Lead-like, drug-like or "Pub-like": how different are they?

Screening Marine Microbial Metabolites as Promising Inhibitors of Borrelia garinii: A Structural Docking Approach towards Developing Novel Lyme Disease Treatment

Lyme disease caused by the Borrelia species is a growing health concern in many parts of the world. Current treatments for the disease may have side effects, and there is also a need for new therapies that can selectively target the bacteria. Pathogens responsible for Lyme disease include B. burgdorferi, B. afzelii, and B. garinii. In this study, we employed structural docking-based screening to identify potential lead-like inhibitors against the bacterium. We first identified the core essential genome fraction of the bacterium, using 37 strains. Later, we screened a library of lead-like marine microbial metabolites (n = 4730) against the arginine deiminase (ADI) protein of Borrelia garinii. This protein plays a crucial role in the survival of the bacteria, and inhibiting it can kill the bacterium. The prioritized lead compounds demonstrating favorable binding energies and interactions with the active site of ADI were then evaluated for their drug-like and pharmacokinetic parameters to assess their suitability for development as drugs. Results from molecular dynamics simulation (100 ns) and other scoring parameters suggest that the compound CMNPD18759 (common name: aureobasidin; IUPAC name: 2-[(4R,6R)-4,6-dihydroxydecanoyl]oxypropan-2-yl (3S,5R)-3,5-dihydroxydecanoate) holds promise as a potential drug candidate for the treatment of Lyme disease, caused by B. garinii. However, further experimental studies are needed to validate the efficacy and safety of this compound in vivo.

MRlogP: Transfer Learning Enables Accurate logP Prediction Using Small Experimental Training Datasets

Small molecule lipophilicity is often included in generalized rules for medicinal chemistry. These rules aim to reduce time, effort, costs, and attrition rates in drug discovery, allowing the rejection or prioritization of compounds without the need for synthesis and testing. The availability of high quality, abundant training data for machine learning methods can be a major limiting factor in building effective property predictors. We utilize transfer learning techniques to get around this problem, first learning on a large amount of low accuracy predicted logP values before finally tuning our model using a small, accurate dataset of 244 druglike compounds to create MRlogP, a neural network-based predictor of logP capable of outperforming state of the art freely available logP prediction methods for druglike small molecules. MRlogP achieves an average root mean squared error of 0.988 and 0.715 against druglike molecules from Reaxys and PHYSPROP. We have made the trained neural network predictor and all associated code for descriptor generation freely available. In addition, MRlogP may be used online via a web interface.

Aniline-containing derivatives of parthenolide: Synthesis and anti-chronic lymphocytic leukaemia activity

Parthenolide exhibits anti-leukaemia activity, whilst its synthetic modification to impart improve drug-like properties, including 1,4-conjugate addition of primary and secondary amines, have previously been used, 1,4-addition of aniline derivatives to parthenolide has not been fully explored. A protocol for such additions to parthenolide is outlined herein. Reaction conditions were determined using tulipane as a model Michael acceptor. Subsequently, aniline-containing parthenolide derivatives were prepared under the optimised conditions and single crystal X-ray diffraction structures were resolved for three of the compounds synthesised. The synthesised derivatives, along with compounds resulting from a side reaction, were tested for their in vitro anti-leukaemia activity using the chronic lymphocytic leukaemia (CLL) MEC1 cell line. Computational studies with the 2RAM protein structure suggested that the activity of the derivatives was independent of their in silico ability to dock with the Cys38 residue of NF-κB.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

How to Prepare a Compound Collection Prior to Virtual Screening

Virtual screening is a well-established technique that has proven to be successful in the identification of novel biologically active molecules, including drug repurposing. Whether for ligand-based or for structure-based virtual screening, a chemical collection needs to be properly processed prior to in silico evaluation. Here we describe our step-by-step procedure for handling very large collections (up to billions) of compounds prior to virtual screening.

High Throughput and Computational Repurposing for Neglected Diseases

Purpose

Neglected tropical diseases (NTDs) represent are a heterogeneous group of communicable diseases that are found within the poorest populations of the world. There are 23 NTDs that have been prioritized by the World Health Organization, which are endemic in 149 countries and affect more than 1.4 billion people, costing these developing economies billions of dollars annually. The NTDs result from four different causative pathogens: protozoa, bacteria, helminth and virus. The majority of the diseases lack effective treatments. Therefore, new therapeutics for NTDs are desperately needed.

Methods

We describe various high throughput screening and computational approaches that have been performed in recent years. We have collated the molecules identified in these studies and calculated molecular properties.

Results

Numerous global repurposing efforts have yielded some promising compounds for various neglected tropical diseases. These compounds when analyzed as one would expect appear drug-like. Several large datasets are also now in the public domain and this enables machine learning models to be constructed that then facilitate the discovery of new molecules for these pathogens.

Conclusions

In the space of a few years many groups have either performed experimental or computational repurposing high throughput screens against neglected diseases. These have identified compounds which in many cases are already approved drugs. Such approaches perhaps offer a more efficient way to develop treatments which are generally not a focus for global pharmaceutical companies because of the economics or the lack of a viable market. Other diseases could perhaps benefit from these repurposing approaches.

Electronic supplementary material

The online version of this article (10.1007/s11095-018-2558-3) contains supplementary material, which is available to authorized users.

2,4-Diaminothieno[3,2-d]pyrimidines, a new class of anthelmintic with activity against adult and egg stages of whipworm

The human whipworm Trichuris trichiura is a parasite that infects around 500 million people globally, with consequences including damage to physical growth and educational performance. Current drugs such as mebendazole have a notable lack of efficacy against whipworm, compared to other soil-transmitted helminths. Mass drug administration programs are therefore unlikely to achieve eradication and new treatments for trichuriasis are desperately needed. All current drug control strategies focus on post-infection eradication, targeting the parasite in vivo. Here we propose developing novel anthelmintics which target the egg stage of the parasite in the soil as an adjunct environmental strategy. As evidence in support of such an approach we describe the actions of a new class of anthelmintic compounds, the 2,4-diaminothieno[3,2-d]pyrimidines (DATPs). This compound class has found broad utility in medicinal chemistry, but has not previously been described as having anthelmintic activity. Importantly, these compounds show efficacy against not only the adult parasite, but also both the embryonated and unembryonated egg stages and thereby may enable a break in the parasite lifecycle.

The human whipworm, Trichuris trichiura, infects around 500 million people globally, impacting on their physical growth and educational performance. There are currently huge mass drug administration (MDA) programs aiming to control whipworm, along with the other major soil transmitted helminths, Ascaris and hookworm. However single doses of albendazole and mebendazole, which are used in MDA, have particularly poor effectiveness against whipworm, with cure rates less than 40%. This means that MDA may not be able to control and eliminate whipworm infection, and risks the spread of resistance to albendazole and mebendazole in the parasite population. We are attempting to develop new treatments for parasitic worm infection, particularly focused on whipworm. We report the identification of a class of compounds, diaminothienopyrimidines (DATPs), which have not previously been described as anthelmintics. These compounds are effective against adult stages of whipworm, and also block the development of the model nematode C. elegans. Our DATP compounds reduce the ability of treated eggs to successfully establish infection in a mouse model of human whipworm. These results support a potential environmental spray to control whipworm by targeting the infectious egg stage in environmental hotspots.

Highly Stereoselective Synthesis of a Compound Collection Based on the Bicyclic Scaffolds of Natural Products

Despite the great contribution of natural products in the history of successful drug discovery, there are significant limitations that persuade the pharmaceutical industry to evade natural products in drug discovery research. The extreme scarcity as well as structural complexity of natural products renders their practical synthetic access and further modifications extremely challenging. Although other alternative technologies, particularly combinatorial chemistry, were embraced by the pharmaceutical industry to get quick access to a large number of small molecules with simple frameworks that often lack three-dimensional complexity, hardly any success was achieved in the discovery of lead molecules. To acquire chemotypes beholding structural features of natural products, for instance high sp³ character, the synthesis of compound collections based on core-scaffolds of natural products presents a promising strategy. Here, we report a natural product inspired synthesis of six different chemotypes and their derivatives for drug discovery research. These bicyclic hetero- and carbocyclic scaffolds are highly novel, rich in sp³ features and with ideal physicochemical properties to display drug likeness. The functional groups on the scaffolds were exploited further to generate corresponding compound collections. Synthesis of two of these collections exemplified with ca. 350 compounds are each also presented. The whole compound library is being exposed to various biological screenings within the European Lead Factory consortium.

Design and Elaboration of a Tractable Tricyclic Scaffold To Synthesize Druglike Inhibitors of Dipeptidyl Peptidase-4 (DPP-4), Antagonists of the C-C Chemokine Receptor Type 5 (CCR5), and Highly Potent and Selective Phosphoinositol-3 Kinase δ (PI3Kδ) Inhibitors

A novel molecular scaffold has been synthesized, and its incorporation into new analogues of biologically active molecules across multiple target classes will be discussed. In these studies, we have shown use of the tricyclic scaffold to synthesize potent inhibitors of the serine peptidase DPP-4, antagonists of the CCR5 receptor, and highly potent and selective PI3K δ isoform inhibitors. We also describe the predicted physicochemical properties of the resulting inhibitors and conclude that the tractable molecular scaffold could have potential application in future drug discovery programs.

Computational Analysis of the Domain Architecture and Substrate-Gating Mechanism of Prolyl Oligopeptidases from Shewanella woodyi and Identification of Probable Lead Molecules

Prolyl oligopeptidases (POPs) are serine proteases found in prokaryotes and eukaryotes which hydrolyze the peptide bond containing proline. The current study focuses on the analysis of POP sequences, their distribution and domain architecture in Shewanella woodyi, a Gram-negative, luminous bacterium which causes celiac sprue and similar infections in marine organisms. The POP undergoes huge interdomain movement, which allows possible route for the entry of any substrate. Hence, it offers an opportunity to understand the mechanism of substrate gating by studying the domain architecture and possibility to identify a probable drug target. In the present study, the POP sequence was retrieved from GenBank database and the best homologous templates were identified by PSI-BLAST search. The three-dimensional structures of the closed and open forms of POP from S. woodyi, which are not available in native form, were generated by homology modeling. The ideal lead molecules were screened by computer-aided virtual screening, and the binding potential of the best leads toward the target was studied by molecular docking. The domain architecture of the POP revealed that it has a propeller domain consists of [Formula: see text]-sheets, surrounded by [Formula: see text]-helices and [Formula: see text] hydrolase domain with catalytic triad containing Ser-564, Asp-646 and His-681. The hypothetical models of open and closed POP showed backbone RMSD value of 0.56 and 0.65 Å, respectively. Ramachandran plot of the open and closed POP conformations accounts for 99.4 and 98.7 % residues in the favoured region, respectively. Our study revealed that propeller domain comes as an insert between N-terminal and C-terminal [Formula: see text] hydrolase domain. Molecular docking, drug likeness properties and ADME prediction suggested that KUC-103481N and Pramiracetum can be used as probable lead molecules toward the POP from S. woodyi.

Getting the most out of PubChem for virtual screening

INTRODUCTION

With the emergence of the 'big data' era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge.

AREAS COVERED

This article provides an overview of how PubChem's data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery.

EXPERT OPINION

PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.

Drug-like Antagonists of P2Y Receptors-From Lead Identification to Drug Development

P2Y receptors are expressed in virtually all cells and tissue types and mediate an astonishing array of biological functions, including platelet aggregation, smooth muscle cell proliferation, and immune regulation. The P2Y receptors belong to the G protein-coupled receptor superfamily and are composed of eight members encoded by distinct genes that can be subdivided into two groups on the basis of their coupling to specific G-proteins. Extensive research has been undertaken to find modulators of P2Y receptors, although to date only a limited number of small-molecule P2Y receptor antagonists have been approved by drug/medicines agencies. This Perspective reviews the known P2Y receptor antagonists, highlighting oral drug-like receptor antagonists, and considers future opportunities for the development of small molecules for clinical evaluation.

Drug combination therapy increases successful drug repositioning

Repositioning of approved drugs has recently gained new momentum for rapid identification and development of new therapeutics for diseases that lack effective drug treatment. Reported repurposing screens have increased dramatically in number in the past five years. However, many newly identified compounds have low potency; this limits their immediate clinical applications because the known, tolerated plasma drug concentrations are lower than the required therapeutic drug concentrations. Drug combinations of two or more compounds with different mechanisms of action are an alternative approach to increase the success rate of drug repositioning.

Secondary metabolites extracted from marine sponge associated Comamonas testosteroni and Citrobacter freundii as potential antimicrobials against MDR pathogens and hypothetical leads for VP40 matrix protein of Ebola virus: an in vitro and in silico investigation

The current study explores therapeutic potential of metabolites extracted from marine sponge (Cliona sp.)-associated bacteria against MDR pathogens and predicts the binding prospective of probable lead molecules against VP40 target of Ebola virus. The metabolite-producing bacteria were characterized by agar overlay assay and as per the protocols in Bergey's manual of determinative bacteriology. The antibacterial activities of extracted metabolites were tested against clinical pathogens by well-diffusion assay. The selected metabolite producers were characterized by 16S rDNA sequencing. Chemical screening and Fourier Transform Infrared (FTIR) analysis for selected compounds were performed. The probable lead molecules present in the metabolites were hypothesized based on proximate analysis, FTIR data, and literature survey. The drug-like properties and binding potential of lead molecules against VP40 target of Ebola virus were hypothesized by computational virtual screening and molecular docking. The current study demonstrated that clear zones around bacterial colonies in agar overlay assay. Antibiotic sensitivity profiling demonstrated that the clinical isolates were multi-drug resistant, however; most of them showed sensitivity to secondary metabolites (MIC-15 μl/well). The proximate and FTIR analysis suggested that probable metabolites belonged to alkaloids with O-H, C-H, C=O, and N-H groups. 16S rDNA characterization of selected metabolite producers demonstrated that 96% and 99% sequence identity to Comamonas testosteroni and Citrobacter freundii, respectively. The docking studies suggested that molecules such as Gymnastatin, Sorbicillactone, Marizomib, and Daryamide can designed as probable lead candidates against VP40 target of Ebola virus.

Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites

BACKGROUND

A recent comparison showed the extensive similarities between the structural properties of metabolites in the reconstructed human metabolic network ("endogenites") and those of successful, marketed drugs ("drugs").

RESULTS

Clustering indicated the related but differential population of chemical space by endogenites and drugs. Differences between the drug-endogenite similarities resulting from various encodings and judged by Tanimoto similarity could be related simply to the fraction of the bitstrings set to 1. By extracting drug/endogenite substructures, we develop a novel family of fingerprints, the Drug Endogenite Substructure (DES) encodings, based on the ranked frequency of the various substructures. These provide a natural assessment of drug-endogenite likeness, and may be used as descriptors with which to derive quantitative structure-activity relationships (QSARs).

CONCLUSIONS

"Drug-endogenite likeness" seems to have utility, and leads to a simple, novel and interpretable substructure-based molecular encoding for cheminformatics.

Computational analysis of the domain architecture and substrate-gating mechanism of prolyl oligopeptidases from Shewanella woodyi and identification probable lead molecules

Prolyl oligopeptidases (POP) are serine proteases found in prokaryotes and eukaryotes which hydrolyze the peptide bond containing proline. The current study focuses on the analysis of POP sequences, their distribution and domain architecture in Shewanella woodyi, a Gram negative, luminous bacterium which causes celiac sprue and similar infections in marine organisms. The POP undergoes huge inter-domain movement, which allows possible route for the entry of any substrate. Hence, it offers an opportunity to understand the mechanism of substrate gating by studying the domain architecture and possibility to identify a probable drug target. In the present study, the POP sequence was retrieved from GenBank data base and the best homologous templates were identified by PSI-BLAST search. The three dimensional structures of the closed and open forms of POP from Shewanella woodyi, which are not available in native form, was generated by homology modeling. The ideal lead molecules were screened by computer aided virtual screening and the binding potential of the best leads towards the target was studied by molecular docking. The domain architecture of the POP revealed that, it has a propeller domain consist of β-sheets, surrounded by α-helices and α/β hydrolase domain with catalytic triad containing Ser-564, Asp-646 and His-681. The hypothetical models of open and closed POP showed backbone RMSD value of 0.56 Å and 0.65 Å respectively. Ramachandran plot of the open and closed POP conformations accounts for 99.4% and 98.7% residues in the favoured region respectively. Our study revealed that, propeller domain comes as an insert between N-terminal and C-terminal α/β hydrolase domain. Molecular docking, drug likeliness properties and ADME prediction suggested that KUC-103481N and Pramiracetum can be used as probable lead molecules towards the POP from Shewanella woodyi.

Attachment of ribonucleotides on α-alumina as a function of pH, ionic strength, and surface loading

The interactions between nucleic acids and mineral surfaces have been the focus of many studies in environmental sciences, in biomedicine, as well as in origin of life studies for the prebiotic formation of biopolymers. However, few studies have focused on a wide range of environmental conditions and the likely modes of attachment. Here we investigated the adsorption of ribonucleotides onto α-alumina surfaces over a wide range of pH, ionic strength, and ligand-to-solid ratio, by both an experimental and a theoretical approach. The adsorption of ribonucleotides is strongly affected by pH, with a maximum adsorption at pH values around 5. Alumina adsorbs high amounts of nucleotides >2 μmol/m(2). We used the extended triple-layer model (ETLM) to predict the speciation of the surface complexes formed as well as the stoichiometry and equilibrium constants. We propose the formation of two surface species: a monodentate inner-sphere complex, dominant at pH <7, and a bidentate outer-sphere complex, dominant at higher pH. Both complexes would involve interactions between the negatively charged phosphate group and the positively charged surface of alumina. Our results provide a better understanding of how nucleic acids attach to mineral surfaces under varying environmental conditions. Moreover, the predicted configuration of nucleotide surface species, bound via the phosphate group, could have implications for the abiotic formation of nucleic acids in the context of the origin of life.

A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs

We exploit the recent availability of a community reconstruction of the human metabolic network ('Recon2') to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) 'nearest' human metabolite. This suggests a 'rule of 0.5' mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This 'rule of 0.5' may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.

Compound collection preparation for virtual screening

Virtual screening is an established technique that has successfully been deployed in the identification of novel biologically active molecules. Whether for ligand-based or for structure-based virtual screening, a chemical collection needs to be properly processed prior to in silico evaluation. Here we describe our step-by-step procedure for handling large collections of compounds prior to virtual screening.

Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it

Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to 'function-first' or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the 'omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially.

Development of a natural products database from the biodiversity of Brazil

We describe herein the design and development of an innovative tool called the NuBBE database (NuBBEDB), a new Web-based database, which incorporates several classes of secondary metabolites and derivatives from the biodiversity of Brazil. This natural product database incorporates botanical, chemical, pharmacological, and toxicological compound information. The NuBBEDB provides specialized information to the worldwide scientific community and can serve as a useful tool for studies on the multidisciplinary interfaces related to chemistry and biology, including virtual screening, dereplication, metabolomics, and medicinal chemistry. The NuBBEDB site is at http://nubbe.iq.unesp.br/nubbeDB.html .

Chemoinformatics and chemical genomics: potential utility of in silico methods

Computational life sciences and informatics are inseparably intertwined and they lie at the heart of modern biology, predictive quantitative modeling and high-performance computing. Two of the applied biological disciplines that are poised to benefit from such progress are pharmacology and toxicology. This review will describe in silico chemoinformatics methods such as (quantitative) structure-activity relationship modeling and will overview how chemoinformatic technologies are considered in applied regulatory research. Given the post-genomics era and large-scale repositories of omics data that are available, this review will also address potential applications of in silico techniques in chemical genomics. Chemical genomics utilizes small molecules to explore the complex biological phenomena that may not be not amenable to straightforward genetic approach. The reader will gain the understanding that chemoinformatics stands at the interface of chemistry and biology with enabling systems for mapping, statistical modeling, pattern recognition, imaging and database tools. The great potential of these technologies to help address complex issues in the toxicological sciences is appreciated with the applied goal of the protection of public health.

A prospective cross-screening study on G-protein-coupled receptors: lessons learned in virtual compound library design

We present the systematic prospective evaluation of a protein-based and a ligand-based virtual screening platform against a set of three G-protein-coupled receptors (GPCRs): the β-2 adrenoreceptor (ADRB2), the adenosine A(2A) receptor (AA2AR), and the sphingosine 1-phosphate receptor (S1PR1). Novel bioactive compounds were identified using a consensus scoring procedure combining ligand-based (frequent substructure ranking) and structure-based (Snooker) tools, and all 900 selected compounds were screened against all three receptors. A striking number of ligands showed affinity/activity for GPCRs other than the intended target, which could be partly attributed to the fuzziness and overlap of protein-based pharmacophore models. Surprisingly, the phosphodiesterase 5 (PDE5) inhibitor sildenafil was found to possess submicromolar affinity for AA2AR. Overall, this is one of the first published prospective chemogenomics studies that demonstrate the identification of novel cross-pharmacology between unrelated protein targets. The lessons learned from this study can be used to guide future virtual ligand design efforts.

Early phase drug discovery: cheminformatics and computational techniques in identifying lead series

Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration.

BDDCS class prediction for new molecular entities

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction.

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ∼75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.

Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces

High-throughput screening (HTS) is a well-established technology which can test up to several million compounds in a few weeks. Despite these appealing capabilities, available resources and high costs may limit the number of molecules screened, making diversity analysis a method of choice to design and prioritize screening libraries. With a constantly increasing number of molecules available for screening, chemical space has become a key concept for visualizing, analyzing, and comparing chemical libraries. In this first article, we present a new method to build delimited reference chemical subspaces (DRCS). A set of 16 million screening compounds from 73 chemical providers has been gathered, resulting in a database of 6.63 million standardized and unique molecules. These molecules have been used to create three DRCS using three different sets of chemical descriptors. A robust principal component analysis model for each space has been obtained, whereby molecules are projected in a reduced two-dimensional viewable space. The specificity of our approach is that each reduced space has been delimited by a representative contour encompassing a very large proportion of molecules and reflecting its overall shape. The methodology is illustrated by mapping and comparing various chemical libraries. Several tools used in these studies are made freely available, thus enabling any user to compute DRCS matching specific requirements.

Atom type preferences, structural diversity, and property profiles of known drugs, leads, and nondrugs: a comparative assessment

A new characterization of known drug, lead, and representative nondrug databases was performed taking into account several properties at the atomic and molecular levels. This characterization included atom type preferences, intrinsic structural diversity (Atom Type Diversity, ATD), and other well-known physicochemical properties, as an approach for rapid assessment of druglikeness for small molecule libraries. To characterize ATD, an elaborate united atom classification, UALOGP (United Atom Log P), with 148 atom types, was developed along with associated atomic physicochemical parameters. This classification also enabled an analysis of atom type and physicochemical property distributions (for calculated log P, molar refractivity, molecular weight, total atom count, and ATD) of drug, lead, and nondrug databases, a reassessment of the Ro5 (Rule of Five) and GVW (Ghose−Viswanadhan−Wendoloski) criteria, and development of new criteria and ranges more accurately reflecting the chemical space occupied by small molecule drugs. A relative druglikeness parameter was defined for atom types in drugs, identifying the most preferred types. The present work demonstrates that drug molecules are constitutionally more diverse relative to nondrugs, while being less diverse than leads.

Probing the links between in vitro potency, ADMET and physicochemical parameters

A common underlying assumption in current drug discovery strategies is that compounds with higher in vitro potency at their target(s) have greater potential to translate into successful, low-dose therapeutics. This has led to the development of screening cascades with in vitro potency embedded as an early filter. However, this approach is beginning to be questioned, given the bias in physicochemical properties that it can introduce early in lead generation and optimization, which is due to the often diametrically opposed relationship between physicochemical parameters associated with high in vitro potency and those associated with desirable absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Here, we describe analyses that probe these issues further using the ChEMBL database, which includes more than 500,000 drug discovery and marketed oral drug compounds. Key findings include: first, that oral drugs seldom possess nanomolar potency (50 nM on average); second, that many oral drugs have considerable off-target activity; and third, that in vitro potency does not correlate strongly with the therapeutic dose. These findings suggest that the perceived benefit of high in vitro potency may be negated by poorer ADMET properties.

Quantifying structure and performance diversity for sets of small molecules comprising small-molecule screening collections

Using a diverse collection of small molecules we recently found that compound sets from different sources (commercial; academic; natural) have different protein-binding behaviors, and these behaviors correlate with trends in stereochemical complexity for these compound sets. These results lend insight into structural features that synthetic chemists might target when synthesizing screening collections for biological discovery. We report extensive characterization of structural properties and diversity of biological performance for these compounds and expand comparative analyses to include physicochemical properties and three-dimensional shapes of predicted conformers. The results highlight additional similarities and differences between the sets, but also the dependence of such comparisons on the choice of molecular descriptors. Using a protein-binding dataset, we introduce an information-theoretic measure to assess diversity of performance with a constraint on specificity. Rather than relying on finding individual active compounds, this measure allows rational judgment of compound subsets as groups. We also apply this measure to publicly available data from ChemBank for the same compound sets across a diverse group of functional assays. We find that performance diversity of compound sets is relatively stable across a range of property values as judged by this measure, both in protein-binding studies and functional assays. Because building screening collections with improved performance depends on efficient use of synthetic organic chemistry resources, these studies illustrate an important quantitative framework to help prioritize choices made in building such collections.

Recent trends and observations in the design of high-quality screening collections

The design of a high-quality screening collection is of utmost importance for the early drug-discovery process and provides, in combination with high-quality assay systems, the foundation of future discoveries. Herein, we review recent trends and observations to successfully expand the access to bioactive chemical space, including the feedback from hit assessment interviews of high-throughput screening campaigns; recent successes with chemogenomics target family approaches, the identification of new relevant target/domain families, diversity-oriented synthesis and new emerging compound classes, and non-classical approaches, such as fragment-based screening and DNA-encoded chemical libraries. The role of in silico library design approaches are emphasized.

A common feature-based 3D-pharmacophore model generation and virtual screening: identification of potential PfDHFR inhibitors

A four-feature 3D-pharmacophore model was built from a set of 24 compounds whose activities were reported against the V1/S strain of the Plasmodium falciparum dihydrofolate reductase (PfDHFR) enzyme. This is an enzyme harboring Asn51Ile + Cys59Arg + Ser108Asn + Ile164Leu mutations. The HipHop module of the Catalyst program was used to generate the model. Selection of the best model among the 10 hypotheses generated by HipHop was carried out based on rank and best-fit values or alignments of the training set compounds onto a particular hypothesis. The best model (hypo1) consisted of two H-bond donors, one hydrophobic aromatic, and one hydrophobic aliphatic features. Hypo1 was used as a query to virtually screen Maybridge2004 and NCI2000 databases. The hits obtained from the search were subsequently subjected to FlexX and Glide docking studies. Based on the binding scores and interactions in the active site of quadruple-mutant PfDHFR, a set of nine hits were identified as potential inhibitors.

Are there differences between launched drugs, clinical candidates, and commercially available compounds?

To clarify the differences between commercially available compounds, clinical candidates, and launched drugs with regard to distribution of physicochemical properties and to characterize the correlation between physicochemical properties, we conducted analyses on physicochemical descriptors of commercially available compounds, clinical candidates, and launched drugs. Initial analysis of the marginal distribution of each physicochemical property showed that the distribution of commercially available compounds obeys a more normal distribution than that of launched drugs and clinical candidates. In addition, we calculated correlation coefficient values between values of physicochemical properties and found little similarity between values of clinical candidates and those of commercially available compounds, while observing marked similarity between values of clinical candidates and those of launched drugs. We also analyzed joint distribution for two physicochemical properties, with results showing that, similar to marginal distribution, the joint distribution of commercially available compounds obeys a more normal distribution than that of launched drugs and clinical candidates. We then assessed items using the Nagahara method, originally developed by one of this study's authors. Results showed that the probability distribution of molecular weight and log P for commercially available compounds was much narrower than that of launched drugs and clinical candidates. In conclusion, clinical candidates are more similar to launched drugs than to commercially available compounds with regard to marginal distribution, joint distribution, and correlation coefficients. These findings provide deeper insight regarding the concept of "druglikeness".

Ligand efficiency indices for an effective mapping of chemico-biological space: the concept of an atlas-like representation

We propose a numerical framework that permits an effective atlas-like representation of chemico-biological space based on a series of Cartesian planes mapping the ligands with the corresponding targets connected by an affinity parameter (K(i) or related). The numerical framework is derived from the concept of ligand efficiency indices, which provide a natural coordinate system combining the potency toward the target (biological space) with the physicochemical properties of the ligand (chemical space). This framework facilitates navigation in the multidimensional drug discovery space using map-like representations based on pairs of combined variables related to the efficiency of the ligands per Dalton (molecular weight or number of non-hydrogen atoms) and per unit of polar surface area (or number of polar atoms).

Probing the probes: fitness factors for small molecule tools

Chemical probes for interrogating biological processes are of considerable current interest. Cell permeable small molecule tools have a major role in facilitating the functional annotation of the human genome, understanding both physiological and pathological processes, and validating new molecular targets. To be valuable, chemical tools must satisfy necessary criteria and recent publications have suggested objective guidelines for what makes a useful chemical probe. Although recognizing that such guidelines may be valuable, we caution against overly restrictive rules that may stifle innovation in favor of a "fit-for-purpose" approach. Reviewing the literature and providing examples from the cancer field, we recommend a series of "fitness factors" to be considered when assessing chemical probes. We hope this will encourage innovative chemical biology research while minimizing the generation of poor quality and misleading biological data, thus increasing understanding of the particular biological area, to the benefit of basic research and drug discovery.