Synthesis of Indomorphan Pseudo-Natural Product Inhibitors of Glucose Transporters GLUT-1 and -3.

Bioactive compound design based on natural product (NP) structure may be limited because of partial coverage of NP-like chemical space and biological target space. These limitations can be overcome by combining NP-centered strategies with fragment-based compound design through combination of NP-derived fragments to afford structurally unprecedented "pseudo-natural products" (pseudo-NPs). The design, synthesis, and biological evaluation of a collection of indomorphan pseudo-NPs that combine biosynthetically unrelated indole- and morphan-alkaloid fragments are described. Indomorphane derivative Glupin was identified as a potent inhibitor of glucose uptake by selectively targeting and upregulating glucose transporters GLUT-1 and GLUT-3. Glupin suppresses glycolysis, reduces the levels of glucose-derived metabolites, and attenuates the growth of various cancer cell lines. Our findings underscore the importance of dual GLUT-1 and GLUT-3 inhibition to efficiently suppress tumor cell growth and the cellular rescue mechanism, which counteracts glucose scarcity.

Design, Synthesis, and Phenotypic Profiling of Pyrano-Furo-Pyridone Pseudo Natural Products.

Natural products (NPs) inspire the design and synthesis of novel biologically relevant chemical matter, for instance through biology-oriented synthesis (BIOS). However, BIOS is limited by the partial coverage of NP-like chemical space by the guiding NPs. The design and synthesis of "pseudo NPs" overcomes these limitations by combining NP-inspired strategies with fragment-based compound design through de novo combination of NP-derived fragments to unprecedented compound classes not accessible through biosynthesis. We describe the development and biological evaluation of pyrano-furo-pyridone (PFP) pseudo NPs, which combine pyridone- and dihydropyran NP fragments in three isomeric arrangements. Cheminformatic analysis indicates that the PFPs reside in an area of NP-like chemical space not covered by existing NPs but rather by drugs and related compounds. Phenotypic profiling in a target-agnostic "cell painting" assay revealed that PFPs induce formation of reactive oxygen species and are structurally novel inhibitors of mitochondrial complex I.

Chromopynones are pseudo natural product glucose uptake inhibitors targeting glucose transporters GLUT-1 and -3.

The principles guiding the design and synthesis of bioactive compounds based on natural product (NP) structure, such as biology-oriented synthesis (BIOS), are limited by their partial coverage of the NP-like chemical space of existing NPs and retainment of bioactivity in the corresponding compound collections. Here we propose and validate a concept to overcome these limitations by de novo combination of NP-derived fragments to structurally unprecedented 'pseudo natural products'. Pseudo NPs inherit characteristic elements of NP structure yet enable the efficient exploration of areas of chemical space not covered by NP-derived chemotypes, and may possess novel bioactivities. We provide a proof of principle by designing, synthesizing and investigating the biological properties of chromopynone pseudo NPs that combine biosynthetically unrelated chromane- and tetrahydropyrimidinone NP fragments. We show that chromopynones define a glucose uptake inhibitor chemotype that selectively targets glucose transporters GLUT-1 and -3, inhibits cancer cell growth and promises to inspire new drug discovery programmes aimed at tumour metabolism.

De novo branching cascades for structural and functional diversity in small molecules.

The limited structural diversity that a compound library represents severely restrains the discovery of bioactive small molecules for medicinal chemistry and chemical biology research, and thus calls for developing new divergent synthetic approaches to structurally diverse and complex scaffolds. Here we present a de novo branching cascades approach wherein simple primary substrates follow different cascade reactions to create various distinct molecular frameworks in a scaffold diversity phase. Later, the scaffold elaboration phase introduces further complexity to the scaffolds by creating a number of chiral centres and incorporating new hetero- or carbocyclic rings. Thus, employing N-phenyl hydroxylamine, dimethyl acetylenedicarboxylate and allene ester as primary substrates, a compound collection of sixty one molecules representing seventeen different scaffolds is built up that delivers a potent tubulin inhibitor, as well as inhibitors of the Hedgehog signalling pathway. This work highlights the immense potential of cascade reactions to deliver compound libraries enriched in structural and functional diversity.

Structure-activity relationships of 2-arylamido-5,7-dihydro-4H-thieno[2,3-c]pyran-3-carboxamide derivatives as cannabinoid receptor agonists and their analgesic action.

SAR studies were performed on a series of 2-arylamido-5,7-dihydro-4H-thieno[2,3-c]pyran-3-carboxamide derivatives as cannabinoid receptor agonists. Starting from a HTS hit both potency and selectivity could be improved. Modifications to the thiophene fusion and C-3 amides were studied. A representative compound 3t produced analgesia when dosed orally in inflammatory pain models of writhing and carrageenan-induced allodynia.

Maximum-score diversity selection for early drug discovery.

Diversity selection is a common task in early drug discovery. One drawback of current approaches is that usually only the structural diversity is taken into account, therefore, activity information is ignored. In this article, we present a modified version of diversity selection, which we term Maximum-Score Diversity Selection, that additionally takes the estimated or predicted activities of the molecules into account. We show that finding an optimal solution to this problem is computationally very expensive (it is NP-hard), and therefore, heuristic approaches are needed. After a discussion of existing approaches, we present our new method, which is computationally far more efficient but at the same time produces comparable results. We conclude by validating these theoretical differences on several data sets.

Large-scale learning of structure-activity relationships using a linear support vector machine and problem-specific metrics.

The goal of this study was to adapt a recently proposed linear large-scale support vector machine to large-scale binary cheminformatics classification problems and to assess its performance on various benchmarks using virtual screening performance measures. We extended the large-scale linear support vector machine library LIBLINEAR with state-of-the-art virtual high-throughput screening metrics to train classifiers on whole large and unbalanced data sets. The formulation of this linear support machine has an excellent performance if applied to high-dimensional sparse feature vectors. An additional advantage is the average linear complexity in the number of non-zero features of a prediction. Nevertheless, the approach assumes that a problem is linearly separable. Therefore, we conducted an extensive benchmarking to evaluate the performance on large-scale problems up to a size of 175000 samples. To examine the virtual screening performance, we determined the chemotype clusters using Feature Trees and integrated this information to compute weighted AUC-based performance measures and a leave-cluster-out cross-validation. We also considered the BEDROC score, a metric that was suggested to tackle the early enrichment problem. The performance on each problem was evaluated by a nested cross-validation and a nested leave-cluster-out cross-validation. We compared LIBLINEAR against a Naïve Bayes classifier, a random decision forest classifier, and a maximum similarity ranking approach. These reference approaches were outperformed in a direct comparison by LIBLINEAR. A comparison to literature results showed that the LIBLINEAR performance is competitive but without achieving results as good as the top-ranked nonlinear machines on these benchmarks. However, considering the overall convincing performance and computation time of the large-scale support vector machine, the approach provides an excellent alternative to established large-scale classification approaches.

Large-scale evaluation of log P predictors: local corrections may compensate insufficient accuracy and need of experimentally testing every other compound.

A large variety of log P calculation methods failed to produce sufficient accuracy in log P prediction for two in-house datasets of more than 96000 compounds contrary to their significantly better performances on public datasets. The minimum Root Mean Squared Error (RMSE) of 1.02 and 0.65 were calculated for the Pfizer and Nycomed datasets, respectively, in the 'out-of-box' implementation. Importantly, the use of local corrections (LC) implemented in the ALOGPS program based on experimental in-house log P data significantly reduced the RMSE to 0.59 and 0.48 for the Pfizer and Nycomed datasets, respectively, instantly without retraining the model. Moreover, more than 60% of molecules predicted with the highest confidence in each set had a mean absolute error (MAE) less than 0.33 log units that is only ca. 10% higher than the estimated variation in experimental log P measurements for the Pfizer dataset. Therefore, following this retrospective analysis, we suggest that the use of the predicted log P values with high confidence may eliminate the need of experimentally testing every other compound. This strategy could reduce the cost of measurements for pharmaceutical companies by a factor of 2, increase the confidence in prediction at the analog design stage of drug discovery programs, and could be extended to other ADMET properties.

Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds.

We first review the state-of-the-art in development of log P prediction approaches falling in two major categories: substructure-based and property-based methods. Then, we compare the predictive power of representative methods for one public (N = 266) and two in house datasets from Nycomed (N = 882) and Pfizer (N = 95809). A total of 30 and 18 methods were tested for public and industrial datasets, respectively. Accuracy of models declined with the number of nonhydrogen atoms. The Arithmetic Average Model (AAM), which predicts the same value (the arithmetic mean) for all compounds, was used as a baseline model for comparison. Methods with Root Mean Squared Error (RMSE) greater than RMSE produced by the AAM were considered as unacceptable. The majority of analyzed methods produced reasonable results for the public dataset but only seven methods were successful on the both in house datasets. We proposed a simple equation based on the number of carbon atoms, NC, and the number of hetero atoms, NHET: log P = 1.46(+/-0.02) + 0.11(+/-0.001) NC-0.11(+/-0.001) NHET. This equation outperformed a large number of programs benchmarked in this study. Factors influencing the accuracy of log P predictions were elucidated and discussed.

The Compressed Feature Matrix--a novel descriptor for adaptive similarity search.

The Compressed Feature Matrix (CFM) is a new molecular descriptor for adaptive similarity searching. Depending on the requirements, it is based on a distance or geometry matrix. Thus, the CFM permits topological and three-dimensional comparisons of molecules. In contrast to the common distance matrix, the CFM is based on features instead of atoms. Each kind of these features may be weighted separately, depending on its (estimated) contribution to the biological effect of the molecule. In this work, we show that the CFM allows us to adapt similarity evaluations to particular ligands as well as to classification requirements. The CFM method is analyzed regarding correctness, adaptivity and speed. Applying the basic setting of feature weights, the similarity evaluations using the CFM on the one hand and the Tanimoto coefficient together with MACCS Keys on the other yield similar results. However, in contrast to the latter method, the CFM even permits us to focus on small parts of molecules to serve as a basis for similarity. Accordingly, we have achieved striking results not only by readjusting the feature weights with regard to the scaffold but also to the side chain of the respective target. The results of the latter run turned out to be rather independent of the molecular scaffold. Hence, the CFM is suitable not only for common similarity evaluation, but also for techniques such as lead or scaffold hopping. Figure Chemical structure, feature graph and topological CFM of serotonine