Several target-site mutations in the genes of subunits SDH-B, SDH-C and SDH-D with different impact on SDHI fungicides were detected. The pattern of mutations varied from year to year and between different regions. Strict resistance management strategies are recommended to maintain SDHIs as effective tools for net blotch control, especially in areas with low frequencies of resistant isolates. © 2016 Society of Chemical Industry.

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Structure-based optimization to improve the affinity of a lead compound is an established approach in drug discovery. Knowledge-based databases holding molecular replacements can be supportive in the optimization process. We introduce a strategy to relate the substitution effect within matched molecular pairs (MMPs) to the atom environment within the cocrystallized protein-ligand complex. Virtually Aligned Matched Molecular Pairs Including Receptor Environment (VAMMPIRE) database and the supplementary web interface ( http://vammpire.pharmchem.uni-frankfurt.de ) provide valuable information for structure-based lead optimization.

Dual-target inhibitors gained increased attention in the past years. A novel in silico approach was employed for the discovery of dual 5-lipoxygenase/soluble epoxide hydrolase inhibitors. The ligand-based approach uses excessive pharmacophore elucidation and pharmacophore alignment in conjunction with shape-based scoring. The virtual screening results were verified in vitro, leading to nine novel inhibitors including a dual-target compound.

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Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.

Most drugs act on a multitude of targets rather than on one single target. Polypharmacology, an upcoming branch of pharmaceutical science, deals with the recognition of these off-target activities of small chemical compounds. Due to the high amount of data to be processed, application of computational methods is indispensable in this area. This review summarizes the most important in silico approaches for polypharmacology. The described methods comprise network pharmacology, machine learning techniques and chemogenomic approaches. The use of these methods for drug repurposing as a branch of drug discovery and development is discussed. Furthermore, a broad range of prospective applications is summarized to give the reader an overview of possibilities and limitations of the described techniques.

Design of multitarget drugs and polypharmacological compounds has become popular during the past decade. However, the main approach to design such compounds is to link two selective ligands via a flexible linker. Although such chimeric ligands often have reasonable potency in vitro, the in vivo efficacy is low due to high molecular weight, low ligand efficiency, and poor pharmacokinetic profile. We developed an unprecedented in silico approach for fragment-based design of multitarget ligands. It relies on superposition of the chemical spaces related to the affinity on single targets represented by selforganizing maps. We used this approach for screening of molecular fragments, which bind to the enzymes 5-lipoxygenase (5-LO) and soluble epoxide hydrolase (sEH). Using STD-NMR and activity-based assays, we were able to identify fragments binding to both targets. Furthermore, we were able to expand one of the fragments to a potent dual inhibitor bearing a reasonable molecular weight (MW = 446) and high affinity to both targets (IC 50 of 0.03 μM toward 5-LO and 0.17 μM toward sEH).