A screen of pharmacologically active compounds to identify modulators of the Adgrg6/Gpr126 signalling pathway in zebrafish embryos.

Adhesion G protein-coupled receptors (GPCRs) are an underrepresented class of GPCRs in drug discovery. We previously developed an in vivo drug screening pipeline to identify compounds with agonist activity for Adgrg6 (Gpr126), an adhesion GPCR required for myelination of the peripheral nervous system in vertebrates. The screening assay tests for rescue of an ear defect found in adgrg6 hypomorphic homozygous mutant zebrafish, using the expression of versican b (vcanb) mRNA as an easily identifiable phenotype.

Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction.

Recently, imputation techniques have been adapted to predict activity values among sparse bioactivity matrices, showing improvements in predictive performance over traditional QSAR models. These models are able to use experimental activity values for auxiliary assays when predicting the activity of a test compound on a specific assay. In this study, we tested three different multi-task imputation techniques on three classification-based toxicity datasets: two of small scale (12 assays each) and one large scale with 417 assays.

Amyloid binding and beyond: a new approach for Alzheimer's disease drug discovery targeting Aβo-PrP binding and downstream pathways.

Amyloid β oligomers (Aβo) are the main toxic species in Alzheimer's disease, which have been targeted for single drug treatment with very little success. In this work we report a new approach for identifying functional Aβo binding compounds. A tailored library of 971 fluorine containing compounds was selected by a computational method, developed to generate molecular diversity.

Identification of a series of hair-cell MET channel blockers that protect against aminoglycoside-induced ototoxicity.

To identify small molecules that shield mammalian sensory hair cells from the ototoxic side effects of aminoglycoside antibiotics, 10,240 compounds were initially screened in zebrafish larvae, selecting for those that protected lateral-line hair cells against neomycin and gentamicin. When the 64 hits from this screen were retested in mouse cochlear cultures, 8 protected outer hair cells (OHCs) from gentamicin in vitro without causing hair-bundle damage. These 8 hits shared structural features and blocked, to varying degrees, the OHC's mechano-electrical transducer (MET) channel, a route of aminoglycoside entry into hair cells.

Identification of compounds that rescue otic and myelination defects in the zebrafish () mutant.

Adgrg6 (Gpr126) is an adhesion class G protein-coupled receptor with a conserved role in myelination of the peripheral nervous system. In the zebrafish, mutation of also results in defects in the inner ear: otic tissue fails to down-regulate gene expression and morphogenesis is disrupted. We have designed a whole-animal screen that tests for rescue of both up- and down-regulated gene expression in mutant embryos, together with analysis of weak and strong alleles.

Effect of missing data on multitask prediction methods.

There has been a growing interest in multitask prediction in chemoinformatics, helped by the increasing use of deep neural networks in this field. This technique is applied to multitarget data sets, where compounds have been tested against different targets, with the aim of developing models to predict a profile of biological activities for a given compound. However, multitarget data sets tend to be sparse; i.

Design of chemical space networks incorporating compound distance relationships.

Networks, in which nodes represent compounds and edges pairwise similarity relationships, are used as coordinate-free representations of chemical space. So-called chemical space networks (CSNs) provide intuitive access to structural relationships within compound data sets and can be annotated with activity information. However, in such similarity-based networks, distances between compounds are typically determined for layout purposes and clarity and have no chemical meaning.

Predicting bioactive conformations and binding modes of macrocycles.

Macrocyclic compounds experience increasing interest in drug discovery. It is often thought that these large and chemically complex molecules provide promising candidates to address difficult targets and interfere with protein-protein interactions. From a computational viewpoint, these molecules are difficult to treat.

Chemical space visualization: transforming multidimensional chemical spaces into similarity-based molecular networks.

Background: The concept of chemical space is of fundamental relevance for medicinal chemistry and chemical informatics. Multidimensional chemical space representations are coordinate-based. Chemical space networks (CSNs) have been introduced as a coordinate-free representation.

Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression.

Activity cliffs (ACs) are formed by structurally similar compounds with large differences in activity. Accordingly, ACs are of high interest for the exploration of structure-activity relationships (SARs). ACs reveal small chemical modifications that result in profound biological effects.

Monitoring the Progression of Structure-Activity Relationship Information during Lead Optimization.

Lead optimization (LO) in medicinal chemistry is largely driven by hypotheses and depends on the ingenuity, experience, and intuition of medicinal chemists, focusing on the key question of which compound should be made next. It is essentially impossible to predict whether an LO project might ultimately be successful, and it is also very difficult to estimate when a sufficient number of compounds has been evaluated to judge the odds of a project. Given the subjective nature of LO decisions and the inherent optimism of project teams, very few attempts have been made to systematically evaluate project progression.

Visualization of multi-property landscapes for compound selection and optimization.

Compound optimization generally requires considering multiple properties in concert and reaching a balance between them. Computationally, this process can be supported by multi-objective optimization methods that produce numerical solutions to an optimization task. Since a variety of comparable multi-property solutions are usually obtained further prioritization is required.

Synthesis, biological evaluation and molecular docking of N-phenyl thiosemicarbazones as urease inhibitors.

Urease is an important enzyme which breaks urea into ammonia and carbon dioxide during metabolic processes. However, an elevated activity of urease causes various complications of clinical importance. The inhibition of urease activity with small molecules as inhibitors is an effective strategy for therapeutic intervention.

Prediction of compound potency changes in matched molecular pairs using support vector regression.

Matched molecular pairs (MMPs) consist of pairs of compounds that are transformed into each other by a substructure exchange. If MMPs are formed by compounds sharing the same biological activity, they encode a potency change. If the potency difference between MMP compounds is very small, the substructure exchange (chemical transformation) encodes a bioisosteric replacement; if the difference is very large, the transformation encodes an activity cliff.

Advancing the activity cliff concept, part II.

We present a follow up contribution to further complement a previous commentary on the activity cliff concept and recent advances in activity cliff research. Activity cliffs have originally been defined as pairs of structurally similar compounds that display a large difference in potency against a given target. For medicinal chemistry, activity cliffs are of high interest because structure-activity relationship (SAR) determinants can often be deduced from them.

Systematic Identification of Matching Molecular Series and Mapping of Screening Hits.

Matching molecular series (MMS) have originally been introduced as an extension of the matched molecular pair (MMP) concept to facilitate the design of substructure-based structure-activity relationship (SAR) networks. An MMP is defined as a pair of compounds that only differ by a structural change at a single site. In addition, an MMS is defined as an MMP-based series of compounds that have a conserved structural core and are distinguished by modifications at a single site.

Matched molecular pair-based data sets for computer-aided medicinal chemistry.

Matched molecular pairs (MMPs) are widely used in medicinal chemistry to study changes in compound properties including biological activity, which are associated with well-defined structural modifications. Herein we describe up-to-date versions of three MMP-based data sets that have originated from in-house research projects. These data sets include activity cliffs, structure-activity relationship (SAR) transfer series, and second generation MMPs based upon retrosynthetic rules.

Formation of activity cliffs is accompanied by systematic increases in ligand efficiency from lowly to highly potent compounds.

Activity cliffs (ACs) are defined as pairs of structurally similar compounds sharing the same biological activity but having a large difference in potency. Therefore, ACs are often studied to rationalize structure-activity relationships (SARs) and aid in lead optimization. Hence, the AC concept plays an important role in compound development.

Draft Genome Sequence of the Steroid Degrader Rhodococcus ruber Strain Chol-4.

The whole-genome shotgun sequence of Rhodococcus ruber strain Chol-4 is presented here. This organism was shown to be able to grow using many steroids as the sole carbon and energy sources. These sequence data will help us to further explore the metabolic abilities of this versatile degrader.

Compound optimization through data set-dependent chemical transformations.

We have searched for chemical transformations that improve drug development-relevant properties within a given class of active compounds, regardless of the compounds they are applied to. For different compound data sets, varying numbers of frequently occurring data set-dependent transformations were identified that consistently induced favorable changes of selected molecular properties. Sequences of compound pairs representing such transformations were determined that formed pathways leading from unfavorable to favorable regions of property space.

Design of a three-dimensional multitarget activity landscape.

The design of activity landscape representations is challenging when compounds are active against multiple targets. Going beyond three or four targets, the complexity of underlying activity spaces is difficult to capture in conventional activity landscape views. Previous attempts to generate multitarget activity landscapes have predominantly utilized extensions of molecular network representations or plots of activity versus chemical similarity for pairs of active compounds.