Inhibitors of Human Divalent Metal Transporters DMT1 (SLC11A2) and ZIP8 (SLC39A8) from a GDB-17 Fragment Library

Solute carrier proteins (SLCs) are membrane proteins controlling fluxes across biological membranes and represent an emerging class of drug targets. Here we searched for inhibitors of divalent metal transporters in a library of 1,676 commercially available 3D-shaped fragment-like molecules from the generated database GDB-17, which lists all possible organic molecules up to 17 atoms of C, N, O, S and halogen following simple criteria for chemical stability and synthetic feasibility. While screening against DMT1 (SLC11A2), an iron transporter associated with hemochromatosis and for which only very few inhibitors are known, only yielded two weak inhibitors, our approach led to the discovery of the first inhibitor of ZIP8 (SLC39A8), a zinc transporter associated with manganese homeostasis and osteoarthritis but with no previously reported pharmacology, demonstrating that this target is druggable.

Slightly congested amino terminal dendrimers. The synthesis of amide-based stable structures on a large scale

Preparation of a new family of stable and aqueous soluble high-generation aliphatic polyamide amino terminal dendrimers. Promising scaffolds for applications where the combination of stability, multivalence and efficient conjugation is required.

Virtual Chemical Libraries

Advances in computer processing speed and storage capacity have enabled researchers to generate virtual chemical libraries containing billions of molecules. While these numbers appear large, they are only a small fraction of the number of organic molecules that could potentially be synthesized. This review provides an overview of recent advances in the generation and use of virtual chemical libraries in medicinal chemistry. We also consider the practical implications of these libraries in drug discovery programs and highlight a number of current and future challenges.

Virtual Exploration of the Ring Systems Chemical Universe

Here, we explore the chemical space of all virtually possible organic molecules focusing on ring systems, which represent the cyclic cores of organic molecules obtained by removing all acyclic bonds and converting all remaining atoms to carbon. This approach circumvents the combinatorial explosion encountered when enumerating the molecules themselves. We report the chemical universe database GDB4c containing 916 130 ring systems up to four saturated or aromatic rings and maximum ring size of 14 atoms and GDB4c3D containing the corresponding 6 555 929 stereoisomers. Almost all (98.6%) of these ring systems are unknown and represent chiral 3D-shaped macrocycles containing small rings and quaternary centers reminiscent of polycyclic natural products. We envision that GDB4c can serve to select new ring systems from which to design analogs of such natural products. The database is available for download at www.gdb.unibe.ch together with interactive visualization and search tools as a resource for molecular design.

Fragment Database FDB-17

To better understand chemical space we recently enumerated the database GDB-17 containing 166.4 billion possible molecules up to 17 atoms of C, N, O, S and halogen following the simple rules of chemical stability and synthetic feasibility. However, due to the combinatorial explosion caused by systematic enumeration GDB-17 is strongly biased toward the largest, functionally and stereochemically most complex molecules and far too large for most virtual screening tools. Herein we selected a much smaller subset of GDB-17, called the fragment database FDB-17, which contains 10 million fragmentlike molecules evenly covering a broad value range for molecular size, polarity, and stereochemical complexity. The database is available at www.gdb.unibe.ch for download and free use, together with an interactive visualization application and a Web-based nearest neighbor search tool to facilitate the selection of new fragment-sized molecules for chemical synthesis.

Dereplication: racing to speed up the natural products discovery process

Covering: 1993-2014 (July)To alleviate the dereplication holdup, which is a major bottleneck in natural products discovery, scientists have been conducting their research efforts to add tools to their "bag of tricks" aiming to achieve faster, more accurate and efficient ways to accelerate the pace of the drug discovery process. Consequently dereplication has become a hot topic presenting a huge publication boom since 2012, blending multidisciplinary fields in new ways that provide important conceptual and/or methodological advances, opening up pioneering research prospects in this field.

The chemical space project

One of the simplest questions that can be asked about molecular diversity is how many organic molecules are possible in total? To answer this question, my research group has computationally enumerated all possible organic molecules up to a certain size to gain an unbiased insight into the entire chemical space. Our latest database, GDB-17, contains 166.4 billion molecules of up to 17 atoms of C, N, O, S, and halogens, by far the largest small molecule database reported to date. Molecules allowed by valency rules but unstable or nonsynthesizable due to strained topologies or reactive functional groups were not considered, which reduced the enumeration by at least 10 orders of magnitude and was essential to arrive at a manageable database size. Despite these restrictions, GDB-17 is highly relevant with respect to known molecules. Beyond enumeration, understanding and exploiting GDBs (generated databases) led us to develop methods for virtual screening and visualization of very large databases in the form of a "periodic system of molecules" comprising six different fingerprint spaces, with web-browsers for nearest neighbor searches, and the MQN- and SMIfp-Mapplet application for exploring color-coded principal component maps of GDB and other large databases. Proof-of-concept applications of GDB for drug discovery were realized by combining virtual screening with chemical synthesis and activity testing for neurotransmitter receptor and transporter ligands. One surprising lesson from using GDB for drug analog searches is the incredible depth of chemical space, that is, the fact that millions of very close analogs of any molecule can be readily identified by nearest-neighbor searches in the MQN-space of the various GDBs. The chemical space project has opened an unprecedented door on chemical diversity. Ongoing and yet unmet challenges concern enumerating molecules beyond 17 atoms and synthesizing GDB molecules with innovative scaffolds and pharmacophores.

Very large virtual compound spaces: construction, storage and utility in drug discovery

Recent activities in the construction, storage and exploration of very large virtual compound spaces are reviewed by this report. As expected, the systematic exploration of compound spaces at the highest resolution (individual atoms and bonds) is intrinsically intractable. By contrast, by staying within a finite number of reactions and a finite number of reactants or fragments, several virtual compound spaces have been constructed in a combinatorial fashion with sizes ranging from 10(11)11 to 10(20)20 compounds. Multiple search methods have been developed to perform searches (e.g. similarity, exact and substructure) into those compound spaces without the need for full enumeration. The up-front investment spent on synthetic feasibility during the construction of some of those virtual compound spaces enables a wider adoption by medicinal chemists to design and synthesize important compounds for drug discovery. Recent activities in the area of exploring virtual compound spaces via the evolutionary approach based on Genetic Algorithm also suggests a positive shift of focus from method development to workflow, integration and ease of use, all of which are required for this approach to be widely adopted by medicinal chemists.

Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds

The "small molecule universe" (SMU), the set of all synthetically feasible organic molecules of 500 Da molecular weight or less, is estimated to contain over 10(60) structures, making exhaustive searches for structures of interest impractical. Here, we describe the construction of a "representative universal library" spanning the SMU that samples the full extent of feasible small molecule chemistries. This library was generated using the newly developed Algorithm for Chemical Space Exploration with Stochastic Search (ACSESS). ACSESS makes two important contributions to chemical space exploration: it allows the systematic search of the unexplored regions of the small molecule universe, and it facilitates the mining of chemical libraries that do not yet exist, providing a near-infinite source of diverse novel compounds.

Visualization and virtual screening of the chemical universe database GDB-17

The chemical universe database GDB-17 contains 166.4 billion molecules of up to 17 atoms of C, N, O, S, and halogens obeying rules for chemical stability, synthetic feasibility, and medicinal chemistry. GDB-17 was analyzed using 42 integer value descriptors of molecular structure which we term "Molecular Quantum Numbers" (MQN). Principal component analysis and representation of the (PC1, PC2)-plane provided a graphical overview of the GDB-17 chemical space. Rapid ligand-based virtual screening (LBVS) of GDB-17 using the city-block distance CBD(MQN) as a similarity search measure was enabled by a hashed MQN-fingerprint. LBVS of the entire GDB-17 and of selected subsets identified shape similar, scaffold hopping analogs (ROCS > 1.6 and T(SF) < 0.5) of 15 drugs. Over 97% of these analogs occurred within CBD(MQN) ≤ 12 from each drug, a constraint which might help focus advanced virtual screening. An MQN-searchable 50 million subset of GDB-17 is publicly available at www.gdb.unibe.ch .

Exploring chemical space for drug discovery using the chemical universe database

Herein we review our recent efforts in searching for bioactive ligands by enumeration and virtual screening of the unknown chemical space of small molecules. Enumeration from first principles shows that almost all small molecules (>99.9%) have never been synthesized and are still available to be prepared and tested. We discuss open access sources of molecules, the classification and representation of chemical space using molecular quantum numbers (MQN), its exhaustive enumeration in form of the chemical universe generated databases (GDB), and examples of using these databases for prospective drug discovery. MQN-searchable GDB, PubChem, and DrugBank are freely accessible at www.gdb.unibe.ch.

Design of a high fragment efficiency library by molecular graph theory

Molecular graph theory was used to design a unique and diverse, high-efficiency fragment screening collection. A data set retrieved from the annotated database AurSCOPE GPS was used as the reference set, and the GDB-13 database, a virtual library of enumerated organic molecules, was used as a source for the fragment selection. The data graph collection of Discngine as implemented in PipelinePilot was applied to perform the graph pharmacophore similarity matching between the reference and the GDB-13 data sets, leading to the ultimate fragment screening library. The relevance of this unique fragment collection was demonstrated by means of a virtual screening exercise using human trypsin as a test case. Several novel entities with high similarity to known trypsin inhibitors were identified in the in silico exercise. The application of this unique, high fragment efficiency collection to other protein targets in the framework of fragment-based drug discovery is warranted.

Exploring α7-Nicotinic Receptor Ligand Diversity by Scaffold Enumeration from the Chemical Universe Database GDB

Virtual analogues (1167860 compounds) of the nicotinic α7-receptor (α7 nAChR) ligands PNU-282,987 and SSR180711 were generated from the chemical universe database GDB-11 by extracting all aliphatic diamine analogues of the aminoquinuclidine and 1,4-diazabicyclo[3.2.2]nonane scaffolds of these ligands and converting them to the corresponding aryl amides using five different aromatic acyl groups. The library was ranked by docking to the nicotinic binding site of the acetylcholine binding protein (AChBP, 1UW6.pdb) using Autodock and Glide. Thirty-eight ligands derived from the best docking hits were synthesized and tested for modulation of the acetylcholine signal at the human α7 nAChR receptor expressed in Xenopus oocytes, leading to competitive and noncompetitive antagonists with IC50 = 5-7 μM. These experiments demonstrate the first example of using GDB in a fragment-based approach by diversifying the scaffold of known drugs.

Identification of selective norbornane-type aspartate analogue inhibitors of the glutamate transporter 1 (GLT-1) from the chemical universe generated database (GDB)

A variety of conformationally constrained aspartate and glutamate analogues inhibit the glutamate transporter 1 (GLT-1, also known as EAAT2). To expand the search for such analogues, a virtual library of aliphatic aspartate and glutamate analogues was generated starting from the chemical universe database GDB-11, which contains 26.4 million possible molecules up to 11 atoms of C, N, O, F, resulting in 101026 aspartate analogues and 151285 glutamate analogues. Virtual screening was realized by high-throughput docking to the glutamate binding site of the glutamate transporter homologue from Pyrococcus horikoshii (PDB code: 1XFH ) using Autodock. Norbornane-type aspartate analogues were selected from the top-scoring virtual hits and synthesized. Testing and optimization led to the identification of (1R*,2R*,3S*,4R*,6R*)-2-amino-6-phenethyl-bicyclo[2.2.1]heptane-2,3-dicarboxylic acid as a new inhibitor of GLT-1 with IC(50) = 1.4 μM against GLT-1 and no inhibition of the related transporter EAAC1. The systematic diversification of known ligands by enumeration with help of GDB followed by virtual screening, synthesis, and testing as exemplified here provides a general strategy for drug discovery.