of scaffolds in different ways. More than 600 scaffolds and 100 scaffold clusters were assigned to 10 SAR profiles. These scaffold sets represent different activity and target selectivity profiles and are provided for further SAR investigations including, for example, the exploration of alternative analog series for a given target of target family or the design of novel compounds on the basis of scaffold(s) with desired SAR profiles.     

Is scaffold hopping a reliable indicator for the ability of computational methods to identify structurally diverse active compounds?

Computational scaffold hopping aims to identify core structure replacements in active compounds. To evaluate scaffold hopping potential from a principal point of view, regardless of the computational methods that are applied, a global analysis of conventional scaffolds in analog series from compound activity classes was carried out. The majority of analog series was found to contain multiple scaffolds, thus enabling the detection of intra-series scaffold hops among closely related compounds. More than 1000 activity classes were found to contain increasing proportions of multi-scaffold analog series. Thus, using such activity classes for scaffold hopping analysis is likely to overestimate the scaffold hopping (core structure replacement) potential of computational methods, due to an abundance of artificial scaffold hops that are possible within analog series.     

Indanoyl amino acid conjugates: tunable elicitors of plant secondary metabolism

The unassuming nature of plants belies their viciously effective defensive strategies in the face of herbivore attack. Under the direction of, among others, octadecanoid hormones, plants respond by producing phytoalexins, bitter and toxic alkaloids, protease inhibitors, and even volatile compounds that call predatory insects to the herbivores. A rational design of 4-oxoindanoyl amino acid conjugates based on the phytotoxin, coronatine, as a structural guide resulted in a series of highly active compounds which turn on defensive systems in much the same way as octadecanoid hormones. The developments in the syntheses of indanoyl amino acid conjugates have created easy access to substantial amounts of a variety of such compounds. When these compounds were tested in biological systems, they showed abilities to induce defensive responses that surpassed octadecanoid hormones. In addition, small changes in the structures of these compounds resulted in large differences in the particular defensive systems that were activated. Indanoyl amino acid conjugates are promising tools in photoaffinity approaches towards the macromolecular targets of octadecanoids and their subcellular localization. Owing to the strong activation of plant defense or their efficient induction of fruit abscission which facilitates mechanical harvest, the compounds are promising candidates for future application in agriculture.     

Adapting the DeepSARM approach for dual-target ligand design

The structure–activity relationship (SAR) matrix (SARM) methodology and data structure was originally developed to extract structurally related compound series from data sets of any composition, organize these series in matrices reminiscent of R-group tables, and visualize SAR patterns. The SARM approach combines the identification of structural relationships between series of active compounds with analog design, which is facilitated by systematically exploring combinations of core structures and substituents that have not been synthesized. The SARM methodology was extended through the introduction of DeepSARM, which added deep learning and generative modeling to target-based analog design by taking compound information from related targets into account to further increase structural novelty. Herein, we present the foundations of the SARM methodology and discuss how DeepSARM modeling can be adapted for the design of compounds with dual-target activity. Generating dual-target compounds represents an equally attractive and challenging task for polypharmacology-oriented drug discovery. The DeepSARM-based approach is illustrated using a computational proof-of-concept application focusing on the design of candidate inhibitors for two prominent anti-cancer targets.

     

Scaffold hopping in drug discovery using inductive logic programming

In chemoinformatics, searching for compounds which are structurally diverse and share a biological activity is called scaffold hopping. Scaffold hopping is important since it can be used to obtain alternative structures when the compound under development has unexpected side-effects. Pharmaceutical companies use scaffold hopping when they wish to circumvent prior patents for targets of interest. We propose a new method for scaffold hopping using inductive logic programming (ILP). ILP uses the observed spatial relationships between pharmacophore types in pretested active and inactive compounds and learns human-readable rules describing the diverse structures of active compounds. The ILP-based scaffold hopping method is compared to two previous algorithms (chemically advanced template search, CATS, and CATS3D) on 10 data sets with diverse scaffolds. The comparison shows that the ILP-based method is significantly better than random selection while the other two algorithms are not. In addition, the ILP-based method retrieves new active scaffolds which were not found by CATS and CATS3D. The results show that the ILP-based method is at least as good as the other methods in this study. ILP produces human-readable rules, which makes it possible to identify the three-dimensional features that lead to scaffold hopping. A minor variant of a rule learnt by ILP for scaffold hopping was subsequently found to cover an inhibitor identified by an independent study. This provides a successful result in a blind trial of the effectiveness of ILP to generate rules for scaffold hopping. We conclude that ILP provides a valuable new approach for scaffold hopping.     

Compound set enrichment: a novel approach to analysis of primary HTS data

The main goal of high-throughput screening (HTS) is to identify active chemical series rather than just individual active compounds. In light of this goal, a new method (called compound set enrichment) to identify active chemical series from primary screening data is proposed. The method employs the scaffold tree compound classification in conjunction with the Kolmogorov-Smirnov statistic to assess the overall activity of a compound scaffold. The application of this method to seven PubChem data sets (containing between 9389 and 263679 molecules) is presented, and the ability of this method to identify compound classes with only weakly active compounds (potentially latent hits) is demonstrated. The analysis presented here shows how methods based on an activity cutoff can distort activity information, leading to the incorrect activity assignment of compound series. These results suggest that this method might have utility in the rational selection of active classes of compounds (and not just individual active compounds) for followup and validation.     

Systematic mining of analog series with related core structures in multi-target activity space

We have aimed to systematically extract analog series with related core structures from multi-target activity space to explore target promiscuity of closely related analogous. Therefore, a previously introduced SAR matrix structure was adapted and further extended for large-scale data mining. These matrices organize analog series with related yet distinct core structures in a consistent manner. High-confidence compound activity data yielded more than 2,300 non-redundant matrices capturing 5,821 analog series that included 4,288 series with multi-target and 735 series with multi-family activities. Many matrices captured more than three analog series with activity against more than five targets. The matrices revealed a variety of promiscuity patterns. Compound series matrices also contain virtual compounds, which provide suggestions for compound design focusing on desired activity profiles.     

Indirect similarity based methods for effective scaffold-hopping in chemical compounds

Methods that can screen large databases to retrieve a structurally diverse set of compounds with desirable bioactivity properties are critical in the drug discovery and development process. This paper presents a set of such methods that are designed to find compounds that are structurally different to a certain query compound while retaining its bioactivity properties (scaffold hops). These methods utilize various indirect ways of measuring the similarity between the query and a compound that take into account additional information beyond their structure-based similarities. The set of techniques that are presented capture these indirect similarities using approaches based on analyzing the similarity network formed by the query and the database compounds. Experimental evaluation shows that most of these methods substantially outperform previously developed approaches both in terms of their ability to identify structurally diverse active compounds as well as active compounds in general.     

Pseudotargeted screening and determination of constituents in Qishen granule based on compound biosynthetic correlation using UHPLC coupled with high‐resolution MS

Detection and determination of many known/unknown compounds in traditional Chinese medicines have always been challenging. To comprehensively identify compounds in Qishen granule, which is a widely prescribed herbal formula for treating chronic heart failure, a pseudotargeted screening method was proposed based on compound biosynthetic correlation using ultra high‐performance liquid chromatography coupled with high‐resolution mass spectrometry. Firstly, all possible compounds of Qishen granule were classified into nine types according to their core skeletons, and potential analogue molecular formulas were predicted according to core compound‐related biosynthetic correlations, such as methylation, hydroxylation, and glucosidation. Secondly, nine pseudocompound databases consisting of core compounds, deduced biosynthetic correlations, and predicted analogue molecular formulas were established. Then, compounds of interest were directly located by pseudotargeted screening of high resolution mass spectrometry data and further verified by target tandem mass spectrometry. As a result, 213 constituents were identified and 21 of them were determined as potential new compounds. This demonstrated that pseudotargeted screening based on compound biosynthetic correlations significantly facilitated the processing of extremely large information data and improved the efficiency of compound identification. This research provided essential data for exploration of effective substances in Qishen granule and enriched the methodology for comprehensive characterization of constituents in complex traditional Chinese medicines.     

CombiDOCK: Structure-based combinatorial docking and library design

We have developed a strategy for efficiently docking a large combinatorial library into a target receptor. For each scaffold orientation, all potential fragments are attached to the scaffold, their interactions with the receptor are individually scored and factorial combinations of fragments are constructed. To test its effectiveness, this approach is compared to two simple control algorithms. Our method is more efficient than the controls at selecting best scoring molecules and at selecting fragments for the construction of an exhaustive combinatorial library. We also carried out a retrospective analysis of the experimental results of a 10×10×10 exhaustive combinatorial library. An enrichment factor of approximately 4 was found for identifying the compounds in the library that are active at 330 nM.     

A novel approach to combinatorial library design

We address the problem of designing a general-purpose combinatorial library to screen for pharmaceutical leads. Conventional approaches focus on diversity as the primary factor in designing such libraries. We suggest making screening libraries out of a set of pharmaceutically relevant scaffolds, with multiple analogs per scaffold. The rationale for this rests on the fact that even though the hit-rate in active series is much higher than in the database as a whole, often a large fraction of the compounds in active series are inactive. This is especially true when the series has not been optimized for the target under study. We introduce the concept of hit-rate within a series and use historic screening data to arrive at a crude estimate for it. We then use simple probability arguments to show that 50-100 compounds are required in each series in order to be nearly certain of finding at least one active compound in each true active series for any given target.     

New Halogenated Compounds from Halimeda macroloba Seaweed with Potential Inhibitory Activity against Malaria

Malaria is one of the most important infectious diseases worldwide. The causative of the most severe forms of malaria, Plasmodium falciparum, has developed resistances against all the available antimalarial drugs. In the present study, the phytochemical investigation of the green seaweed Halimeda macroloba has afforded two new compounds 1–2, along with 4 known ones 3–6. The structures of the compounds had been confirmed using 1& 2D-NMR and HRESIMS analyses. Extensive machine-learning-supported virtual-screening suggested cytochrome-C enzyme as a potential target for compound 2. Docking, absolute-binding-free-energy (ΔG_binding) and molecular-dynamics-simulation (MDS) of compound 2 revealed the strong binding interaction of this compound with cytochrome-C. In vitro testing for crude extract and isolated compounds revealed the potential in vitro inhibitory activity of both extract and compound 2 against P. falciparum. The crude extract was able to inhibit the parasite growth with an IC₅₀ value of 1.8 ± 0.35 µg/mL. Compound 2 also showed good inhibitory activity with an IC₅₀ value of 3.2 ± 0.23 µg/mL. Meanwhile, compound 6 showed moderate inhibitory activity with an IC₅₀ value of 19.3 ± 0.51 µg/mL. Accordingly, the scaffold of compound 2 can be considered as a good lead compound for the future development of new antimalarial agents.     

Structural unit analysis identifies lead series and facilitates scaffold hopping in combinatorial chemistry

Combinatorial chemistry and high-throughput screening technologies produce huge amounts of data on a regular basis. Sieving through these libraries of compounds and their associated assay data to identify appropriate series for follow-up is a daunting task, which has created a need for computational techniques that can find coherent islands of structure-activity relationships in this sea. Structural unit analysis (SUA) examines an entire data set so as to identify the molecular substructures or fragments that distinguish compounds with high activity from those with average activity. The algorithm is iterative and follows set heuristics in order to generate the structural units. It produces graphs that represent a set of units, which become SUA rules. Finding all of the input structures that match these graphs generates clusters. The Apriori algorithm for association rule mining is adapted to explore all of the combinations of structural units that define useful series. User-defined constraints are applied toward series selection and the refinement of rules. The significance of a series is determined by applying statistical methods appropriate to each data set. Application to the NCI-H23 (DTP Human Tumor Cell Line Screen) database serves to illustrate the process by which structural series are identified. An application of the method to scaffold hopping is then discussed in connection with proprietary screening data from a lead optimization project directed toward the treatment of respiratory tract infections at Bayer Healthcare. SUA was able to successfully identify promising alternative core structures in addition to identifying compounds with above-average activity and selectivity.     

Narrowband Deep-Blue Multi-Resonance Induced Thermally Activated Delayed Fluorescence: Insights from the Theoretical Molecular Design

Multi-resonance thermal activated delayed fluorescence (MR-TADF) has been promising with large oscillator strength and narrow full width at half maxima of luminescence, overcoming the compromise of emission intensity and energy criteria of traditional charge transfer TADF frameworks. However, there are still limited theoretical investigations on the excitation mechanism and systematic molecular manipulation of MR-TADF structures. We systematically study the highly localized excitation (LE) characteristics based on typical blue boron-nitrogen (BN) MR-TADF emitters and prove the potential triangular core with theoretical approaches. A design strategy by extending the planar π-conjugate core structure is proposed to enhance the multiple resonance effects. Moreover, several substituted groups are introduced to the designed core, achieving color-tunable functions with relatively small energy split and strong oscillator strength simultaneously. This work provides a theoretical direction for molecular design strategy and a series of potential candidates for highly efficient BN MR-TADF emitters.     

High-performance liquid chromatography with diode array detection coupled to electrospray time-of-flight and ion-trap tandem mass spectrometry to identify phenolic compounds from a lemon verbena extract

High-performance liquid chromatography with diode array and electrospray ionization mass spectrometric detection was used to carry out the comprehensive characterization of a lemon verbena extract with demonstrated antioxidant and antiinflammatory activity. Two different MS techniques have been coupled to HPLC: on one hand, time-of-flight mass spectrometry, and on the other hand, tandem mass spectrometry on an ion-trap. The use of a small particle size C18 column (1.8 μm) provided a great resolution and made possible the separation of several isomers. The UV–visible spectrophotometry was used to delimit the class of phenolic compound and the accurate mass measurements on time-of-flight spectrometer enabled to identify the compounds present in the extract. Finally, the fragmentation pattern obtained in MS–MS experiments confirmed the proposed structures. This procedure was able to determine many well-known phenolic compounds present in lemon verbena such as verbascoside and its derivatives, diglucuronide derivatives of apigenin and luteolin, and eukovoside. Also gardoside, verbasoside, cistanoside F, theveside, campneoside I, chrysoeriol-7-diglucuronide, forsythoside A and acacetin-7-diglucuronide were found for the first time in lemon verbena.     

Learning from the data: mining of large high-throughput screening databases

High-throughput screening (HTS) campaigns in pharmaceutical companies have accumulated a large amount of data for several million compounds over a couple of hundred assays. Despite the general awareness that rich information is hidden inside the vast amount of data, little has been reported for a systematic data mining method that can reliably extract relevant knowledge of interest for chemists and biologists. We developed a data mining approach based on an algorithm called ontology-based pattern identification (OPI) and applied it to our in-house HTS database. We identified nearly 1500 scaffold families with statistically significant structure-HTS activity profile relationships. Among them, dozens of scaffolds were characterized as leading to artifactual results stemming from the screening technology employed, such as assay format and/or readout. Four types of compound scaffolds can be characterized based on this data mining effort: tumor cytotoxic, general toxic, potential reporter gene assay artifact, and target family specific. The OPI-based data mining approach can reliably identify compounds that are not only structurally similar but also share statistically significant biological activity profiles. Statistical tests such as Kruskal-Wallis test and analysis of variance (ANOVA) can then be applied to the discovered scaffolds for effective assignment of relevant biological information. The scaffolds identified by our HTS data mining efforts are an invaluable resource for designing SAR-robust diversity libraries, generating in silico biological annotations of compounds on a scaffold basis, and providing novel target family specific scaffolds for focused compound library design.     

Highly conjugated 4-ring nematic liquid crystals with a lateral alkoxy branch

Three types of liquid crystalline compound containing a 4-ring mesogenic core with a lateral alkoxy chain on one of the inner rings were synthesized, and their mesogenic properties studied. The 4-ring core of these compounds bears an electron-accepting nitro group at one end and an electron-donating alkylamino moiety at or near the other end. Therefore, they are highly coloured and have lambda max 473 nm. One of these three types of compound has a wide enantiotropic nematic range. Twelve homologous analogues in this series with different lengths for the terminal alkyl chain and the lateral alkoxy chain were synthesized and compared.     

Structure-Based Design and Synthesis of Piperidinol-Containing Molecules as New Mycobacterium abscessus Inhibitors

Non-tuberculous mycobacterium (NTM) infections, such as those caused by Mycobacterium abscessus, are increasing globally. Due to their intrinsic drug resistance, M. abscessus pulmonary infections are often difficult to cure using standard chemotherapy. We previously demonstrated that a piperidinol derivative, named PIPD1, is an efficient molecule both against M. abscessus and Mycobacterium tuberculosis, the agent of tuberculosis, by targeting the mycolic acid transporter MmpL3. These results prompted us to design and synthesize a series of piperidinol derivatives and to determine the biological activity against M. abscessus. Structure-activity relationship (SAR) studies pointed toward specific sites on the scaffold that can tolerate slight modifications. Overall, these results identified FMD-88 as a new promising active analogue against M. abscessus. Also, we determined the pharmacokinetics properties of PIPD1 and showed that intraperitoneal administration of this compound resulted in promising serum concentration and an elimination half-life of 3.2 hours.     

Activity-aware clustering of high throughput screening data and elucidation of orthogonal structure-activity relationships

From a medicinal chemistry point of view, one of the primary goals of high throughput screening (HTS) hit list assessment is the identification of chemotypes with an informative structure-activity relationship (SAR). Such chemotypes may enable optimization of the primary potency, as well as selectivity and phamacokinetic properties. A common way to prioritize them is molecular clustering of the hits. Typical clustering techniques, however, rely on a general notion of chemical similarity or standard rules of scaffold decomposition and are thus insensitive to molecular features that are enriched in biologically active compounds. This hinders SAR analysis, because compounds sharing the same pharmacophore might not end up in the same cluster and thus are not directly compared to each other by the medicinal chemist. Similarly, common chemotypes that are not related to activity may contaminate clusters, distracting from important chemical motifs. We combined molecular similarity and Bayesian models and introduce (I) a robust, activity-aware clustering approach and (II) a feature mapping method for the elucidation of distinct SAR determinants in polypharmacologic compounds. We evaluated the method on 462 dose-response assays from the Pubchem Bioassay repository. Activity-aware clustering grouped compounds sharing molecular cores that were specific for the target or pathway at hand, rather than grouping inactive scaffolds commonly found in compound series. Many of these core structures we also found in literature that discussed SARs of the respective targets. A numerical comparison of cores allowed for identification of the structural prerequisites for polypharmacology, i.e., distinct bioactive regions within a single compound, and pointed toward selectivity-conferring medchem strategies. The method presented here is generally applicable to any type of activity data and may help bridge the gap between hit list assessment and designing a medchem strategy.