Regions Of Chemical Space Research Articles

Ring-system-based Chemical Structure Enumeration for de Novo Design

De novo molecular design aims to propose molecules exhibiting desired properties and/or activities, as constructed from scratch. Although this approach opposes the widely used virtual screening (VS), the same criteria should be applied, such as ones based on substructure filters, and quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) regression models. QSPR/QSAR, which enables us to predict properties/activities by making use of experimental data, are widely used in academia as well as in industry. Herewith, we present a novel chemical structure generation system by combining fragments whose final chemical structures satisfy the aforementioned criteria. Using inverse analysis, QSPR/QSAR models determine a specific region in chemical space corresponding to a set of desired values by a designer. Chemical structures are generated by combining ring systems, as well as atom fragments, in every possible way until violating the upper bounds of that region. We also show the results of inverse-QSAR analysis for the human Alpha-2A adrenergic receptor. This suggests that our system has features preferable to VS-like methods in terms of the number of generated structures.

Manipulating the Monolayer: Responsive and Reversible Control of Colloidal Inorganic Nanoparticle Properties

AbstractFor a wide range of nanomaterials, surface‐bound molecules play a central role in defining properties, and are key to integration with other components—be they molecules, surfaces, or other nanoparticles. Predictable and general methods for manipulating the surface monolayer are therefore crucial to exploiting this new region of chemical space. This review highlights limitations of the few established methods for controlling nanoparticle‐bound molecular functionality, then focuses on emerging new strategies. In particular, approaches that can achieve stimuli‐responsive and reversible modification of surface‐bound molecules in colloidal solution are examined, with an emphasis on using these methods to control nanoparticle properties such as solvent compatibility, catalytic activity and cytotoxicity. Finally, the outstanding challenges and future potential for precisely controlled nanoparticle‐bound monolayers are discussed.

On a simple approach for determining applicability domain of QSAR models

Quantitative structure–activity/property/toxicity relationship (QSAR/QSPR/QSTR) modeling has been used in medicinal chemistry, material sciences, environmental fate modeling, risk assessment and computational toxicology for a long time. The Organization for Economic Co-operation and Development (OECD) has recommended that for application of validated QSAR models for prediction of new data points, there is a strict requirement of defining the applicability domain (AD) according to the Principle 3. The AD is a theoretical region in chemical space encompassing both the model descriptors and modeled response which allows one to estimate the uncertainty in the prediction of a particular compound based on how similar it is to the training compounds employed in the model development. The AD is an important tool for reliable application of QSAR models, while characterization of interpolation space is significant in defining the AD. An attempt is made here to suggest a simple method for defining the X-outliers (in the case of the training set) and identifying the compounds that reside outside the AD (in the case of the test set) employing the basic theory of the standardization approach. Further, a standalone application named “Applicability domain using standardization approach” (available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/) has been developed. The present study reports that the web application can be easily used for identification of the X-outliers for training set compounds and detection of the test compounds residing outside the AD using the descriptor pool of the training and test sets.

Unique macrocycles in the Taiwan traditional Chinese medicine database.

Chemical space coverage within natural product databases is an important criterion for the selection of databases for virtual screening. Chemical space analysis can also provide valuable hints towards chemical substructures that are responsible for medical activity. We therefore developed a protocol for structurally characterizing the chemical space covered in specific natural product databases by comparing it to a "standard" natural product scaffold distribution. In this contribution, we analyzed the structural characteristics of the traditional Chinese medicine database@Taiwan as an example. While we did not find classes of very characteristic small molecule scaffolds, we found that there are a number of specific macrocyclic scaffolds that are highly enriched in the traditional Chinese medicine database@Taiwan and not documented elsewhere. This surprising finding points towards underused regions in chemical space with a big potential for biological impact.

Ring-System-Based Exhaustive Structure Generation for Inverse-QSPR/QSAR.

Inverse-QSPR/QSAR aims to solve the inverse problem of chemical structure generation based on QSPR/QSAR models, once the properties or activities are specified. To efficiently solve this problem, an exhaustive ring-system-based structure generation methodology was developed. The concept of the applicability domain (AD) is automatically acknowledged within the proposed strategy. The local AD is considered by introducing the probability distribution of a given data set, and the universal AD is considered using ring-system-based fragments in the training data set. Structures with desired properties or activities are enumerated by assembling fragments, including atomic elements, in a tree-like way. The usefulness of the proposed method is demonstrated through a case study of ligand design for the human alpha 2A adrenergic receptor (ADR2A_HUMAN). We succeeded in generating structures focusing only on a pre-defined region in chemical space, resulting in structures whose desired activity has a high likelihood being efficiently generated. In addition, the limitations of our proposed method and future challenges are discussed.

Predicting pKa Values in Aqueous Solution for the Guanidine Functional Group from Gas Phase Ab Initio Bond Lengths.

Here we applied a novel method1a to predict pKa values of the guanidine functional group, which is a notoriously difficult. This method, which was developed in our lab, uses only one ab initio bond length obtained at a low level of theory. The method is shown to work for drug molecules, delivers prediction errors of less than 0.5 log units, successfully treats tautomerisation in close relation with experiment, and demonstrates strong correlations with only a few data points. The high structural content of the ab initio bond length makes a given data set essentially divide itself into high correlation subsets. One then observes that molecules within a subset possess a common substructure. Each high correlation subset exists in its own region of chemical space. The high correlation subset method is explored with respect to this position in chemical space, in particular tautomerisation. The proposed method is able to distinguish between different tautomeric forms and the preferred tautomeric form emerges naturally, in agreement with experiment.

In silico comparison of marine, terrestrial and synthetic compounds using ChemGPS-NP for navigating chemical space

Nature represents a vast source of chemical diversity, which is supposed to cover broader areas of chemical space than synthetically obtained substances typical of medicinal chemistry. With regard to drug discovery from nature, the terrestrial environment has been the most and longest studied source, while the investigation of compounds produced by marine organisms is still in its infancy. With the objective of demonstrating the enormous chemical diversity of nature, in particular that of the marine environment, we used the chemical space navigation tool ChemGPS-NP to compare sets of marine, terrestrial and synthetic compounds with respect to physico-chemical properties and their occupation of the biologically relevant chemical space. Despite considerable overlap, the three datasets clearly differ from each other by occupying and extending into different, specific, regions in chemical space. Synthetic compounds are e.g. comparably small, with some of them being highly flexible, while marine and terrestrial products are larger and characterised by higher and lower molecular flexibility, respectively, with increasing size. Moreover, the three datasets differ to some degree in polarity, aromaticity and heteroatom content. Taken together, ChemGPS-NP has been proven to be a useful tool for navigating large volumes of biologically relevant chemical space. In this study we demonstrated the chemical uniqueness and differences of large sets of natural products, with particular emphasis on marine substances. The hence de-veiled differences further underline the relevance of natural products, of both marine and terrester origin, for future drug discovery.

Shaping a Screening File for Maximal Lead Discovery Efficiency and Effectiveness: Elimination of Molecular Redundancy

High Throughput Screening (HTS) is a successful strategy for finding hits and leads that have the opportunity to be converted into drugs. In this paper we highlight novel computational methods used to select compounds to build a new screening file at Pfizer and the analytical methods we used to assess their quality. We also introduce the novel concept of molecular redundancy to help decide on the density of compounds required in any region of chemical space in order to be confident of running successful HTS campaigns.

Development of in silico filters to predict activation of the pregnane X receptor (PXR) by structurally diverse drug-like molecules

The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r2 of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.

Current aspects and future trends of computer-aided rescaffolding

The competitive pressure in pharmaceutical industry is reflected by longer development times and increasing development costs yielding less new chemical entities. In particular, identification of suitable lead compounds is one of the key challenges in early drug discovery. Next to well established techniques like high throughput screening (HTS) and virtual screening computer-aided rescaffolding has become an important approach in detecting novel chemical structures [1,2]. The development of New Chemical Entities (NCEs) requires the exploration of novel landscapes in chemical space. Rescaffolding is in particular important in lead identification but also in lead optimization. If a lead series cannot be optimized in multidimensional space, scaffold-rescuing is often perceived as back-up strategy to transfer hitherto available SAR into a new scaffold. With a binding site or a pharmacophore in hand often de novo design methods are applied to identify novel chemical matter. However, de novo design differs from rescaffolding in that the goal of the first is primarily to generate new molecules in chemical space while the latter aims to design new scaffolds under constraints: high similarity in the desired property space and novelty in scaffold space. This allows jumping out of the known region of chemical space towards new regions of chemistry resulting in molecules with similar properties and activities but with novel frameworks. This talk will focus on ligand-based rescaffolding by taking into account only the topology of reference molecules. Several descriptors have been successfully applied for rescaffolding purposes, e.g. CATS (including CATS3D), Feature Trees (cf. ReCore), Topomers, Gaussian shape (cf. ROCS, BROOD) and others. In fragment-based rescaffolding new molecules are assembled from small (sub-) molecular fragments or building blocks often by applying pseudo-chemical rules in their recombination (e.g., RECAP). The use of fragments reduces the sampling rate of chemical space and provides access to synthesizable new compounds. This will be exemplified by two rescaffolding examples taking into account 2D as well as 3D descriptors.

Systematic extraction of structure-activity relationship information from biological screening data

The analysis of high-throughput screening data poses significant challenges to medicinal and computational chemists. The number of compounds assayed in a single screen is prohibitively large for manual data analysis and no generally applicable computational methods have thus far been developed to consistently solve the problem of how to best select hits for further chemical exploration. Focusing on the question of how structure-activity relationship (SAR) information can be used to support this decision, we present methods for the descriptive analysis of screening data. Network representations visualize the distribution of 2D similarity relationships and potency in a data set and give an overview of global and local features of an activity landscape. Although dominated by many weakly active hits, different local SAR environments can be identified among screening hits, thus helping to focus on regions in chemical space that might show favorable SAR behavior in further exploration [1]. A more detailed analysis of the data is achieved by systematically mining the network for SAR pathways, i.e. sequences of pairwise similar compounds that connect two molecules via a gradually increasing potency gradient. The SAR pathways are calculated exhaustively for all possible compound pairs in a data set to identify those having most significant SAR information content. Often, high-scoring pathways lead to activity cliffs, i.e. pairs of similar compounds with significant differences in potency, and scaffold transitions can be observed along the pathways. Furthermore, a tree structure organizes alternative pathways that begin at the same compound but lead to different molecules and chemotypes. Similarly, SAR trees can be generated from all pathways that lead to an activity cliff in order to characterize the surrounding SAR microenvironment [2].

Melting Point Prediction Employing k-Nearest Neighbor Algorithms and Genetic Parameter Optimization

We have applied the k-nearest neighbor (kNN) modeling technique to the prediction of melting points. A data set of 4119 diverse organic molecules (data set 1) and an additional set of 277 drugs (data set 2) were used to compare performance in different regions of chemical space, and we investigated the influence of the number of nearest neighbors using different types of molecular descriptors. To compute the prediction on the basis of the melting temperatures of the nearest neighbors, we used four different methods (arithmetic and geometric average, inverse distance weighting, and exponential weighting), of which the exponential weighting scheme yielded the best results. We assessed our model via a 25-fold Monte Carlo cross-validation (with approximately 30% of the total data as a test set) and optimized it using a genetic algorithm. Predictions for drugs based on drugs (separate training and test sets each taken from data set 2) were found to be considerably better [root-mean-squared error (RMSE)=46.3 degrees C, r2=0.30] than those based on nondrugs (prediction of data set 2 based on the training set from data set 1, RMSE=50.3 degrees C, r2=0.20). The optimized model yields an average RMSE as low as 46.2 degrees C (r2=0.49) for data set 1, and an average RMSE of 42.2 degrees C (r2=0.42) for data set 2. It is shown that the kNN method inherently introduces a systematic error in melting point prediction. Much of the remaining error can be attributed to the lack of information about interactions in the liquid state, which are not well-captured by molecular descriptors.

Future of ToxicologyPredictive Toxicology: An Expanded View of “Chemical Toxicity”

A chemistry approach to predictive toxicology relies on structure-activity relationship (SAR) modeling to predict biological activity from chemical structure. Such approaches have proven capabilities when applied to well-defined toxicity end points or regions of chemical space. These approaches are less well-suited, however, to the challenges of global toxicity prediction, i.e., to predicting the potential toxicity of structurally diverse chemicals across a wide range of end points of regulatory and pharmaceutical concern. New approaches that have the potential to significantly improve capabilities in predictive toxicology are elaborating the "activity" portion of the SAR paradigm. Recent advances in two areas of endeavor are particularly promising. Toxicity data informatics relies on standardized data schema, developed for particular areas of toxicological study, to facilitate data integration and enable relational exploration and mining of data across both historical and new areas of toxicological investigation. Bioassay profiling refers to large-scale high-throughput screening approaches that use chemicals as probes to broadly characterize biological response space, extending the concept of chemical "properties" to the biological activity domain. The effective capture and representation of legacy and new toxicity data into mineable form and the large-scale generation of new bioassay data in relation to chemical toxicity, both employing chemical structure information to inform and integrate diverse biological data, are opening exciting new horizons in predictive toxicology.

Linked Rings from a Library

Combinatorial chemical synthesis can rapidly generate many different compounds, but they often share an underlying structure that represents a fairly small region of chemical space. Lam et al. used a dynamic combinatorial approach to discover a surprisingly elaborate structure for binding the acetylcholine neurotransmitter. The authors added synthetic dipeptides to an acetylcholine solution under conditions allowing reversible coupling. At equilibrium, the predominant structure that had self-assembled as a receptor was a pair of linked 42-membered rings, each a trimer of the dipeptide building blocks. This catenane molecule was isolated in 65% yield and showed a 100 nanomolar affinity for the neurotransmitter. R. T. S. Lam, A. Belenguer, S. L. Roberts, C. Naumann, T. Jarrosson, S. Otto, J. K. M. Sanders, Amplification of acetylcholine-binding catenanes from dynamic combinatorial libraries. Science 308 , 667-669 (2005). [Abstract] [Full Text]

Design of a compound screening collection for use in high throughput screening.

In this paper we introduce a quantitative model that relates chemical structural similarity to biological activity, and in particular to the activity of lead series of compounds in high-throughput assays. From this model we derive the optimal screening collection make up for a given fixed size of screening collection, and identify the conditions under which a diverse collection of compounds or a collection focusing on particular regions of chemical space are appropriate strategies. We derive from the model a diversity function that may be used to assess compounds for acquisition or libraries for combinatorial synthesis by their ability to complement an existing screening collection. The diversity function is linked directly through the model to the goal of more frequent discovery of lead series from high-throughput screening. We show how the model may also be used to derive relationships between collection size and probabilities of lead discovery in high-throughput screening, and to guide the judicious application of structural filters.