A comparison of ligand based virtual screening methods and application to corticotropin releasing factor 1 receptor

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions

New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.

Scaffold Hopping to Imidazo[1,2-a]pyrazin-8-one Positive Allosteric Modulators of Metabotropic Glutamate 2 Receptor

Glutamate hyperfunction is implicated in multiple neurological and psychiatric diseases. Activation of the mGlu2 receptor results in reduced glutamate release and decreased excitability representing a promising novel therapeutic agent for the treatment of disorders such as epilepsy, schizophrenia, mood, anxiety, and other neuropsychiatric disorders. We have previously reported substantial efforts leading to potent and selective mGlu2 PAMs from different chemical series. Herein, the discovery and optimization of a novel series of imidazopyrazinone mGlu2 PAMs are reported. This new scaffold originated from computational searching of fragment databases and comparison with our previously explored scaffolds. Optimization guided by our robust understanding of SAR from former series led to potent, selective, and brain-penetrant compounds.

Pharmacophore-based screening of diamidine small molecule inhibitors for protein arginine methyltransferases

Protein arginine methyltransferases (PRMTs) are essential epigenetic and post-translational regulators in eukaryotic organisms. Dysregulation of PRMTs is intimately related to multiple types of human diseases, particularly cancer. Based on the previously reported PRMT1 inhibitors bearing the diamidine pharmacophore, we performed virtual screening to identify additional amidine-associated structural analogs. Subsequent enzymatic tests and characterization led to the discovery of a top lead K313 (2-(4-((4-carbamimidoylphenyl)amino)phenyl)-1H-indole-6-carboximidamide), which possessed low-micromolar potency with biochemical IC50 of 2.6 μM for human PRMT1. Limited selectivity was observed over some other PRMT isoforms such as CARM1 and PRMT7. Molecular modeling and inhibition pattern studies suggest that K313 is a nonclassic noncompetitive inhibitor to PRMT1. K313 significantly inhibited cell proliferation and reduced the arginine asymmetric dimethylation level in the leukaemia cancer cells.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Spiro-oxindole Piperidines and 3-(Azetidin-3-yl)-1H-benzimidazol-2-ones as mGlu2 Receptor PAMs

Starting from two weak mGlu₂ receptor positive allosteric modulator (PAM) HTS hits (4 and 5), a molecular hybridization strategy resulted in the identification of a novel spiro-oxindole piperidine series with improved activity and metabolic stability. Scaffold hopping around the spiro-oxindole core identified the 3-(azetidin-3-yl)-1H-benzimidazol-2-one as bioisoster. Medicinal chemistry optimization of these two novel chemotypes resulted in the identification of potent, selective, orally bioavailable, and brain penetrant mGluR₂ PAMs.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

Analysing and Navigating Natural Products Space for Generating Small, Diverse, But Representative Chemical Libraries

Armed with the digital availability of two natural products libraries, amounting to some 195 885 molecular entities, we ask the question of how we can best sample from them to maximize their "representativeness" in smaller and more usable libraries of 96, 384, 1152, and 1920 molecules. The term "representativeness" is intended to include diversity, but for numerical reasons (and the likelihood of being able to perform a QSAR) it is necessary to focus on areas of chemical space that are more highly populated. Encoding chemical structures as fingerprints using the RDKit "patterned" algorithm, we first assess the granularity of the natural products space using a simple clustering algorithm, showing that there are major regions of "denseness" but also a great many very sparsely populated areas. We then apply a "hybrid" hierarchical K-means clustering algorithm to the data to produce more statistically robust clusters from which representative and appropriate numbers of samples may be chosen. There is necessarily again a trade-off between cluster size and cluster number, but within these constraints, libraries containing 384 or 1152 molecules can be found that come from clusters that represent some 18 and 30% of the whole chemical space, with cluster sizes of, respectively, 50 and 27 or above, just about sufficient to perform a QSAR. By using the online availability of molecules via the Molport system (www.molport.com), we are also able to construct (and, for the first time, provide the contents of) a small virtual library of available molecules that provided effective coverage of the chemical space described. Consistent with this, the average molecular similarities of the contents of the libraries developed is considerably smaller than is that of the original libraries. The suggested libraries may have use in molecular or phenotypic screening, including for determining possible transporter substrates.

Industrial medicinal chemistry insights: neuroscience hit generation at Janssen

The role of medicinal chemistry has changed over the past 10 years. Chemistry had become one step in a process; funneling the output of high-throughput screening (HTS) on to the next stage. The goal to identify the ideal clinical compound remains, but the means to achieve this have changed. Modern medicinal chemistry is responsible for integrating innovation throughout early drug discovery, including new screening paradigms, computational approaches, novel synthetic chemistry, gene-family screening, investigating routes of delivery, and so on. In this Foundation Review, we show how a successful medicinal chemistry team has a broad impact and requires multidisciplinary expertise in these areas.

Discovery of selective inhibitors of tyrosyl-DNA phosphodiesterase 2 by targeting the enzyme DNA-binding cleft

Tyrosyl-DNA phosphodiesterase 2 (TDP2) processes protein/DNA adducts resulting from abortive DNA topoisomerase II (Top2) activity. TDP2 inhibition could provide synergism with the Top2 poison class of chemotherapeutics. By virtual screening of the NCI diversity small molecule database, we identified selective TDP2 inhibitors and experimentally verified their selective inhibitory activity. Three inhibitors exhibited low-micromolar IC50 values. Molecular dynamics simulations revealed a common binding mode for these inhibitors, involving association to the TDP2 DNA-binding cleft. MM-PBSA per-residue energy decomposition identified important interactions of the compounds with specific TDP2 residues. These interactions could provide new avenues for synthetic optimization of these scaffolds.

Structural dynamics and inhibitor searching for Wnt-4 protein using comparative computational studies

Wnt-4 (wingless mouse mammary tumor virus integration site-4) protein is involved in many crucial embryonic pathways regulating essential processes. Aberrant Wnt-4 activity causes various anomalies leading to gastric, colon, or breast cancer. Wnt-4 is a conserved protein in structure and sequence. All Wnt proteins contain an unusual fold comprising of a thumb (or N-terminal domain) and index finger (or C-terminal domain) bifurcated by a palm domain. The aim of this study was to identify the best inhibitors of Wnt-4 that not only interact with Wnt-4 protein but also with the covalently bound acyl group to inhibit aberrant Wnt-4 activity. A systematic computational approach was used to analyze inhibition of Wnt-4. Palmitoleic acid was docked into Wnt-4 protein, followed by ligand-based virtual screening of nearly 209,847 compounds; conformer generation of 271 compounds resulted from extensive virtual screening and comparative docking of 10,531 conformers of 271 unique compounds through GOLD (Genetic Optimization for Ligand Docking), AutoDock-Vina, and FRED (Fast Rigid Exhaustive Docking) was subsequently performed. Linux scripts was used to handle the libraries of compounds. The best compounds were selected on the basis of having maximum interactions to protein with bound palmitoleic acid. These represented lead inhibitors in further experiments. Palmitoleic acid is important for efficient Wnt activity, but aberrant Wnt-4 expression can be inhibited by designing inhibitors interacting with both protein and palmitoleic acid.

Prospective performance evaluation of selected common virtual screening tools. Case study: Cyclooxygenase (COX) 1 and 2

Computational methods can be applied in drug development for the identification of novel lead candidates, but also for the prediction of pharmacokinetic properties and potential adverse effects, thereby aiding to prioritize and identify the most promising compounds. In principle, several techniques are available for this purpose, however, which one is the most suitable for a specific research objective still requires further investigation. Within this study, the performance of several programs, representing common virtual screening methods, was compared in a prospective manner. First, we selected top-ranked virtual screening hits from the three methods pharmacophore modeling, shape-based modeling, and docking. For comparison, these hits were then additionally predicted by external pharmacophore- and 2D similarity-based bioactivity profiling tools. Subsequently, the biological activities of the selected hits were assessed in vitro, which allowed for evaluating and comparing the prospective performance of the applied tools. Although all methods performed well, considerable differences were observed concerning hit rates, true positive and true negative hits, and hitlist composition. Our results suggest that a rational selection of the applied method represents a powerful strategy to maximize the success of a research project, tightly linked to its aims. We employed cyclooxygenase as application example, however, the focus of this study lied on highlighting the differences in the virtual screening tool performances and not in the identification of novel COX-inhibitors.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Identifying potential protein targets for toluene using a molecular similarity search, in silico docking and in vitro validation

The toxicity of chemicals greatly depends on their interaction with macromolecular targets.

Molecular fingerprint similarity search in virtual screening

Molecular fingerprints have been used for a long time now in drug discovery and virtual screening. Their ease of use (requiring little to no configuration) and the speed at which substructure and similarity searches can be performed with them - paired with a virtual screening performance similar to other more complex methods - is the reason for their popularity. However, there are many types of fingerprints, each representing a different aspect of the molecule, which can greatly affect search performance. This review focuses on commonly used fingerprint algorithms, their usage in virtual screening, and the software packages and online tools that provide these algorithms.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Biochanin A and prunetin improve epithelial barrier function in intestinal CaCo-2 cells via downregulation of ERK, NF-κB, and tyrosine phosphorylation

The single-layered gut epithelium represents the primary line of defense against environmental stressors; thereby monolayer integrity and tightness are essentially required to maintain gut health and function. To date only a few plant-derived phytochemicals have been described as affecting intestinal barrier function. We investigated the impact of 28 secondary plant compounds on the barrier function of intestinal epithelial CaCo-2/TC-7 cells via transepithelial electrical resistance (TEER) measurements. Apart from genistein, the compounds that had the biggest effect in the TEER measurements were biochanin A and prunetin. These isoflavones improved barrier tightness by 36 and 60%, respectively, compared to the untreated control. Furthermore, both isoflavones significantly attenuated TNFα-dependent barrier disruption, thereby maintaining a high barrier resistance comparable to nonstressed cells. In docking analyses exploring the putative interaction with the tyrosine kinase EGFR, these novel modulators of barrier tightness showed very similar values compared to the known tyrosine kinase inhibitor genistein. Both biochanin A and prunetin were also identified as potent reducers of NF-κB and ERK activation, zonula occludens 1 tyrosine phosphorylation, and metalloproteinase-mediated shedding activity, which may account for the barrier-improving ability of these isoflavones.

Template CoMFA: the 3D-QSAR Grail?

Template CoMFA, a novel alignment methodology for training or test set structures in 3D-QSAR, is introduced. Its two most significant advantages are its complete automation and its ability to derive a single combined model from multiple structural series affecting a biological target. Its only two inputs are one or more "template" structures having 3D coordinates that share some Cartesian space, as may result from X-ray crystallography or pharmacophoric hypothesis, and one or more connectivity-only SAR tables associated with a common target. Template CoMFA also overcomes the major disadvantages of both existing 3D-QSAR alignment methodologies, specifically the tedium and subjectivity of familiar ad hoc approaches, and the awkwardness, occasional physicochemical heresies, and structural scope limitations of the purely topomer approach. The template CoMFA algorithms are described, and two of its application classes are presented. The first class, general models of binding to factor Xa and P38 map kinase, uses crystallographic structures as templates, with the encouraging result that the statistical qualities of each of these two combined models are equivalent to those of their constituent individual series models. The second, 15 data sets originally collected for validation of topomer CoMFA, with arbitrary structures as templates, confirms that the modeling power of template CoMFA resembles that of its predecessors.

Structural properties of non-traditional drug targets present new challenges for virtual screening

Traditional drug targets have historically included signaling proteins that respond to small molecules and enzymes that use small molecules as substrates. Increasing attention is now being directed toward other types of protein targets, in particular those that exert their function by interacting with nucleic acids or other proteins rather than small-molecule ligands. Here, we systematically compare existing examples of inhibitors of protein-protein interactions to inhibitors of traditional drug targets. While both sets of inhibitors bind with similar potency, we find that the inhibitors of protein-protein interactions typically bury a smaller fraction of their surface area upon binding to their protein targets. The fact that an average atom is less buried suggests that more atoms are needed to achieve a given potency, explaining the observation that ligand efficiency is typically poor for inhibitors of protein-protein interactions. We then carried out a series of docking experiments and found a further consequence of these relatively exposed binding modes is that structure-based virtual screening may be more difficult: such binding modes do not provide sufficient clues to pick out active compounds from decoy compounds. Collectively, these results suggest that the challenges associated with such non-traditional drug targets may not lie with identifying compounds that potently bind to the target protein surface, but rather with identifying compounds that bind in a sufficiently buried manner to achieve good ligand efficiency and, thus, good oral bioavailability. While the number of available crystal structures of distinct protein interaction sites bound to small-molecule inhibitors is relatively small at present (only 21 such complexes were included in this study), these are sufficient to draw conclusions based on the current state of the field; as additional data accumulate it will be exciting to refine the viewpoint presented here. Even with this limited perspective however, we anticipate that these insights, together with new methods for exploring protein conformational fluctuations, may prove useful for identifying the "low-hanging fruit" among non-traditional targets for therapeutic intervention.

Ligand-based virtual screening approach using a new scoring function

In this study, we aimed to develop a new ligand-based virtual screening approach using an effective shape-overlapping procedure and a more robust scoring function (denoted by the HWZ score for convenience). The HWZ score-based virtual screening approach was tested against the compounds for 40 protein targets available in the Database of Useful Decoys (DUD; dud.docking.org/jahn/ ), and the virtual screening performance was evaluated in terms of the area under the receiver operator characteristic (ROC) curve (AUC), enrichment factor (EF), and hit rate (HR), demonstrating an improved overall performance compared to other popularly used approaches examined. In particular, the HWZ score-based virtual screening led to an average AUC value of 0.84 ± 0.02 (95% confidence interval) for the 40 targets. The average HR values at the top 1% and 10% of the active compounds for the 40 targets were 46.3% ± 6.7% and 59.2% ± 4.7%, respectively. In addition, the performance of the HWZ score-based virtual screening approach is less sensitive to the choice of the target.

Structure-based discovery of allosteric modulators of two related class B G-protein-coupled receptors

Despite the availability of X-ray crystal structure data for several members of the G-protein-coupled receptor (GPCR) superfamily, structure-based discovery of GPCR ligands has been exclusively restricted to class A (rhodopsin-like) receptors. Herein we report the identification, by a docking-based virtual screening approach, of noncompetitive ligands for two related class B (secretin-like) GPCRs: the glucagon receptor (GLR) and the glucagon-like peptide 1 receptor (GLP-1R). Starting from a knowledge-based three-dimensional model of the GLR, a database of 1.9 million commercially available drug-like compounds was screened for chemical similarity to existing GLR noncompetitive antagonists and docked to the transmembrane cavity of the GLR; 23 compounds were then selected based on protein-ligand interaction fingerprints, and were then purchased and evaluated for in vitro binding to GLR and modulation of glucagon-induced cAMP release. Two of the 23 compounds inhibited the effect of glucagon in a dose-dependent manner, with one inhibitor exhibiting the same potency as L-168 049, a reference noncompetitive GLR antagonist, in a whole-cell-based functional assay. Interestingly, one virtual hit that was inactive at the GLR was shown to bind to GLP-1R and potentiate the response to the endogenous GLP-1 ligand.

Hydractinia echinata test system. II. SAR toxicity study of some anilide derivatives of Naphthol-AS type

In this paper, a toxicity study for a series of anilides of Naphthol-AS type is presented. The toxicity of the model compounds was determined by using the Hydractinia echinata (Hydrozoa) test system. Conformational analysis of Naphthol-AS derivatives was performed to elucidate the possible enzymatic hydrolysis mechanism of these compounds. This mechanism occurs with different rates and always leads to a stoichiometric mixture of reaction products, consisting in the substituted amine and the corresponding α-hydroxy-carboxylic acid. With one exception, the toxicities of the reaction products are subadditive. Quite similar measured toxicity values, log(1/MRC₅₀), led to their average calculated values, and thus to the establishment of class isotoxicity. This method represents a practical alternative useful for the reduction of experimental tests on animals to the lowest possible level, in accordance to the '3Rs' (reduction, refinement and replacement) concept.

Cis-isomerism and other chemical requirements of steroidal agonists and partial agonists acting at TRPM3 channels

BACKGROUND AND PURPOSE

The transient receptor potential melastatin-3 (TRPM3) channel forms calcium-permeable, non-selective, cationic channels that are stimulated by pregnenolone sulphate (PregS). Here, we aimed to define chemical requirements of this acute steroid action and potentially reveal novel stimulators with physiological relevance.

EXPERIMENTAL APPROACH

We used TRPM3 channels over-expressed in HEK 293 cells, with intracellular calcium measurement and whole-cell patch-clamp recording techniques.

KEY RESULTS

The stimulation of TRPM3 channels was confined to PregS and closely related steroids and not mimicked by other major classes of steroids, including progesterone. Relatively potent stimulation of TRPM3-dependent calcium entry was observed. A sulphate group positioned at ring A was important for strong stimulation but more striking was the requirement for a cis (beta) configuration of the side group, revealing previously unrecognized stereo-selectivity and supporting existence of a specific binding site. A cis-oriented side group on ring A was not the only feature necessary for high activity because loss of the double bond in ring B reduced potency and loss of the acetyl group at ring D reduced efficacy and potency. Weak steroid stimulators of TRPM3 channels inhibited effects of PregS, suggesting partial agonism. In silico screening of chemical libraries for non-steroid modulators of TRPM3 channels revealed the importance of the steroid backbone for stimulatory effects.

CONCLUSIONS AND IMPLICATIONS

Our data defined some of the chemical requirements for acute stimulation of TRPM3 channels by steroids, supporting the existence of a specific and unique steroid binding site. Epipregnanolone sulphate was identified as a novel TRPM3 channel stimulator.

Modeling approaches for ligand-based 3D similarity

3D ligand-based similarity approaches are widely used in the early phases of drug discovery for tasks such as hit finding by virtual screening or compound design with quantitative structure–activity relationships. Here in we review widely used software for performing such tasks. Some techniques are based on relatively mature technology, shape-based similarity for instance. Typically, these methods remained in the realm of the expert user, the experienced modeler. However, advances in implementation and speed have improved usability and allow these methods to be applied to databases comprising millions of compounds. There are now many reports of such methods impacting drug-discovery projects. As such, the medicinal chemistry community has become the intended market for some of these new tools, yet they may consider the wide array and choice of approaches somewhat disconcerting. Each method has subtle differences and is better suited to certain tasks than others. In this article we review some of the widely used computational methods via application, provide straightforward background on the underlying theory and provide examples for the interested reader to pursue in more detail. In the new era of preclinical drug discovery there will be ever more pressure to move faster and more efficiently, and computational approaches based on 3D ligand similarity will play an increasing role in in this process.

Small molecule inhibitors of hantavirus infection

Hantaviruses use α(v)β(3) integrins on the surface of human host cells as a gateway to invasion, hence compounds that target this receptor may be used as antiviral agents. To accomplish this aim, new peptidomimetic compounds were selected based on similarity to a cyclic peptide known to bind the α(v)β(3) receptor. This first round of biological screening identified peptidomimetic molecules which were effective hantavirus inhibitors in the low micromolar range, two thousand times more potent than the original cyclic peptide. Pharmacophore models were built to broaden the structural diversity of the second set of compounds screened. Structure-activity relationships (SAR) were drawn from the entire dataset. Further characterization by dose-response studies revealed that three compounds had potency in the nanomolar range. Selectivity assays with a panel of hantaviruses supported the mechanism of inhibition by targeting the α(v)β(3) receptor, through the β(3) integrin.

HPPD: ligand- and target-based virtual screening on a herbicide target

Hydroxyphenylpyruvate dioxygenase (HPPD) has proven to be a very successful target for the development of herbicides with bleaching properties, and today HPPD inhibitors are well established in the agrochemical market. Syngenta has a long history of HPPD-inhibitor research, and HPPD was chosen as a case study for the validation of diverse ligand- and target-based virtual screening approaches to identify compounds with inhibitory properties. Two-dimensional extended connectivity fingerprints, three-dimensional shape-based tools (ROCS, EON, and Phase-shape) and a pharmacophore approach (Phase) were used as ligand-based methods; Glide and Gold were used as target-based. Both the virtual screening utility and the scaffold-hopping ability of the screening tools were assessed. Particular emphasis was put on the specific pitfalls to take into account for the design of a virtual screening campaign in an agrochemical context, as compared to a pharmaceutical environment.