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In silico driven optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety is a key requirement in modern drug discovery. Nowadays, large and harmonized datasets allow to implement deep neural networks (DNNs) as a framework for leveraging predictive models. Nevertheless, various available model architectures differ in their global applicability and performance in lead optimization projects, such as stability over time and interpretability of the results. Here, we describe and compare the value of established DNN-based methods for the prediction of key ADME property trends and biological activity in an industrial drug discovery environment, represented by microsomal lability, CYP3A4 inhibition and factor Xa inhibition. Three architectures are exemplified, our earlier described multilayer perceptron approach (MLP), graph convolutional network-based models (GCN) and a vector representation approach, Mol2Vec. From a statistical perspective, MLP and GCN were found to perform superior over Mol2Vec, when applied to external validation sets. Interestingly, GCN-based predictions are most stable over a longer period in a time series validation study. Apart from those statistical observations, DNN prove of value to guide local SAR. To illustrate this important aspect in pharmaceutical research projects, we discuss challenging applications in medicinal chemistry towards a more realistic picture of artificial intelligence in drug discovery.

Proteolysis-targeting chimeras (PROTACs) are an emerging drug modality that may offer new opportunities to circumvent some of the limitations associated with traditional small-molecule therapeutics. By analogy with the concept of the 'druggable genome', the question arises as to which potential drug targets might PROTAC-mediated protein degradation be most applicable. Here, we present a systematic approach to the assessment of the PROTAC tractability (PROTACtability) of protein targets using a series of criteria based on data and information from a diverse range of relevant publicly available resources. Our approach could support decision-making on whether or not a particular target may be amenable to modulation using a PROTAC. Using our approach, we identified 1,067 proteins of the human proteome that have not yet been described in the literature as PROTAC targets that offer potential opportunities for future PROTAC-based efforts.

Classical high-throughput screening (HTS) technologies for the analysis of ionic currents across biological membranes can be performed using fluorescence-based, radioactive, and mass spectrometry (MS)-based uptake assays. These assays provide rapid results for pharmacological HTS, but the underlying, indirect analytical character of these assays can be linked to high false-positive hit rates. Thus, orthogonal and secondary assays using more biological target-based technologies are indispensable for further compound validation and optimization. Direct assay technologies for transporter proteins are electrophysiology-based, but are also complex, time-consuming, and not well applicable for automated profiling purposes. In contrast to conventional patch clamp systems, solid supported membrane (SSM)-based electrophysiology is a sensitive, membrane-based method for transporter analysis, and current technical developments target the demand for automated, accelerated, and sensitive assays for transporter-directed compound screening. In this study, the suitability of the SSM-based technique for pharmacological compound identification and optimization was evaluated performing cell-free SSM-based measurements with the electrogenic amino acid transporter B0AT1 (SLC6A19). Electrophysiological characterization of leucine-induced currents demonstrated that the observed signals were specific to B0AT1. Moreover, B0AT1-dependent responses were successfully inhibited using an established in-house tool compound. Evaluation of current stability and data reproducibility verified the robustness and reliability of the applied assay. Active compounds from primary screens of large compound libraries were validated, and false-positive hits were identified. These results clearly demonstrate the suitability of the SSM-based technique as a direct electrophysiological method for rapid and automated identification of small molecules that can inhibit B0AT1 activity.

Artificial intelligence offers promising solutions for property prediction, compound design, and retrosynthetic planning, which are expected to significantly accelerate the search for pharmacologically relevant molecules. Here, we investigate aspects of artificial intelligence based de novo design pertaining to its integration into real-life workflows. First, different chemical spaces were used as training sets for reinforcement learning (RL) in combination with different reward functions. With the trained neuronal networks different biologically active molecules could be regenerated. Excluding molecules with substructures such as five-membered rings from training spaces nevertheless produced results containing these moieties. Furthermore, different scoring functions in RL were investigated and produced different design ensembles. In summary, some of these design proposals are close in chemical space to the query, thus supporting lead optimization, while 3D-shape or QSAR (quantitative structure-activity relationship) models produced significantly different proposals by sampling a broader region of the chemical space, thus supporting lead generation. Therefore, RL provides a good framework to tailored design approaches for different discovery phases.

Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design.

Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design.

Computational approaches in drug discovery and development hold great promise, with artificial intelligence methods undergoing widespread contemporary use, but the experimental validation of these new approaches is frequently inadequate. We are initiating Critical Assessment of Computational Hit-finding Experiments (CACHE) as a public benchmarking project that aims to accelerate the development of small molecule hit-finding algorithms by competitive assessment. Compounds will be identified by participants using a wide range of computational methods for dozens of protein targets selected for different types of prediction scenarios, as well as for their potential biological or pharmaceutical relevance. Community-generated predictions will be tested centrally and rigorously in an experimental hub(s), and all data, including the chemical structures of experimentally tested compounds, will be made publicly available without restrictions. The ability of a range of computational approaches to find novel compounds will be evaluated, compared, and published. The overarching goal of CACHE is to accelerate the development of computational chemistry methods by providing rapid and unbiased feedback to those developing methods, with an ancillary and valuable benefit of identifying new compound-protein binding pairs for biologically interesting targets. The initiative builds on the power of crowd sourcing and expands the open science paradigm for drug discovery.

Computational approaches in drug discovery and development hold great promise, with artificial intelligence methods undergoing widespread contemporary use, but the experimental validation of these new approaches is frequently inadequate. We are initiating Critical Assessment of Computational Hit-finding Experiments (CACHE) as a public benchmarking project that aims to accelerate the development of small molecule hit-finding algorithms by competitive assessment. Compounds will be identified by participants using a wide range of computational methods for dozens of protein targets selected for different types of prediction scenarios, as well as for their potential biological or pharmaceutical relevance. Community-generated predictions will be tested centrally and rigorously in an experimental hub(s), and all data, including the chemical structures of experimentally tested compounds, will be made publicly available without restrictions. The ability of a range of computational approaches to find novel compounds will be evaluated, compared, and published. The overarching goal of CACHE is to accelerate the development of computational chemistry methods by providing rapid and unbiased feedback to those developing methods, with an ancillary and valuable benefit of identifying new compound-protein binding pairs for biologically interesting targets. The initiative builds on the power of crowd sourcing and expands the open science paradigm for drug discovery., ACKNOWLEDGEMENTS The Structural Genomics Consortium is a registered charity (no: 1097737) that receives funds from Bayer AG, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute [OGI-196], Janssen, Merck KGaA (aka EMD in Canada and US), Pfizer, Takeda and the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 875510. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and Ontario Institute for Cancer Research, Royal Institution for the Advancement of Learning McGill University, Kungliga Tekniska Hoegskolan, Diamond Light Source Limited. This communication reflects the views of the authors and the JU is not liable for any use that may be made of the information contained herein. M. K. Gilson acknowledges funding from National Institute of General Medical Sciences (GM061300). J. J. Irwin acknowledges funding from National Institute of General Medical Sciences (GM133836). These findings are solely of the authors and do not necessarily represent the views of the NIH. The Lee laboratory at the University of Cambridge receives funding from multiple sources, including Pfizer, AstraZeneca, the Engineering and Physical Sciences Research Council and the Winton Programme for the Physics of Sustainability. T.I. Oprea and C. G. Bologa acknowledge funding from the National Institutes of Health Common Fund program, Illuminating the Druggable Genome (CA224370 and TR002278). COMPETING INTERESTS M. K. Gilson has an equity interest in and is a cofounder and scientific advisor of VeraChem LLC. J. J. Irwin is a co-founder of Blue Dolphin LLC, which undertakes fee-for-service ligand discovery. A.A. Lee is the chief scientific officer and a shareholder of PostEra Inc. T. I. Oprea has received honoraria from or consulted for Abbott, AstraZeneca, Chiron, Genentech, Infinity Pharmaceuticals, Merz Pharmaceuticals, Merck Darmstadt, Mitsubishi Tanabe, Novartis, Ono Pharmaceuticals, Pfizer, Roche, Sanofi and Wyeth, and is on the Scientific Advisory Board of ChemDiv and InSilico Medicine.

Classical high-throughput screening (HTS) technologies for the analysis of ionic currents across biological membranes can be performed using fluorescence-based, radioactive, and mass spectrometry (MS)-based uptake assays. These assays provide rapid results for pharmacological HTS, but the underlying, indirect analytical character of these assays can be linked to high false-positive hit rates. Thus, orthogonal and secondary assays using more biological target-based technologies are indispensable for further compound validation and optimization. Direct assay technologies for transporter proteins are electrophysiology-based, but are also complex, time-consuming, and not well applicable for automated profiling purposes. In contrast to conventional patch clamp systems, solid supported membrane (SSM)-based electrophysiology is a sensitive, membrane-based method for transporter analysis, and current technical developments target the demand for automated, accelerated, and sensitive assays for transporter-directed compound screening. In this study, the suitability of the SSM-based technique for pharmacological compound identification and optimization was evaluated performing cell-free SSM-based measurements with the electrogenic amino acid transporter B

Joint academic–industrial projects supporting drug discovery are frequently pursued to deploy and benchmark cutting-edge methodical developments from academia in a real-world industrial environment at different scales. The dimensionality of tasks ranges from small molecule physicochemical property assessment over protein–ligand interaction up to statistical analyses of biological data. This way, method development and usability both benefit from insights gained at both ends, when predictiveness and readiness of novel approaches are confirmed, but the pharmaceutical drug makers get early access to novel tools for the quality of drug products and benefit of patients. Quantum–mechanical and simulation methods particularly fall into this group of methods, as they require skills and expense in their development but also significant resources in their application, thus are comparatively slowly dripping into the realm of industrial use. Nevertheless, these physics-based methods are becoming more and more useful. Starting with a general overview of these and in particular quantum–mechanical methods for drug discovery we review a decade-long and ongoing collaboration between Sanofi and the Kast group focused on the application of the embedded cluster reference interaction site model (EC-RISM), a solvation model for quantum chemistry, to study small molecule chemistry in the context of joint participation in several SAMPL (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenges. Starting with early application to tautomer equilibria in water (SAMPL2) the methodology was further developed to allow for challenge contributions related to predictions of distribution coefficients (SAMPL5) and acidity constants (SAMPL6) over the years. Particular emphasis is put on a frequently overlooked aspect of measuring the quality of models, namely the retrospective analysis of earlier datasets and predictions in light of more recent and advanced developments. We therefore demonstrate the performance of the current methodical state of the art as developed and optimized for the SAMPL6 pKa and octanol–water log P challenges when re-applied to the earlier SAMPL5 cyclohexane-water log D and SAMPL2 tautomer equilibria datasets. Systematic improvement is not consistently found throughout despite the similarity of the problem class, i.e. protonation reactions and phase distribution. Hence, it is possible to learn about hidden bias in model assessment, as results derived from more elaborate methods do not necessarily improve quantitative agreement. This indicates the role of chance or coincidence for model development on the one hand which allows for the identification of systematic error and opportunities toward improvement and reveals possible sources of experimental uncertainty on the other. These insights are particularly useful for further academia–industry collaborations, as both partners are then enabled to optimize both the computational and experimental settings for data generation., Journal of computer-aided molecular design;35

Macrocycles and cyclic peptides are increasingly attractive therapeutic modalities as they often have improved affinity, are able to bind to extended protein interfaces and otherwise have favorable properties. Macrocyclization of a known binder molecule has the potential to stabilize its bioactive conformation, improve its metabolic stability, cell permeability and in certain cases oral bioavailability. Herein, we present an in silico approach that automatically generates, evaluates and proposes cyclizations utilizing a library of well-established chemical reactions and reagents. Using the three-dimensional (3D) conformation of the linear molecule in complex with a target protein as starting point, this approach identifies attachment points, generates linkers, evaluates the conformational landscape of suitable linkers and their geometric compatibility and ranks the resulting molecules with respect to their predicted conformational stability and interactions with the target protein. As we show here with several prospective and retrospective case studies, this procedure can be applied for the macrocyclization of small molecules and peptides and even PROTACs and proteins.The presented approach is an important step towards the enhanced utilization of macrocycles andcyclic peptides as attractive therapeutic modalities.

Mechanisms of protein-carbohydrate recognition attract a lot of interest due to their roles in various cellular processes and metabolism disorders. We have performed a large-scale analysis of protein structures solved in complex with glucose, galactose and their substituted analogues. We found that, on average, sugar molecules establish five hydrogen bonds (HBs) in the binding site, including one to three HBs with bridging water molecules. The free energy contribution of bridging and direct HBs was estimated using the free energy perturbation (FEP+) methodology for mono- and disaccharides that bind to l-ABP, ttGBP, TrmB, hGalectin-1 and hGalectin-3. We show that removing hydroxy groups that are engaged in direct HBs with the charged groups of Asp, Arg and Glu residues, protein backbone amide or buried water dramatically decreases binding affinity. In contrast, all solvent-exposed hydroxy groups and hydroxy groups engaged in HBs with the solvent-exposed bridging water molecules contribute weakly to binding affinity and so can be replaced to optimize ligand potency. Finally, we rationalize an effect of binding site water replacement on the binding affinity to l-ABP.

Physicochemical properties of peptides need to be compatible with the manufacturing process and formulation requirements to ensure developability toward the commercial drug product. This aspect is often disregarded and only evaluated late in discovery, imposing a high risk for delays in development, increased costs, and finally for the project in general. Here, we report a case study of early physicochemical peptide characterization and optimization of dual glucagon-like peptide 1/glucagon receptor agonists toward specific formulation requirements. Aggregation issues which were observed at acidic pH in the presence of phenolic preservatives could be eliminated by modification of the peptide sequence, and chemical stability issues were significantly improved by addition of stabilizing formulation excipients. We describe structural, analytical, and biophysical characterization in different compositions to analyze the effect of pH and formulation excipients on physical and chemical stability. Molecular models have been generated to rationalize peptide stability behavior based on computed physicochemical descriptors and interactions with excipients. To conclude these studies, a general roadmap is proposed how to assess and optimize early physicochemical peptide properties in a sophisticated way by combining experimental and in silico profiling to provide stable peptide drugs under relevant formulation conditions at the end of discovery.

Background and purpose The NaV 1.7 channel is highly expressed in dorsal root ganglia of the sensory nervous system and plays a central role in the pain signalling process. We investigated a library prepared from original venoms of 117 different animals to identify new selective inhibitors of this target. Experimental approach We used high throughput screening of a large venom collection using automated patch-clamp experiments on human voltage-gated sodium channel subtypes and then in vitro and in vivo electrophysiological experiments to characterize the active peptides that have been purified, sequenced, and chemically synthesized. Analgesic effects were evaluated in vivo in mice models. Key results We identified cyriotoxin-1a (CyrTx-1a), a novel peptide isolated from Cyriopagopus schioedtei spider venom, as a candidate for further characterization. This 33 amino acids toxin belongs to the inhibitor cystine knot structural family and inhibits hNaV 1.1-1.3 and 1.6-1.7 channels in the low nanomolar range, compared to the micromolar range for hNaV 1.4-1.5 and 1.8 channels. CyrTx-1a was 920 times more efficient at inhibiting tetrodotoxin (TTX)-sensitive than TTX-resistant sodium currents recorded from adult mouse dorsal root ganglia neurons and in vivo electrophysiological experiments showed that CyrTx-1a was approximately 170 times less efficient than huwentoxin-IV at altering mouse skeletal neuromuscular excitability properties. CyrTx-1a exhibited an analgesic effect in mice by increasing reaction time in the hot-plate assay. Conclusions and implications The pharmacological profile of CyrTx-1a paves the way for further molecular engineering aimed to optimize the potential antinociceptive properties of this peptide.

Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein–ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.

A novel procedure (CROSS: Computational Rescaffolding and Optimization using Synthetic Schemes) for in silico rescaffolding and side-chain optimization is reported with explicit consideration of the route of synthesis and availability of compatible chemical reagents. We have defined a set of retrosynthetic disconnections representing robust reactions, amenable for combinatorial chemistry. This rule set is used to generate virtual fragment databases from available reagents. Each reactive center is annotated with its compatibility with regard to the chemical reactions. The rule set is then applied to a new molecule to obtain separate query subunits for rescaffolding by 3D similarity searching in specific reagent-derived fragment databases. Thus, only fragments compatible with the chemistry and shape of the corresponding query moiety are investigated further. The identified fragment hits directly indicate (1) available chemical reagents that can replace the query moiety in the starting molecule and (2) the route for the synthesis of the proposed molecules.

Author(s): Fechner, Uli; de Graaf, Chris; Torda, Andrew E; Gussregen, Stefan; Evers, Andreas; Matter, Hans; Hessler, Gerhard; Richmond, Nicola J; Schmidtke, Peter; Segler, Marwin HS; Waller, Mark P; Pleik, Stefanie; Shea, Joan-Emma; Levine, Zachary; Mullen, Ryan; van den Broek, Karina; Epple, Matthias; Kuhn, Hubert; Truszkowski, Andreas; Zielesny, Achim; Fraaije, Johannes Hans; Gracia, Ruben Serral; Kast, Stefan M; Bulusu, Krishna C; Bender, Andreas; Yosipof, Abraham; Nahum, Oren; Senderowitz, Hanoch; Krotzky, Timo; Schulz, Robert; Wolber, Gerhard; Bietz, Stefan; Rarey, Matthias; Zimmermann, Markus O; Lange, Andreas; Ruff, Manuel; Heidrich, Johannes; Onlia, Ionut; Exner, Thomas E; Boeckler, Frank M; Bermudez, Marcel; Firaha, Dzmitry S; Holloczki, Oldamur; Kirchner, Barbara; Tautermann, Christofer S; Volkamer, Andrea; Eid, Sameh; Turk, Samo; Rippmann, Friedrich; Fulle, Simone; Saleh, Noureldin; Saladino, Giorgio; Gervasio, Francesco L; Haensele, Elke; Banting, Lee; Whitley, David C; Oliveira Santos, Jana Sopkova-de; Bureau, Ronan; Clark, Timothy; Sandmann, Achim; Lanig, Harald; Kibies, Patrick; Heil, Jochen; Hoffgaard, Franziska; Frach, Roland; Engel, Julian; Smith, Steven; Basu, Debjit; Rauh, Daniel; Kohlbacher, Oliver; Boeckler, Frank M; Essex, Jonathan W; Bodnarchuk, Michael S; Ross, Gregory A; Finkelmann, Arndt R; Goller, Andreas H; Schneider, Gisbert; Husch, Tamara; Schutter, Christoph; Balducci, Andrea; Korth, Martin; Ntie-Kang, Fidele; Gunther, Stefan; Sippl, Wolfgang; Mbaze, Luc Meva'a

The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r(2) of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.

The optimization of a lead structure to a development candidate often requires removal of undesirable antitarget activities. To this end, we have developed an approach to extract antitarget activity hotspots from larger databases and to transfer this knowledge onto novel chemical series. These antitarget activity hotspots will be captured as pairs of informative molecules, which are chemically closely related, but differ significantly in biological activity. We illustrate the application of antitarget activity hotspots as informative compound pairs for the optimization of side effects in lead structures for relevant antitargets in pharmaceutical research. The use for prospective design requires establishing a structural link between known antitarget hotspot pairs and a new lead structure: we employ 3D-based similarity comparison for this task. The entire workflow serves as idea generator in early optimization. The feasibility of this approach is demonstrated in several optimization problems related to hERG inhibition, and CYP3A4 inhibition. Several structural examples demonstrate the ability of the 3D-shape searching to identify related scaffolds and the usefulness of the antitarget hotspot information to guide optimization by modulating the undesirable antitarget activity. Such a concept based on the analysis of local similarities and the transfer to 3D-related series is especially promising in those cases, where the construction of antitarget QSAR models fails to detect local SAR trends for guiding the next optimization cycle.

The goal of scaffold hopping is to replace the chemical core structure by a novel chemical motif while keeping the biological activity of the molecule. As pharmacophores define chemical features essential for biological activity, they can be successfully employed to guide scaffold replacements. To this end, various novel approaches have recently been developed and applied.

We thank Sandra Schimanski-Breves and Alexander Liesum for crystallization, Dirk Bossemeyer and Michael Gassel for protein production of PKA.; International audience; Structure-based drug design (SBDD) is a powerful and widely used approach to optimize affinity of drug candidates. With the recently introduced INPHARMA method, the binding mode of small molecules to their protein target can be characterized even if no spectroscopic information about the protein is known. Here, we show that the combination of the spin-diffusion-based NMR methods INPHARMA, trNOE, and STD results in an accurate scoring function for docking modes and therefore determination of protein-ligand complex structures. Applications are shown on the model system protein kinaseA and the drug targets glycogen phosphorylase and soluble epoxide hydrolase (sEH). Multiplexing of several ligands improves the reliability of the scoring function further. The new score allows in the case of sEH detecting two binding modes of the ligand in its binding site, which was corroborated by X-ray analysis.

Terephthalic acid based derivatives containing beta- and gamma-amino acid residues were prepared as antagonists of the leukocyte cell adhesion process that is mediated through the interaction of the very late antigen 4 (VLA-4) and the vascular cell adhesion molecule 1 (VCAM-1). The compounds 2, 10-12, 14, and 16-17 inhibited the adhesion in a cell based assay in the low and sub micromolar range.

G-protein-coupled receptors (GPCRs) form a large protein family that plays an important role in many physiological and pathophysiological processes. Since the sequencing of the human genome has revealed several hundred new members of this receptor family, many new opportunities for developing novel therapeutics have emerged. The increasing knowledge of GPCRs (biological target space) and their ligands (chemical ligand space) enables novel drug design strategies to accelerate the finding and optimization of GPCR leads: The crystal structure of rhodopsin provides the first three-dimensional GPCR information, which now supports homology modeling studies and structure-based drug design approaches within the GPCR target family. On the other hand, the classical ligand-based design approaches (for example, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR)) are still powerful methods for lead finding and optimization. In addition, the cross-target analysis of GPCR ligands has revealed more and more common structural motifs and three-dimensional pharmacophores. Such GPCR privileged structural motifs have been successfully used by many pharmaceutical companies to design and synthesize combinatorial libraries, which are subsequently tested against novel GPCR targets for lead finding. In the near future structural biology and chemogenomics might allow the mapping of the ligand binding to the receptor. The linking of chemical and biological spaces will aid in generating lead-finding libraries, which are tailor-made for their respective receptor.

Similarity searching allows a fast identification of analogues to biologically active molecules. Depending on the applied similarity metrics, either structurally close analogues or more diverse compounds can be identified. This is of particular interest for the analysis of high-throughput screening (HTS) hits. A combination of similarity searching and data mining applied to HTS data derives early structure-activity relationships to guide a subsequent optimization of hits.

The biosynthesis of the diterpene 8alpha-acetoxy-13alpha-hydroxy-5-oxo-13-epi- neoverrucosane in the arctic liverwort Fossombronia alaskana was studied by incorporation experiments using [1-(13)C]- and [U-(13)C(6)]glucose as precursors. The (13)C-labeling patterns of acetyl-CoA, pyruvate, and phosphoenolpyruvate in intermediary metabolism were reconstructed from the (13)C NMR data of biosynthetic amino acids (leucine, alanine, phenylalanine) and were used to predict hypothetical labeling patterns for isopentenyl pyrophosphate formed via the mevalonate pathway and the deoxyxylulose pathway. The labeling patterns observed for the neoverrucosane diterpene were consistent with the intermediate formation of geranyllinaloyl pyrophosphate assembled from dimethylallyl pyrophosphate and three molecules of isopentenyl pyrophosphate generated predominantly or entirely via 1-deoxyxylulose 5-phosphate. The experimental data can be integrated into a detailed biosynthetic scheme involving a 1,5-hydride shift. The postulated involvement of the 1,5-hydride shift was confirmed by an incorporation experiment with [6,6-(2)H(2)]glucose.

Quantifying the properties of macromolecules is a prerequisite for understanding their roles in biochemical processes. One of the less-explored geometric features of macromolecules is molecular surface irregularity, or ‘roughness’, which can be measured in terms of fractal dimension (D). In this study, we demonstrate that surface roughness correlates with ligand binding potential. We quantified the surface roughnesses of biological macromolecules in a large-scale survey that revealed D values between 2.0 and 2.4. The results of our study imply that surface patches involved in molecular interactions, such as ligand-binding pockets and protein-protein interfaces, exhibit greater local fluctuations in their fractal dimensions than ‘inert’ surface areas. We expect approximately 22 % of a protein’s surface outside of the crystallographically known ligand binding sites to be ligandable. These findings provide a fresh perspective on macromolecular structure and have considerable implications for drug design as well as chemical and systems biology., Molecular Informatics, 33 (9), ISSN:1868-1743, ISSN:1868-1751