Enhancing the Small-Scale Screenable Biological Space beyond Known Chemogenomics Libraries with Gray Chemical Matter─Compounds with Novel Mechanisms from High-Throughput Screening Profiles

Phenotypic assays have become an established approach to drug discovery. Greater disease relevance is often achieved through cellular models with increased complexity and more detailed readouts, such as gene expression or advanced imaging. However, the intricate nature and cost of these assays impose limitations on their screening capacity, often restricting screens to well-characterized small compound sets such as chemogenomics libraries. Here, we outline a cheminformatics approach to identify a small set of compounds with likely novel mechanisms of action (MoAs), expanding the MoA search space for throughput limited phenotypic assays. Our approach is based on mining existing large-scale, phenotypic high-throughput screening (HTS) data. It enables the identification of chemotypes that exhibit selectivity across multiple cell-based assays, which are characterized by persistent and broad structure activity relationships (SAR). We validate the effectiveness of our approach in broad cellular profiling assays (Cell Painting, DRUG-seq, and Promotor Signature Profiling) and chemical proteomics experiments. These experiments revealed that the compounds behave similarly to known chemogenetic libraries, but with a notable bias toward novel protein targets. To foster collaboration and advance research in this area, we have curated a public set of such compounds based on the PubChem BioAssay dataset and made it available for use by the scientific community.

High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery

Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial activity testing has also proven to be challenging. In addition to exploring new sources of natural products and synthetic biology, omics technology helped to study the biosynthetic machinery of existing natural sources enabling the construction of unnatural synthesizers of bioactive molecules and the identification of molecular targets of antibacterial agents. On the other hand, newer and smarter strategies have been continuously pursued to screen synthetic molecule libraries for new antibiotics and new druggable targets. Biomimetic conditions are explored to mimic the real infection model to better study the ligand-target interaction to enable the designing of more effective antibacterial drugs. This narrative review describes various traditional and contemporaneous approaches of high-throughput screening of natural products and synthetic molecule libraries for antibacterial drug discovery. It further discusses critical factors for HTS assay design, makes a general recommendation, and discusses possible alternatives to traditional HTS of natural products and synthetic molecule libraries for antibacterial drug discovery.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.

Identification of the Benzoimidazole Compound as a Selective FLT3 Inhibitor by Cell-Based High-Throughput Screening of a Diversity Library

Internal tandem duplication in the FLT3 receptor tyrosine kinase (FLT3/ITD mutation) occurs in approximately 25% of acute myeloid leukemia (AML) patients. To specifically target this driver mutation in AML, we assessed and compared the cell-based cytotoxicity of a diversity library (10,000 compounds) against the normal cell line BaF3 and the isogenic leukemic cell line BaF3/ITD. A benzoimidazole scaffold-based compound (HP1142) was identified as the most selective compound against a series of murine and human leukemia cells with FLT3/ITD. Novel benzoimidazole compounds were further designed to improve the aqueous solubility of HP1142. The most potent compound, HP1328, demonstrated desirable pharmaceutical and pharmacokinetic properties. Treatment with HP1328 significantly reduced the leukemia burden and prolonged the survival of mice with FLT3/ITD leukemia. Our findings establish the specific activity of the benzoimidazole compound against FLT3/ITD leukemia and warrant further investigation in this subset of leukemia patients with poor prognosis.

Cytotoxic Profiling of Annotated and Diverse Chemical Libraries Using Quantitative High-Throughput Screening

Cell-based phenotypic screening is a commonly used approach to discover biological pathways, novel drug targets, chemical probes, and high-quality hit-to-lead molecules. Many hits identified from high-throughput screening campaigns are ruled out through a series of follow-up potency, selectivity/specificity, and cytotoxicity assays. Prioritization of molecules with little or no cytotoxicity for downstream evaluation can influence the future direction of projects, so cytotoxicity profiling of screening libraries at an early stage is essential for increasing the likelihood of candidate success. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in the National Institutes of Health, National Center for Advancing Translational Sciences annotated libraries and more than 100,000 compounds in a diversity library against four normal cell lines (HEK 293, NIH 3T3, CRL-7250, and HaCat) and one cancer cell line (KB 3-1, a HeLa subline). This large-scale library profiling was analyzed for overall screening outcomes, hit rates, pan-activity, and selectivity. For the annotated library, we also examined the primary targets and mechanistic pathways regularly associated with cell death. To our knowledge, this is the first study to use high-throughput screening to profile a large screening collection (>100,000 compounds) for cytotoxicity in both normal and cancer cell lines. The results generated here constitute a valuable resource for the scientific community and provide insight into the extent of cytotoxic compounds in screening libraries, allowing for the identification and avoidance of compounds with cytotoxicity during high-throughput screening campaigns.

Design of the LifeArc Index Set and Retrospective Review of Its Performance: A Collection for Sharing

Building, curating, and maintaining a compound collection is an expensive operation, beyond the scope of most academic organizations. Here we describe the selection criteria used to compile the LifeArc diversity set from commercial suppliers and the process we undertook to generate our representative LifeArc index set. The aim was to avoid a "junk in, junk out" screen collection to increase chemical tractability going forward, while maximizing diversity. Using historical LifeArc screening data, we demonstrate that the index set was predictive of ligandability and that progressable hits could be identified by mining associated clusters within our larger diversity set. Indeed, a higher percentage of index-derived hit clusters were found to have been progressed into hit-to-lead programs, reflecting better drug-likeness. In practice, the library has been shared widely with academic groups and used routinely within LifeArc to assess the ligandability of novel targets. Its small size is well suited to meet the needs of medium-throughput screening in labs with either limited automation, limited precious or expensive reagents, or complex cellular assays. The strategy of screening a small set in combination with rapid hit analog follow-up has demonstrated the utility of finding active clusters for potential development against challenging targets.

Rare Medium-Sized Rings Prepared via Hydrolytic Imidazoline Ring Expansion (HIRE)

The hydrolytic imidazoline ring expansion (HIRE) methodology was extended to readily available tetracyclic [1,4]thiazepines as well as sulfoxide and sulfone analogs thereof. The reactions resulted in the facile formation of a rare medium-sized [1,4,7]thiazecine ring system that has an emerging utility in bioactive compound design. Comparing the HIRE rates for representative compounds in the three groups of substrates allowed drawing some generalizations about the substituent effects on the course of the reaction.

Optimizing Antimicrobial Peptide Dendrimers in Chemical Space

We used nearest-neighbor searches in chemical space to improve the activity of the antimicrobial peptide dendrimer (AMPD) G3KL and identified dendrimer T7, which has an expanded activity range against Gram-negative pathogenic bacteria including Klebsiellae pneumoniae, increased serum stability, and promising activity in an in vivo infection model against a multidrug-resistant strain of Acinetobacter baumannii. Imaging, spectroscopic studies, and a structural model from molecular dynamics simulations suggest that T7 acts through membrane disruption. These experiments provide the first example of using virtual screening in the field of dendrimers and show that dendrimer size does not limit the activity of AMPDs.

An antimicrobial bicyclic peptide from chemical space against multidrug resistant Gram-negative bacteria

We used the concept of chemical space to explore a virtual library of bicyclic peptides formed by double thioether cyclization of a precursor linear peptide, and identified an antimicrobial bicyclic peptide (AMBP) with remarkable activity against several MDR strains of Acinetobacter baumannii and Pseudomonas aeruginosa.

The Future of Computational Chemogenomics

Following the elucidation of the human genome, chemogenomics emerged in the beginning of the twenty-first century as an interdisciplinary research field with the aim to accelerate target and drug discovery by making best usage of the genomic data and the data linkable to it. What started as a systematization approach within protein target families now encompasses all types of chemical compounds and gene products. A key objective of chemogenomics is the establishment, extension, analysis, and prediction of a comprehensive SAR matrix which by application will enable further systematization in drug discovery. Herein we outline future perspectives of chemogenomics including the extension to new molecular modalities, or the potential extension beyond the pharma to the agro and nutrition sectors, and the importance for environmental protection. The focus is on computational sciences with potential applications for compound library design, virtual screening, hit assessment, analysis of phenotypic screens, lead finding and optimization, and systems biology-based prediction of toxicology and translational research.

Protocols for the Design of Kinase-focused Compound Libraries

Protocols for the design of kinase-focused compound libraries are presented. Kinase-focused compound libraries can be differentiated based on the design goal. Depending on whether the library should be a discovery library specific for one particular kinase, a general discovery library for multiple distinct kinase projects, or even phenotypic screening, there exists today a variety of in silico methods to design candidate compound libraries. We address the following scenarios: 1) Datamining of SAR databases and kinase focused vendor catalogues; 2) Predictions and virtual screening; 3) Structure-based design of combinatorial kinase inhibitors; 4) Design of covalent kinase inhibitors; 5) Design of macrocyclic kinase inhibitors; and 6) Design of allosteric kinase inhibitors and activators.

Chemical space guided discovery of antimicrobial bridged bicyclic peptides against Pseudomonas aeruginosa and its biofilms

Herein we report the discovery of antimicrobial bridged bicyclic peptides (AMBPs) active against Pseudomonas aeruginosa, a highly problematic Gram negative bacterium in the hospital environment. Two of these AMBPs show strong biofilm inhibition and dispersal activity and enhance the activity of polymyxin, currently a last resort antibiotic against which resistance is emerging. To discover our AMBPs we used the concept of chemical space, which is well known in the area of small molecule drug discovery, to define a small number of test compounds for synthesis and experimental evaluation. Our chemical space was calculated using 2DP, a new topological shape and pharmacophore fingerprint for peptides. This method provides a general strategy to search for bioactive peptides with unusual topologies and expand the structural diversity of peptide-based drugs.

Non-stoichiometric inhibition in integrated lead finding - a literature review

INTRODUCTION

Non-stoichiometric inhibition summarizes different mechanisms by which low-molecular weight compounds can reproducibly inhibit high-throughput screening (HTS) and other lead finding assays without binding to a structurally defined site on their molecular target. This disqualifies such molecules from optimization by medicinal chemistry, and therefore their rapid elimination from screening hit lists is essential for productive and effective drug discovery.

AREAS COVERED

This review covers recent literature that either investigates the various mechanisms behind non-stoichiometric inhibition or suggests assays and readouts to identify them. In addition, combination of the various methods to distill promising molecules out of raw primary hit lists step-by-step is considered. Emerging technologies to demonstrate target engagement in cells are also discussed.

EXPERT OPINION

Over the last few years, awareness of non-stoichiometric inhibitors within screening libraries and HTS hit lists has considerably increased, not only in the pharmaceutical industry but also in the academic drug discovery community. This has resulted in a variety of methods to detect and handle such compounds. These range from in silico approaches to flag suspicious compounds, and counterassays to measure non-stoichiometric inhibition, to biophysical methods that positively demonstrate stoichiometric binding. In addition, novel technologies to verify target engagement within cells are becoming available. While still a time- and resource-consuming nuisance, non-stoichiometric inhibitors therefore do not fundamentally jeopardize the discovery of low molecular weight lead and drug candidates. Rather, they should be viewed as a manageable issue that with appropriate expertise can be overcome through integration of the above-mentioned approaches.

Bridged bicyclic peptides as potential drug scaffolds: synthesis, structure, protein binding and stability

Double cyclization of short linear peptides obtained by solid phase peptide synthesis was used to prepare bridged bicyclic peptides (BBPs) corresponding to the topology of bridged bicyclic alkanes such as norbornane. Diastereomeric norbornapeptides were investigated by 1H-NMR, X-ray crystallography and CD spectroscopy and found to represent rigid globular scaffolds stabilized by intramolecular backbone hydrogen bonds with scaffold geometries determined by the chirality of amino acid residues and sharing structural features of β-turns and α-helices. Proteome profiling by capture compound mass spectrometry (CCMS) led to the discovery of the norbornapeptide 27c binding selectively to calmodulin as an example of a BBP protein binder. This and other BBPs showed high stability towards proteolytic degradation in serum.

Screening technologies for small molecule discovery: the state of the art

Screening, high-throughput screening, and ultra-high-throughput screening are all really just points on a spectrum that represent differing applications of the same process: the creation of biologically relevant assays that are relevant, reproducible, reliable, and robust. Whether the discovery program is developing a pharmaceutical, an academic probe, cosmetics, pesticides, or a toxicity monitoring assay, the development of a screen focuses on generating a method that will reliably deliver reproducible results over a period of weeks, months, or years and that will generate consistent results for every test along the way. This review provides both historical perspective on how this unique scientific discipline evolved and commentary on the current state of the art technologies and techniques.

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.

Stereoselective virtual screening of the ZINC database using atom pair 3D-fingerprints

BACKGROUND

Tools to explore large compound databases in search for analogs of query molecules provide a strategically important support in drug discovery to help identify available analogs of any given reference or hit compound by ligand based virtual screening (LBVS). We recently showed that large databases can be formatted for very fast searching with various 2D-fingerprints using the city-block distance as similarity measure, in particular a 2D-atom pair fingerprint (APfp) and the related category extended atom pair fingerprint (Xfp) which efficiently encode molecular shape and pharmacophores, but do not perceive stereochemistry. Here we investigated related 3D-atom pair fingerprints to enable rapid stereoselective searches in the ZINC database (23.2 million 3D structures).

RESULTS

Molecular fingerprints counting atom pairs at increasing through-space distance intervals were designed using either all atoms (16-bit 3DAPfp) or different atom categories (80-bit 3DXfp). These 3D-fingerprints retrieved molecular shape and pharmacophore analogs (defined by OpenEye ROCS scoring functions) of 110,000 compounds from the Cambridge Structural Database with equal or better accuracy than the 2D-fingerprints APfp and Xfp, and showed comparable performance in recovering actives from decoys in the DUD database. LBVS by 3DXfp or 3DAPfp similarity was stereoselective and gave very different analogs when starting from different diastereomers of the same chiral drug. Results were also different from LBVS with the parent 2D-fingerprints Xfp or APfp. 3D- and 2D-fingerprints also gave very different results in LBVS of folded molecules where through-space distances between atom pairs are much shorter than topological distances.

CONCLUSIONS

3DAPfp and 3DXfp are suitable for stereoselective searches for shape and pharmacophore analogs of query molecules in large databases. Web-browsers for searching ZINC by 3DAPfp and 3DXfp similarity are accessible at www.gdb.unibe.ch and should provide useful assistance to drug discovery projects. Graphical abstractAtom pair fingerprints based on through-space distances (3DAPfp) provide better shape encoding than atom pair fingerprints based on topological distances (APfp) as measured by the recovery of ROCS shape analogs by fp similarity.

Efficient exploration of chemical space by fragment-based screening

Screening methods seek to sample a vast chemical space in order to identify starting points for further chemical optimisation. Fragment based drug discovery exploits the superior sampling of chemical space that can be achieved when the molecular weight is restricted. Here we show that commercially available fragment space is still relatively poorly sampled and argue for highly sensitive screening methods to allow the detection of smaller fragments. We analyse the properties of our fragment library versus the properties of X-ray hits derived from the library. We particularly consider properties related to the degree of planarity of the fragments.

Composition and applications of focus libraries to phenotypic assays

The wealth of bioactivity information now available on low-molecular weight compounds has enabled a paradigm shift in chemical biology and early phase drug discovery efforts. Traditionally chemical libraries have been most commonly employed in screening approaches where a bioassay is used to characterize a chemical library in a random search for active samples. However, robust curating of bioassay data, establishment of ontologies enabling mining of large chemical biology datasets, and a wealth of public chemical biology information has made possible the establishment of highly annotated compound collections. Such annotated chemical libraries can now be used to build a pathway/target hypothesis and have led to a new view where chemical libraries are used to characterize a bioassay. In this article we discuss the types of compounds in these annotated libraries composed of tools, probes, and drugs. As well, we provide rationale and a few examples for how such libraries can enable phenotypic/forward chemical genomic approaches. As with any approach, there are several pitfalls that need to be considered and we also outline some strategies to avoid these.

Targeting the Wnt signaling pathways in pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a life-threatening disorder that is associated with elevated pulmonary pressures and right heart failure resulting from progressive loss and thickening of small pulmonary arteries. Despite their ability to improve symptoms, current therapies fail to prevent disease progression, leaving lung transplantation as the only therapy in end-stage PAH. To overcome the limitations of current therapies, there is an active search for disease-modifying agents capable of altering the natural history of, and improving clinical outcomes in, PAH. The Wnt signaling pathways have emerged as attractive treatment targets in PAH given their role in the preservation of pulmonary vascular homeostasis and the recent development of Wnt-specific compounds and biological therapies capable of modulating pathway activity. In this review, we summarize the literature describing the role of Wnt signaling in the pulmonary circulation and discuss promising advances in the field of Wnt therapeutics that could lead to novel clinical therapies capable of preventing and/or reversing pulmonary vascular pathology in patients with this devastating disease.

Discovery of potent positive allosteric modulators of the α3β2 nicotinic acetylcholine receptor by a chemical space walk in ChEMBL

While a plethora of ligands are known for the well studied α7 and α4β2 nicotinic acetylcholine receptor (nAChR), only very few ligands address the related α3β2 nAChR expressed in the central nervous system and at the neuromuscular junction. Starting with the public database ChEMBL organized in the chemical space of Molecular Quantum Numbers (MQN, a series of 42 integer value descriptors of molecular structure), a visual survey of nearest neighbors of the α7 nAChR partial agonist N-(3R)-1-azabicyclo[2.2.2]oct-3-yl-4-chlorobenzamide (PNU-282,987) pointed to N-(2-halobenzyl)-3-aminoquinuclidines as possible nAChR modulators. This simple "chemical space walk" was performed using a web-browser available at www.gdb.unibe.ch . Electrophysiological recordings revealed that these ligands represent a new and to date most potent class of positive allosteric modulators (PAMs) of the α3β2 nAChR, which also exert significant effects in vivo. The present discovery highlights the value of surveying chemical space neighbors of known drugs within public databases to uncover new pharmacology.

Oncogenic protein interfaces: small molecules, big challenges

Historically, targeting protein-protein interactions with small molecules was not thought possible because the corresponding interfaces were considered mostly flat and featureless and therefore 'undruggable'. Instead, such interactions were targeted with larger molecules, such as peptides and antibodies. However, the past decade has seen encouraging breakthroughs through the refinement of existing techniques and the development of new ones, together with the identification and exploitation of unexpected aspects of protein-protein interaction surfaces. In this Review, we describe some of the latest techniques to discover modulators of protein-protein interactions and how current drug discovery approaches have been adapted to successfully target these interfaces.

Low concentrations of the solvent dimethyl sulphoxide alter intrinsic excitability properties of cortical and hippocampal pyramidal cells

Dimethylsulfoxide (DMSO) is a widely used solvent in biology. It has many applications perhaps the most common of which is in aiding the preparation of drug solutions from hydrophobic chemical entities. Recent studies have suggested that this molecule may be able to induce apoptosis in neural tissues urging caution regarding its introduction into humans, for example as part of stem cell transplants. Here we have used in vitro electrophysiological methods applied to murine brain slices to examine whether a few hours treatment with 0.05% DMSO (a concentration regarded by many as innocuous) alters intrinsic excitability properties of neurones. We investigated pyramidal neurones in two distinct brain regions, namely area CA1 of the hippocampus and layer 2 of perirhinal cortex. In the former there was no effect on resting potential but input resistance was decreased by DMSO pre-treatment. In line with this action potential count for any level of depolarizing current stimulus was reduced by ∼25% following DMSO treatment. I_h-mediated “sag” was also increased in CA1 pyramids and action potential waveform analysis demonstrated that DMSO treatment moved action potential threshold towards resting potential. In perirhinal cortex a decreased action potential output for various depolarizing current stimuli was also seen. In these cells action potential threshold was unaltered by DMSO but a significant increase in action potential width was apparent. These data indicate that pre-treatment with this widely employed solvent can elicit multifaceted neurophysiological changes in mammalian neurones at concentrations below those frequently encountered in the published literature.

A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

Expanding the number of 'druggable' targets: non-enzymes and protein-protein interactions

Following sequencing and assembly of the human genome, the preferred methods for identification of new drug targets have changed dramatically. Modern tactics such as genome-wide association studies (GWAS) and deep sequencing are fundamentally different from the pharmacology-guided approaches used previously, in which knowledge of small molecule ligands acting at their cellular targets was the primary discovery engine. A consequence of the 'target-first, pharmacology-second' strategy is that many predicted drug targets are non-enzymes, such as scaffolding, regulatory or structural proteins, and their activities are often dependent on protein-protein interactions (PPIs). These types of targets create unique challenges to drug discovery efforts because enzymatic turnover cannot be used as a convenient surrogate for compound potency. Moreover, it is often challenging to predict how ligand binding to non-enzymes might affect changes in protein function and/or pathobiology. Thus, in the postgenomic era, targets might be strongly implicated by molecular biology-based methods, yet they often later earn the designation of 'undruggable'. Can the scope of available targets be widened to include these promising, but challenging, non-enzymes? In this review, we discuss advances in high-throughput screening (HTS) technology and chemical library design that are emerging to deal with these challenges.

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.

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 .

Broad coverage of commercially available lead-like screening space with fewer than 350,000 compounds

In establishing what we propose is the globe's highest quality collection of available screening compounds, it is convincingly shown that the globe's pool of such compounds is extremely shallow and can be represented by fewer than 350,000 compounds. To support our argument, we discuss and fully disclose our extensive battery of functional group filters. We discuss the use of PAINS filters and also show the effect of similarity exclusion on structure-activity relationships. We show why limited analogue representation requires screening at higher concentrations to capture hit classes for difficult targets that otherwise may be prosecuted unsuccessfully. We construct our arguments in a structurally focused manner to be most useful to medicinal chemists, the key players in drug discovery.

Big pharma screening collections: more of the same or unique libraries? The AstraZeneca-Bayer Pharma AG case

In this study, the screening collections of two major pharmaceutical companies (AstraZeneca and Bayer Pharma AG) have been compared using a 2D molecular fingerprint by a nearest neighborhood approach. Results revealed a low overlap between both collections in terms of compound identity and similarity. This emphasizes the value of screening multiple compound collections to expand the chemical space that can be accessed by high-throughput screening (HTS).

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.

Fine-tuning multiprotein complexes using small molecules

Multiprotein complexes such as the transcriptional machinery, signaling hubs, and protein folding machines are typically composed of at least one enzyme combined with multiple non-enzymes. Often the components of these complexes are incorporated in a combinatorial manner, in which the ultimate composition of the system helps dictate the type, location, or duration of cellular activities. Although drugs and chemical probes have traditionally targeted the enzyme components, emerging strategies call for controlling the function of protein complexes by modulation of protein-protein interactions (PPIs). However, the challenges of targeting PPIs have been well documented, and the diversity of PPIs makes a "one-size-fits-all" solution highly unlikely. These hurdles are particularly daunting for PPIs that encompass large buried surface areas and those with weak affinities. In this Review, we discuss lessons from natural systems, in which allostery and other mechanisms are used to overcome the challenge of regulating the most difficult PPIs. These systems may provide a blueprint for identifying small molecules that target challenging PPIs and affecting molecular decision-making within multiprotein systems.

Established and emerging trends in computational drug discovery in the structural genomics era

Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.

A collection of robust organic synthesis reactions for in silico molecule design

A focused collection of organic synthesis reactions for computer-based molecule construction is presented. It is inspired by real-world chemistry and has been compiled in close collaboration with medicinal chemists to achieve high practical relevance. Virtual molecules assembled from existing starting material connected by these reactions are supposed to have an enhanced chance to be amenable to real chemical synthesis. About 50% of the reactions in the dataset are ring-forming reactions, which fosters the assembly of novel ring systems and innovative chemotypes. A comparison with a recent survey of the reactions used in early drug discovery revealed considerable overlaps with the collection presented here. The dataset is available encoded as computer-readable Reaction SMARTS expressions from the Supporting Information presented for this paper.