Hole filling and library optimization: Application to commercially available fragment libraries

Compound libraries comprise an integral component of drug discovery in the pharmaceutical and biotechnology industries. While in-house libraries often contain millions of molecules, this number pales in comparison to the accessible space of drug-like molecules. Therefore, care must be taken when adding new compounds to an existing library in order to ensure that unexplored regions in the chemical space are filled efficiently while not needlessly increasing the library size. In this work, we present an automated method to fill holes in an existing library using compounds from an external source and apply it to commercially available fragment libraries. The method, called Canvas HF, uses distances computed from 2D chemical fingerprints and selects compounds that fill vacuous regions while not suffering from the problem of selecting only compounds at the edge of the chemical space. We show that the method is robust with respect to different databases and the number of requested compounds to retrieve. We also present an extension of the method where chemical properties can be considered simultaneously with the selection process to bias the compounds toward a desired property space without imposing hard property cutoffs. We compare the results of Canvas HF to those obtained with a standard sphere exclusion method and with random compound selection and find that Canvas HF performs favorably. Overall, the method presented here offers an efficient and effective hole-filling strategy to augment compound libraries with compounds from external sources. The method does not have any fit parameters and therefore it should be applicable in most hole-filling applications.     

Memory switches in chemical reaction space

Just as complex electronic circuits are built from simple Boolean gates, diverse biological functions, including signal transduction, differentiation, and stress response, frequently use biochemical switches as a functional module. A relatively small number of such switches have been described in the literature, and these exhibit considerable diversity in chemical topology. We asked if biochemical switches are indeed rare and if there are common chemical motifs and family relationships among such switches. We performed a systematic exploration of chemical reaction space by generating all possible stoichiometrically valid chemical configurations up to 3 molecules and 6 reactions and up to 4 molecules and 3 reactions. We used Monte Carlo sampling of parameter space for each such configuration to generate specific models and checked each model for switching properties. We found nearly 4,500 reaction topologies, or about 10% of our tested configurations, that demonstrate switching behavior. Commonly accepted topological features such as feedback were poor predictors of bistability, and we identified new reaction motifs that were likely to be found in switches. Furthermore, the discovered switches were related in that most of the larger configurations were derived from smaller ones by addition of one or more reactions. To explore even larger configurations, we developed two tools: the “bistabilizer,” which converts almost-bistable systems into bistable ones, and frequent motif mining, which helps rank untested configurations. Both of these tools increased the coverage of our library of bistable systems. Thus, our systematic exploration of chemical reaction space has produced a valuable resource for investigating the key signaling motif of bistability.     

Chemical modulators working at pharmacological interface of target proteins

For last few decades, the active site cleft and substrate-binding site of enzymes as well as ligand-binding site of the receptors have served as the main pharmacological space for drug discovery. However, rapid accumulation of proteome and protein network analysis data has opened a new therapeutic space that is the interface between the interacting proteins. Due to the complexity of the interaction modes and the numbers of the participating components, it is still challenging to identify the chemicals that can accurately control the protein-protein interactions at desire. Nonetheless, the number of chemical drugs and candidates working at the interface of the interacting proteins are rapidly increasing. This review addresses the current case studies and state-of-the-arts in the development of small chemical modulators controlling the interactions of the proteins that have pathological implications in various human diseases such as cancer, immune disorders, neurodegenerative and infectious diseases.     

Navigating molecular space for reaction mechanisms: an efficient,automated procedure

Mechanism is a core chemical concept that has vital implications for reaction rate, efficiency and selectivity. The discovery of mechanism is not easy due to the great diversity of possible chemical rearrangements in even relatively simple systems. For this reason, mechanisms involving bond breaking and forming are usually proposed via chemical intuition – which limits the scope of considered possibilities – and these hypotheses are then tested using simulation or experiment. This article discusses an automated simulation strategy for investigating multiple elementary step reaction mechanisms in chemical systems. The method starts from a single input structure and seeks out nearby intermediates, optimises the proposed structures and then determines the kinetic viability of each elementary step. The kinetically accessible intermediates are catalogued and new searches are performed on each unique structure. This process is repeated for an arbitrary number of steps without human intervention, and massively parallel computation enables fast searches in chemical space. Importantly, this strategy can be empirically shown to lead to a finite number of accessible structures, not a combinatorial explosion of intermediates. Therefore, the method should be able to predict multi-step reaction pathways in many interesting chemical systems. Demonstrations on organic reactions and a hydrogen storage material, ammonia borane, show that the herein proposed strategy can uncover complex reactivity without relying on existing chemical intuition.     

Blood-brain barrier permeability considerations for CNS-targeted compound library design

A further refinement of the concept of drug-likeness is required for compound libraries intended for central nervous system (CNS) targets to account for the limitations imposed by blood-brain barrier permeability. This review describes criteria and processes that can be applied in the de novo design and assembly of libraries to increase the odds of compounds residing within CNS-accessible chemical space. A number of published examples where CNS activity and/or penetration characteristics have been a factor in library design are discussed.     

Quality coding by neural populations in the early olfactory pathway: analysis using information theory and lessons for artificial olfactory systems

In this article, we analyze the ability of the early olfactory system to detect and discriminate different odors by means of information theory measurements applied to olfactory bulb activity images. We have studied the role that the diversity and number of receptor neuron types play in encoding chemical information. Our results show that the olfactory receptors of the biological system are low correlated and present good coverage of the input space. The coding capacity of ensembles of olfactory receptors with the same receptive range is maximized when the receptors cover half of the odor input space - a configuration that corresponds to receptors that are not particularly selective. However, the ensemble's performance slightly increases when mixing uncorrelated receptors of different receptive ranges. Our results confirm that the low correlation between sensors could be more significant than the sensor selectivity for general purpose chemo-sensory systems, whether these are biological or biomimetic.     

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.     

Beryllium: genotoxicity and carcinogenicity

Beryllium (Be) has physical-chemical properties, including low density and high tensile strength, which make it useful in the manufacture of products ranging from space shuttles to golf clubs. Despite its utility, a number of standard setting agencies have determined that beryllium is a carcinogen. Only a limited number of studies, however, have addressed the underlying mechanisms of the carcinogenicity and mutagenicity of beryllium. Importantly, mutation and chromosomal aberration assays have yielded somewhat contradictory results for beryllium compounds and whereas bacterial tests were largely negative, mammalian test systems showed evidence of beryllium-induced mutations, chromosomal aberrations, and cell transformation. Although inter-laboratory differences may play a role in the variability observed in genotoxicity assays, it is more likely that the different chemical forms of beryllium have a significant effect on mutagenicity and carcinogenicity. Because workers are predominantly exposed to airborne particles which are generated during the machining of beryllium metal, ceramics, or alloys, testing of the mechanisms of the mutagenic and carcinogenic activity of beryllium should be performed with relevant chemical forms of beryllium.     

Small zebrafish in a big chemical pond

The number of possible small organic molecules of different structure is virtually limitless. One of the main goals of chemical biologists is to identify, from this “chemical space”, entities that affect biological processes or systems in a specific manner. This can lead to a better understanding of the regulation and components of various biological machineries, as well as provide insights into efficacious therapeutic targets and drug candidates. However, the challenges confronting chemical biologists are multiple. How do we efficiently identify compounds that possess desirable activities without unwanted off‐target effects? Once a candidate compound has been found, how do we determine its mode of action? In this Prospects piece, we call attention to recent studies using embryonic and larval zebrafish to illustrate the breadth and depth of questions in chemical biology that may be addressed using this model, and hope that they can serve as catalysts for future investigational ideas. J. Cell. Biochem. 113: 2208–2216, 2012. © 2012 Wiley Periodicals, Inc.     

Global mapping of pharmacological space

We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.     

Natural product-like libraries based on non-aromatic, polycyclic motifs

Diversity-oriented synthesis is an intriguing approach for creating structurally diverse compounds that cover the pharmaceutically relevant chemical space in an optimal way. On the other hand, an over-proportionally large number of drugs or lead structures originate from compounds isolated from natural sources. Thus, not surprisingly, an increasing number of combinatorial libraries are based on motifs resembling natural products. A particular aspect of many natural products is the presence of non-aromatic, polycyclic core structures. The fusion of several rings leads to geometrically well-defined structures and, thus, holds the promise of a high functional specialisation. In this review we present several actual examples of natural product-like libraries with non-aromatic polycyclic motifs based on naturally occurring compounds.     

Rule-based programming paradigm: a formal basis for biological, chemical and physical computation

A rule-based programming paradigm is described as a formal basis for biological, chemical and physical computations. In this paradigm, the computations are interpreted as the outcome arising out of interaction of elements in an object space. The interactions can create new elements (or same elements with modified attributes) or annihilate old elements according to specific rules. Since the interaction rules are inherently parallel, any number of actions can be performed cooperatively or competitively among the subsets of elements, so that the elements evolve toward an equilibrium or unstable or chaotic state. Such an evolution may retain certain invariant properties of the attributes of the elements. The object space resembles Gibbsian ensemble that corresponds to a distribution of points in the space of positions and momenta (called phase space). It permits the introduction of probabilities in rule applications. As each element of the ensemble changes over time, its phase point is carried into a new phase point. The evolution of this probability cloud in phase space corresponds to a distributed probabilistic computation. Thus, this paradigm can handle tor deterministic exact computation when the initial conditions are exactly specified and the trajectory of evolution is deterministic. Also, it can handle probabilistic mode of computation if we want to derive macroscopic or bulk properties of matter. We also explain how to support this rule-based paradigm using relational-database like query processing and transactions.     

Bounds for generalized Dreitlein-Smoes models

The analysis of a previous paper obtaining bounds on the total population number of species (chemical or biological) described by the recently proposed Dreitlein-Smoes model of oscillatory kinetic systems, including diffusion, is extended to generalized models of the Dreitlein-Smoes type, describing a system ofS components withS>2. The results for such generalized models are analogous to those of the previous case. It is found that the effects of diffusion serve to restrict the region in the concentration space available to limitcycle type oscillations.     

The LUX Score: A Metric for Lipidome Homology

A lipidome is the set of lipids in a given organism, cell or cell compartment and this set reflects the organism’s synthetic pathways and interactions with its environment. Recently, lipidomes of biological model organisms and cell lines were published and the number of functional studies of lipids is increasing. In this study we propose a homology metric that can quantify systematic differences in the composition of a lipidome. Algorithms were developed to 1. consistently convert lipids structure into SMILES, 2. determine structural similarity between molecular species and 3. describe a lipidome in a chemical space model. We tested lipid structure conversion and structure similarity metrics, in detail, using sets of isomeric ceramide molecules and chemically related phosphatidylinositols. Template-based SMILES showed the best properties for representing lipid-specific structural diversity. We also show that sequence analysis algorithms are best suited to calculate distances between such template-based SMILES and we adjudged the Levenshtein distance as best choice for quantifying structural changes. When all lipid molecules of the LIPIDMAPS structure database were mapped in chemical space, they automatically formed clusters corresponding to conventional chemical families. Accordingly, we mapped a pair of lipidomes into the same chemical space and determined the degree of overlap by calculating the Hausdorff distance. We named this metric the ‘Lipidome jUXtaposition (LUX) score’. First, we tested this approach for estimating the lipidome similarity on four yeast strains with known genetic alteration in fatty acid synthesis. We show that the LUX score reflects the genetic relationship and growth temperature better than conventional methods although the score is based solely on lipid structures. Next, we applied this metric to high-throughput data of larval tissue lipidomes of Drosophila. This showed that the LUX score is sufficient to cluster tissues and determine the impact of nutritional changes in an unbiased manner, despite the limited information on the underlying structural diversity of each lipidome. This study is the first effort to define a lipidome homology metric based on structures that will enrich functional association of lipids in a similar manner to measures used in genetics. Finally, we discuss the significance of the LUX score to perform comparative lipidome studies across species borders.     

Grand Molecular Dynamics: A Method for Open Systems

We present a new molecular dynamics method for studying the dynamics of open systems. The method couples a classical system to a chemical potential reservior. In the formulation, following the extended system dynamics approach, we introduce a variable, v to represent the coupling to the chemical potential reservoir. The new variable governs the dynamics of the variation of number of particles in the system. The number of particles is determined by taking the integer part of v. The fractional part of the new variable is used to scale the potential energy and the kinetic energy of an additional particle: i.e., we introduce a fractional particle. We give the ansatz Lagrangians and equations of motion for both the isothermal and the adiabatic forms of grand molecular dynamics. The averages calculated over the trajectories generated by these equations of motion represent the classical grand canonical ensemble (μVT) and the constant chemical potential adiabatic ensemble (μVL) averages, respectively. The microcanonical phase space densities of the adiabatic and isothermal forms the molecular dynamics method are shown to be equivalent to adiabatic constant chemical potential ensemble, and grand canonical ensemble partition functions. We also discuss the extension to multi-component systems, molecular fluids, ionic solutions and the problems and solutions associated with the implementation of the method. The statistical expressions for thermodynamic functions such as specific heat; adiabatic bulk modulus, Grüneissen parameter and number fluctuations are derived. These expressions are used to analyse trajectories of constant chemical potential systems.     

Merging chemical and biological space: Structural mapping of enzyme binding pocket space

Structure‐based drug design tries to mutually map pharmacological space populated by putative target proteins onto chemical space comprising possible small molecule drug candidates. Both spaces are connected where proteins and ligands recognize each other: in the binding pockets. Therefore, it is highly relevant to study the properties of the space composed by all possible binding cavities. In the present contribution, a global mapping of protein cavity space is presented by extracting consensus cavities from individual members of protein families and clustering them in terms of their shape and exposed physicochemical properties. Discovered similarities indicate common binding epitopes in binding pockets independent of any possibly given similarity in sequence and fold space. Unexpected links between remote targets indicate possible cross‐reactivity of ligands and suggest putative side effects. The global clustering of cavity space is compared to a similar clustering of sequence and fold space and compared to chemical ligand space spanned by the chemical properties of small molecules found in binding pockets of crystalline complexes. The overall similarity architecture of sequence, fold, and cavity space differs significantly. Similarities in cavity space can be mapped best to similarities in ligand binding space indicating possible cross‐reactivities. Most cross‐reactivities affect co‐factor and other endogenous ligand binding sites. Proteins 2009. © 2008 Wiley‐Liss, Inc.