Ruud van Deursen

Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

Drug molecules consist of a few tens of atoms connected by covalent bonds. How many such molecules are possible in total and what is their structure? This question is of pressing interest in medicinal chemistry to help solve the problems of drug potency, selectivity, and toxicity and reduce attrition rates by pointing to new molecular series. To better define the unknown chemical space, we have enumerated 166.4 billion molecules of up to 17 atoms of C, N, O, S, and halogens forming the chemical universe database GDB-17, covering a size range containing many drugs and typical for lead compounds. GDB-17 contains millions of isomers of known drugs, including analogs with high shape similarity to the parent drug. Compared to known molecules in PubChem, GDB-17 molecules are much richer in nonaromatic heterocycles, quaternary centers, and stereoisomers, densely populate the third dimension in shape space, and represent many more scaffold types.

Chemical Space Travel

Modern medicine critically depends on the discovery of new drugs. In this context a detailed knowledge of the ensemble of all possible organic molecules would be extremely useful to identify new structural types. This so-called chemical space is estimated at 10-10 structures in the typical drug range of MW 500 Da, which is far too large for an exhaustive listing. On the other hand known drugs define regions of chemical space that might be particularly favourable for discovering useful compounds. Herein we report a “spaceship” program which travels from a starting molecule A to a target molecule B through a continuum of structural mutations, and thereby charts unexplored chemical space. The compounds encountered along the way provide valuable starting points for virtual screening, as exemplified for ligands of the AMPA receptor. The principle of chemical space travel presented here is different from previously reported molecular structure evolution programs that combine fragments of different molecules, which did not follow the structural continuum and were not shown to reach a set target molecule. Chemical space is often visualized as a property space whose dimensions represent numerical properties of molecules, such as physicochemical descriptor values, pharmacophore descriptors, or similarity measures to reference compounds. One can define nearest neighbours in such property space as compounds with the most similar numerical property values, a concept which has been previously used for data mining by classification of existing libraries and databases. However, because one cannot derive a structure from its descriptor values, it is not possible to move between nearest neighbours in property space unless the structure of the nearest neighbours and hence all compounds under consideration are known in advance. To enable movement in an unexplored chemical space and the discovery of new structures, we describe chemical space as a structural continuum. Rather than referring to proximity in property space, we define nearest neighbours as molecules related through a single structural mutation, for example an atom-type exchange, the addition or retrieval of a bond or atom, or a skeletal rearrangement (Table 1). This description or-

Classification of organic molecules by molecular quantum numbers.

The periodic table classifies elements by increasing atomic number in periods following the principal quantum number, and allows their physicochemical properties to be rationalized. Herein, we propose a related system for organic molecules based on 42 molecular quantum numbers (MQNs), defined here as counts for simple structural features such as atom, bond and ring types, creating a multidimensional grid called MQN space. In analogy to the elements and their isotopes grouped in each entry of the periodic table, MQN isomers have identical MQNs and occupy the same position in MQN space. The MQN system is able to analyze large molecular databases and clusters compounds with similar structure, physicochemical properties and bioactivities, as illustrated for the databases ZINC and GDB-11. Organic molecules can be named by systematic nomenclature, or coded in line notations such as SMILES or InChI. These methods achieve an exact description of the molecular structure, but only provide a unidimensional classification, which is of limited use for analyzing molecular diversity. More recently, chemical space has emerged as a concept to classify large molecular databases. Chemical space is most often represented as a property space whose dimensions measure a combination of structural parameters and predicted physicochemical properties, allowing useful data mining, such as the quest for natural products analogues. While many descriptors of molecular structures and properties of varying complexity are known and may be used for defining chemical space, we set out to test whether a system based only on counts for simple structural features (MQNs) might produce an easily accessible and logical classification system for organic molecules. MQNs were considered counting atoms and bonds, polarity and topology (Table 1). MQNs were determined for the ZINC database, listing 8.4 million organic molecules, and the GDB11 database, listing 26.4 million possible molecules with up to 11 atoms of C, N, O, F, giving a total of 6 501 005 MQN combinations, or MQN bins (Table 2). Molecules with identical MQNs (MQN isomers) were strongly related structural isomers (Figure 1). Principal component analysis (PCA) showed that MQN space organizes molecules by structural types. For ZINC, 73 % of the variability is visible in the PC1/PC2 plane. PC1 mostly represents molecular size (Figure 2 a, and figure S1 in the Supporting Information). Molecules appear in elongated clusters distributed along the ascending diagonal with increasing number of rings (Figure 2 b). The number of rotatable bonds (rbc) representing molecular flexibility, the number of H-bond acceptor sites (hbam, counting nonbonding electron pairs on Nand O-atoms) and the topological surface area (TPSA in ) indicative of polarity, all increase along the descending diagonal (Figure 2 c, d & e). The calculated water–octanol partition coefficient (clog P) follows molecular size and, in part, rings and hbam (Figure 2 f). PCA of GDB-11 shows similar patterns in the PC1/PC2 plane, containing 63 % of the variability (Supporting Information, figure S2/ S3). Interestingly, compounds with similar bioactivities form groups in MQN space. Ranking by MQN distance (calculated as [a] Dr. K. T. Nguyen, L. C. Blum, R. van Deursen, Prof. Dr. J.-L. Reymond Departement of Chemistry and Biochemistry, University of Berne Freiestrasse 3, 3012 Berne (Switzerland) Fax: (+ 41) 31-631-8057 E-mail : jean-louis.reymond@ioc.unibe.ch Supporting information for this article is available on the WWW under http://dx.doi.org/10.1002/cmdc.200900317. Table 1. Molecular quantum numbers.

A Searchable Map of PubChem

The database PubChem was classified using 42 integer value descriptors of molecular structure, here called molecular quantum numbers (MQNs), which count atoms and bond types, polar groups, and topological features. Principal component analysis of the MQN data set shows that PubChem compounds occupy a partially filled elliptical cone in the (PC1,PC2,PC3) space whose axis is the first principal component PC1 (65% variability) representing molecular size, and the ellipse axes are PC2 (18% variability, representing structural flexibility) and PC3 (7% variability, representing polarity). A visual overview of PubChem is provided by color-coded representations of the (PC2,PC3) plane. The MQNs form a scalar fingerprint which can be used to measure the similarity between pairs of molecules and enable ligand-based virtual screening, as illustrated for the enrichment of bioactives from the DUD data set from PubChem. An MQN-annotated version of PubChem with an MQN-similarity search tool is available at www.gdb.unibe.ch .

The enumeration of chemical space

In the field of medicinal chemistry, the chemical space describes the ensemble of all organic molecules to be considered when searching for new drugs (estimated >1060 molecules), as well as the property spaces in which these molecules are placed for the sake of describing them. Molecules can be enumerated computationally by the millions, which was first undertaken in the field of computer‐aided structure elucidation. Scoring the enumerated virtual libraries by virtual screening has recently become an attractive strategy to prioritize compounds for synthesis and testing. Enumeration methods include combinatorial linking of fragments, genetic algorithms based on cycles of enumeration and selection by ligand‐based or target‐based scoring functions, and exhaustive enumeration from first principles. The chemical space of molecules following simple rules of chemical stability and synthetic feasibility has been enumerated up to 13 atoms of C, N, O, Cl, S, forming the GDB‐13 database with 977 million structures. The database has been organized in a 42‐dimensional chemical space using molecular quantum numbers (MQN) as descriptors, which can be visualized by projection in two dimensions by principal component analysis, and searched within seconds using a Web browser available at www.gdb.unibe.ch.

MQN-Mapplet: Visualization of Chemical Space with Interactive Maps of DrugBank, ChEMBL, PubChem, GDB-11, and GDB-13

The MQN-mapplet is a Java application giving access to the structure of small molecules in large databases via color-coded maps of their chemical space. These maps are projections from a 42-dimensional property space defined by 42 integer value descriptors called molecular quantum numbers (MQN), which count different categories of atoms, bonds, polar groups, and topological features and categorize molecules by size, rigidity, and polarity. Despite its simplicity, MQN-space is relevant to biological activities. The MQN-mapplet allows localization of any molecule on the color-coded images, visualization of the molecules, and identification of analogs as neighbors on the MQN-map or in the original 42-dimensional MQN-space. No query molecule is necessary to start the exploration, which may be particularly attractive for nonchemists. To our knowledge, this type of interactive exploration tool is unprecedented for very large databases such as PubChem and GDB-13 (almost one billion molecules). The application is freely available for download at www.gdb.unibe.ch.

Discovery of α7-Nicotinic Receptor Ligands by Virtual Screening of the Chemical Universe Database GDB-13

The chemical universe database GDB-13 enumerates 977 million organic molecules up to 13 atoms of C, N, O, Cl, and S that are virtually possible following simple rules for chemical stability and synthetic feasibility. Analogs of nicotine were identified in GDB-13 using the city-block distance in MQN-space (CBD(MQN)) as a similarity measure, combined with a restriction eliminating problematic structural elements. The search was carried out with a Web browser available at www.gdb.unibe.ch . This virtual screening procedure selected 31 504 analogs of nicotine from GDB-13, from which 48 were known nicotinic ligands reported in Chembl. An additional 60 virtual screening hits were purchased and tested for modulation of the acetylcholine signal at the human α7 nAChR expressed in Xenopus oocytes, which led to the identification of three previously unknown inhibitors. These experiments demonstrate for the first time the use of GDB-13 for ligand discovery.

Exploring the Chemical Space of Known and Unknown Organic Small Molecules at www.gdb.unibe.ch

Organic small molecules are of particular interest for medicinal chemistry since they comprise many biologically active compounds which are potential drugs. To understand this vast chemical space, we are enumerating all possible organic molecules to create the chemical universe database GDB, which currently comprises 977 million molecules up to 13 atoms of C, N, O, Cl and S. Furthermore, we have established a simple classification method for organic molecules in form of the MQN (molecular quantum numbers) system, which is an equivalent of the periodic system of the elements. Despite its simplicity the 42 dimensional MQN system is surprisingly relevant with respect to bioactivity, as evidenced by the fact that groups of biosimilar compounds form close groups in MQN space. The MQN space of the known organic molecules in PubChem and of the unknown molecules in the Chemical Universe Database GDB-13 can be searched interactively using browser tools freely accessible at www.gdb.unibe.ch.

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.

What we have learned from crystal structures of proteins to receptor function.

The activity of ligand gated channels is crucial for proper brain function and dysfunction of a single receptor subtype have led to neurological impairments ranging from benign to major diseases such as epilepsy, startle diseases, etc. Molecular biology and crystallography allowed the characterization at the atomic scale of the first four transmembrane ligand gated channels and of proteins sharing a high degree of homology with the neurotransmitter-binding domain. Gaining an adequate knowledge of the structural features of the ligand binding pocket led to the possibilities of developing virtual screening based approaches and probing in silico the docking of very large numbers of molecules. Development of new computing tools further extended such possibilities and rendered possible the screening of the chemical universe database GDB-11, which contains all possible organic molecules up to 11 atoms of C, N, O and F. In the case of the nicotinic acetylcholine receptors molecules identified using such screening methods were synthesized and characterized in binding assays and their pose determined in crystal structure with the acetylcholine binding protein. However, in spite of these thorough approaches, functional studies revealed that these molecules had a greater affinity for the pore domain of the channel and acted as open channel blocker rather than binding site antagonist. In this work, we discuss the potential and current limitations of how progresses made with the crystal structures of ligand gated channels, or ligand binding proteins, can be used in combination with virtual screening and functional assays, to identify novel compounds.