Representation of multi-target activity landscapes through target pair-based compound encoding in self-organizing maps

Activity landscape representations provide access to structure-activity relationships information in compound data sets. In general, activity landscape models integrate molecular similarity relationships with biological activity data. Typically, activity against a single target is monitored. However, for steadily increasing numbers of compounds, activity against multiple targets is reported, resulting in an opportunity, and often a need, to explore multi-target structure-activity relationships. It would be attractive to utilize activity landscape representations to aid in this process, but the design of activity landscapes for multiple targets is a complicated task. Only recently has a first multi-target landscape model been introduced, consisting of an annotated compound network focused on the systematic detection of activity cliffs. Herein, we report a conceptually different multi-target activity landscape design that is based on a 2D projection of chemical reference space using self-organizing maps and encodes compounds as arrays of pair-wise target activity relationships. In this context, we introduce the concept of discontinuity in multi-target activity space. The well-ordered activity landscape model highlights centers of discontinuity in activity space and is straightforward to interpret. It has been applied to analyze compound data sets with three, four, and five target annotations and identify multi-target structure-activity relationships determinants in analog series.     

BindingDB and ChEMBL: online compound databases for drug discovery

Public domain repositories of compound structures and activity data are indispensable tools for many aspects of pharmaceutical research, especially in academic environments. Such databases provide essential resources for structure-activity data mining and the evaluation of chemoinformatics and drug design methods. They are also important to support scientific interactions between commercial and academic environments. This editorial highlights two major public domain compound data repositories, BindingDB and ChEMBL, which have different origins. BindingDB has evolved in an academic setting (and continues to be developed there) and ChEMBL in a biotechnology environment. The ChEMBL database is now maintained and further developed at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory. These databases mostly contain structures and activity data taken from the scientific literature, covering different stages of compound exploration and optimization efforts, and provide a substantial body of complementary compound activity information. Together with PubChem bioassays, ChEMBL and BindingDB provide the foundation of compound data analysis in the public domain.     

Exploring SAR continuity in the vicinity of activity cliffs

Activity cliffs are formed by structurally similar compounds with significant differences in potency and represent an extreme form of structure-activity relationships discontinuity. By contrast, regions of structure-activity relationships continuity in compound data sets result from the presence of structurally increasingly diverse compounds retaining similar activity. Previous studies have revealed that structure-activity relationships information extracted from large compound data sets is often heterogeneous in nature containing both continuous and discontinuous structure-activity relationships components. Structure-activity relationships discontinuity and continuity are often represented by different compound series, independent of each other. Here, we have searched different compound data sets for the presence of structure-activity relationships continuity within the vicinity of prominent activity cliffs. For this purpose, we have designed and implemented a computational approach utilizing particle swarm optimization to examine the structural neighborhood of activity cliffs for continuous structure-activity relationships components. Structure-activity relationships continuity in the structural neighborhood of activity cliffs was relatively rarely observed. However, in a number of cases, notable structure-activity relationships continuity was detected in the vicinity of prominent activity cliffs. Exemplary local structure-activity relationships environments displaying these characteristics were analyzed in detail. Thus, the structure-activity relationships environment of activity cliffs must not necessarily be discontinuous in nature, and local structure-activity relationships continuity and discontinuity can occur in a concerted manner in series of structurally related compounds.     

Activity cliff clusters as a source of structure–activity relationship information

The activity cliff (AC) concept is widely applied in medicinal chemistry. ACs are formed by compounds with small structural changes having large differences in potency. Accordingly, ACs are a primary source of structure–activity relationship (SAR) information. Through large-scale compound data mining it has been shown that the vast majority of ACs are formed in a coordinated manner by groups of structurally analogous compounds with significant potency variations. In network representations coordinated ACs form clusters of varying size but frequently recurrent topology. Recently, computational methods have been introduced to systematically organize AC clusters and extract SAR information from them. AC clusters are widely distributed over compound activity classes and represent a rich source of SAR information. These clusters can be visualized in AC networks and isolated. However, it is challenging to extract SAR information from such clusters and make this information available to the practice of medicinal chemistry. Therefore, it is essential to go beyond subjective case-by-case analysis and design computational approaches to systematically access SAR information associated with AC clusters.     

Comprehensive analysis of single- and multi-target activity cliffs formed by currently available bioactive compounds

Activity cliffs are formed by structurally similar compounds having large potency differences. Their study is a focal point of SAR analysis. We present a first systematic survey of single- and multitarget activity cliffs contained in currently available bioactive compounds. Approximately 12% of all active compounds were involved in the formation of activity cliffs. Perhaps unexpectedly, activity cliffs were found to be similarly distributed over different protein target families. Moreover, only approximately 5% of all activity cliffs were multitarget cliffs. Importantly, we also found that only very few multitarget cliffs were formed by compounds having different target selectivity. In addition, 'polypharmacological cliffs', i.e., multitarget activity cliffs involving targets from different protein families, were also only rarely found. Taken together, our findings reveal that only approximately 2% of all pairs of structurally similar compounds sharing the same biological activity form activity cliffs but that, on average, approximately one of 10 active compounds is involved in the formation of one or two single-target cliffs of large magnitude (with at least 100-fold difference in potency). These compounds provide a rich source of SAR information and can be identified across many different target families.     

3D-QSAR and molecular docking for the discovery of ketolide derivatives

Introduction: There is an urgent need to discover novel antibiotics to overcome the growing problem of antibiotic resistance, which has become a serious concern in current medicine. Ketolides, the third generation of macrolide antibiotics, have shown promising effect against macrolide-resistant pathogens in respiratory diseases. Currently, a number of ketolide derivatives with excellent antibacterial activities have been reported, while their structure–activity relationships (SARs) were rarely explored systematically. Computer-aided drug design (CADD) such as 3D-QSAR and molecular docking are useful tools to study drug SARs in medicinal chemistry. Using these technologies, ketolide derivatives were systemically analyzed revealing important useful information about their SARs, providing useful information which can guide new drug design and optimization.

Areas covered: The authors provide an overview of the currently reported 3D-QSAR models of ketolide derivatives. The authors present a comprehensive SAR model obtained from in-depth 3D-QSAR and molecular docking analysis for all kinds of ketolide derivatives.

Expert opinion: 3D-QSAR has been shown to be a reliable tool that had successfully assisted the design of several new antibiotics with improved activity and reduced toxicity. By applying 3D-QSAR and molecular docking, a comprehensive and systematic SAR model for ketolide derivative discovery was formed, which is important to guide future drug design for the discovery of better ketolides with lower toxicity.     

DataWarrior: an evaluation of the open-source drug discovery tool

Introduction: DataWarrior is open and interactive software for data analysis and visualization that integrates well-established and novel chemoinformatics algorithms in a single environment. Since its public release in 2014, DataWarrior has been used by research groups in universities, government, and industry.

Areas covered: Herein, the authors discuss, in a critical manner, the tools and distinct technical features of DataWarrior and analyze the areas of opportunity. Authors also present the most common applications as well as emerging uses in research areas beyond drug discovery with an emphasis on multidisciplinary projects.

Expert opinion: In the era of big data and data-driven science, DataWarrior stands out as a technology that combines prediction of physicochemical properties of pharmaceutical interest, cheminformatics calculations, multivariate data analysis, and interactive visualization with dynamic plots. The well-established chemoinformatics tools implemented in DataWarrior, as well as the innovative algorithms, make the technology useful and attractive as revealed by the increasing number of documented applications.     

Activity landscape of DNA methyltransferase inhibitors bridges chemoinformatics with epigenetic drug discovery

Introduction: Activity landscapes are valuable tools for exploring systematically the structure–activity relationships (SAR) of chemical databases. Their application to analyze the SAR of DNA methyltransferase (DNMT) inhibitors, which are attractive compounds as potential epi-drugs or epi-probes, provides useful information to identify pharmacophoric regions and plan the development of predictive models and virtual screening.

Areas covered: This paper highlights different approaches for conducting SAR analysis of datasets with a particular focus on the activity landscape methodology. SAR information of DNMT inhibitors (DNMTi), stored in a public database, is surveyed to further illustrate concepts and generalities of activity landscape modeling with a special emphasis on structure–activity similarity (SAS) maps.

Expert opinion: The increasing SAR information reported for DNMTi opens up avenues to implement activity landscape methods. Despite several activity landscape methods, such as SAS maps, being well established, these need further refinement. For instance, novel combinations of multiple representations, such as the addition of Z-values of similarity (fusion-Z), lead to more robust representations of consensus SAS maps. Density SAS maps improve the visualization of the SAR. A survey of activity cliffs (i.e., pairs of compounds with high structural similarity but high differences in potency) of DNMTi available in a public database suggest that it is feasible to develop predictive models for non-nucleoside DNMTi using approaches such as quantitative structure-activity relationships and that non-nucleoside DNMTi in ChEMBL can be used as query molecules in similarity-based virtual screening.     

High-throughput drug screens for amyotrophic lateral sclerosis drug discovery

ABSTRACT

Introduction: Amyotrophic lateral sclerosis (ALS) is a rapid adult-onset neurodegenerative disorder characterised by the progressive loss of upper and lower motor neurons. Current treatment options are limited for ALS, with very modest effects on survival. Therefore, there is a unmet need for novel therapeutics to treat ALS.

Areas covered: This review highlights the many diverse high-throughput screening platforms that have been implemented in ALS drug discovery. The authors discuss cell free assays including in silico and protein interaction models. The review also covers classical in vitro cell studies and new cell technologies, such as patient derived cell lines. Finally, the review looks at novel in vivo models and their use in high-throughput ALS drug discovery

Expert opinion: Greater use of patient-derived in vitro cell models and development of better animal models of ALS will improve translation of lead compounds into clinic. Furthermore, AI technology is being developed to digest and interpret obtained data and to make ‘hidden knowledge’ usable to researchers. As a result, AI will improve target selection for high-throughput drug screening (HTDS) and aid lead compound optimisation. Furthermore, with greater genetic characterisation of ALS patients recruited to clinical trials, AI may help identify responsive genetic subtypes of patients from clinical trials.     

Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective

Introduction: Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition.

Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics.

Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the ‘big bang’ of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.     

Fluorination methods for drug discovery and development

ABSTRACT

Introduction: The pivotal role that the element fluorine plays in modulating the properties of bioactive molecules is reflected by the growth of its presence in approved drugs. In 1970, approximately 2% of drugs contained fluorine with this number rising to 25% by 2011. The synthetic chemistry regarding incorporation of fluorine into organic molecules has also evolved over this time with a paradigm shift from harsh, toxic, hazardous reagents utilized primarily by specialist vendors to new deoxyfluorination reagents and metal-mediated techniques capable of the precise introduction of fluorine into complex organic substrates under relatively mild conditions.

Areas covered: This review highlights the importance of fluorinated compounds in drug discovery, and provides an overview on the synthetic strategies and methodologies developed to access them both in discovery and development.

Expert opinion: The development of new reagents for the safe and precise regioselective fluorination of biologically relevant compounds particularly in drug discovery remains a contemporary challenge in organic chemistry. However, significant strides have been made with the development of new deoxyfluorination reagents and the emergence of practical metal-mediated fluorination techniques have enabled the goal of efficient late-stage fluorination of drug-like compounds to be realized, and the extension of these methods for PET-labelling is being investigated.     

异苯并呋喃酮-3-乙酰胺类化合物的合成及其抗惊活性构效关系的初步探讨

设计合成了１１个新的异苯并呋喃酮３乙酰胺类化合物，它们均未见文献报道，产物经核磁共振氢谱和元素分析确证．抗惊实验表明：当酰胺氮原子上的取代基为脂肪取代基且碳原子总数在６～１０个之间时，该类化合物才有较好的抗惊活性．并对其构效关系进行了初步讨论     

Principal component and causal analysis of structural and acute in vitro toxicity data for nanoparticles

Structure toxicity relationship analysis was conducted using principal component analysis (PCA) for a panel of nanoparticles that included dry powders of oxides of titanium, zinc, cerium and silicon, dry powders of silvers, suspensions of polystyrene latex beads and dry particles of carbon black, nanotubes and fullerene, as well as diesel exhaust particles. Acute in vitro toxicity was assessed by different measures of cell viability, apoptosis and necrosis, haemolytic effects and the impact on cell morphology, while structural properties were characterised by particle size and size distribution, surface area, morphology, metal content, reactivity, free radical generation and zeta potential. Different acute toxicity measures were processed using PCA that classified the particles and identified four materials with an acute toxicity profile: zinc oxide, polystyrene latex amine, nanotubes and nickel oxide. PCA and contribution plot analysis then focused on identifying the structural properties that could determine the acute cytotoxicity of these four materials. It was found that metal content was an explanatory variable for acute toxicity associated with zinc oxide and nickel oxide, while high aspect ratio appeared the most important feature in nanotubes. Particle charge was considered as a determinant for high toxicity of polystyrene latex amine.