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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. These compounds were screened for Aβo binding by a combined 19F and STD NMR technique. Six hits were evaluated in three parallel biochemical and functional assays. Two compounds disrupted Aβo binding to its receptor PrPC in HEK293 cells. They reduced the pFyn levels triggered by Aβo treatment in neuroprogenitor cells derived from human induced pluripotent stem cells (hiPSC). Inhibitory effects on pTau production in cortical neurons derived from hiPSC were also observed. These drug-like compounds connect three of the pillars in Alzheimer's disease pathology, i.e. prion, Aβ and Tau, affecting three different pathways through specific binding to Aβo and are, indeed, promising candidates for further development.

Machine Learning techniques have been widely adopted to predict drug-target interactions, a central area of research in early drug discovery. These techniques have shown promising results on various benchmarks although they tend to suffer from poor generalization. This is typically related to very sparse and nonuniform datasets available, which limits the applicability domain of machine learning techniques. Moreover, widespread approaches to split datasets (into training and test sets) treat a drug-target interaction as an independent entities, when in reality the drug and target involved may take part in other interactions, breaking apart the assumption of independence. We observe that this leads to overly optimistic test results and poor generalization of out-of-distribution samples for various state-of-the-art sequence-based machine learning models for drug-target prediction. We show that previous approaches to reduce bias in binding datasets focus on drug or target information only and, thus, lead to similar pitfalls. Finally, we propose a minimum viable solution to evaluate the generalization capability of a machine learning model based on the systematic separation of test samples with respect to drugs and targets in the training set, thus discerning the three out-of-distribution scenarios seen at test time: (1) drug or (2) target present in the training set, or (3) neither.

To identify small molecules that shield mammalian sensory hair cells from the ototoxic side effects of aminoglycoside antibiotics we screened 10,240 compounds, selecting those that protected against neomycin and gentamicin in zebrafish lateral-line hair cells and, when retested in mouse cochlear cultures, prevented gentamicin-induced death of outer hair cells (OHCs). Of 64 compounds that protected zebrafish hair cells, 8 protect OHCs from gentamicin in vitro. These hits share structural features and all block, to varying degrees, the OHC’s mechano-electrical transducer (MET) channel, a known route of aminoglycoside entry into hair cells. Further characterisation of one of the strongest MET-channel blockers, UoS-7692, revealed it additionally protects against kanamycin and tobramycin, and does not abrogate the bactericidal activity of gentamicin. UoS-7692 behaves, like the aminoglycosides, as a permeant blocker of the MET channel, significantly reduces gentamicin-Texas Red loading into OHCs, and preserves lateral-line function in neomycin-treated zebrafish. Trans-tympanic injection of UoS-7692 protects mouse OHCs from furosemide-kanamycin exposure in vivo and partially preserves hearing. The results confirm the hair-cell MET channel as a viable target for the identification of compounds that protect the cochlea from aminoglycosides, and provide a series of hit compounds that will inform the design of future otoprotectants.

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. Results: A computational approach is presented for the transformation of multidimensional chemical space into CSNs. The design of transformation CSNs (TRANS-CSNs) is based upon a similarity function that directly reflects distance relationships in original multidimensional space. Conclusion: TRANS-CSNs provide an immediate visualization of coordinate-based chemical space and do not require the use of dimensionality reduction techniques. At low network density, TRANS-CSNs are readily interpretable and make it possible to evaluate structure–activity relationship information originating from multidimensional chemical space.

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 adgrg6 also results in defects in the inner ear: otic tissue fails to down-regulate versican 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. From a screen of 3120 structurally diverse compounds, we have identified 68 that reduce versican b expression in the adgrg6 mutant ear, 41 of which also restore myelin basic protein gene expression in Schwann cells of mutant embryos. Nineteen compounds unable to rescue a strong adgrg6 allele provide candidates for molecules that interact directly with the Adgrg6 receptor. Our pipeline provides a powerful approach for identifying compounds that modulate GPCR activity, with potential impact for future drug design.

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.e., not all compound-target combinations have experimental values. There has been little research on the effect of missing data on the performance of multitask methods. We have used two complete data sets to simulate sparseness by removing data from the training set. Different models to remove the data were compared. These sparse sets were used to train two different multitask methods, deep neural networks and Macau, which is a Bayesian probabilistic matrix factorization technique. Results from both methods were remarkably similar and showed that the performance decrease because of missing data is at first small before accelerating after large amounts of data are removed. This work provides a first approximation to assess how much data is required to produce good performance in multitask prediction exercises. Electronic supplementary material The online version of this article (10.1186/s13321-018-0281-z) contains supplementary material, which is available to authorized users.

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. Herein, we introduce a computational framework to follow the evolution of structure-activity relationship (SAR) information over a time course. The approach is based on the use of SAR matrix data structures as a diagnostic tool and enables graphical analysis of SAR redundancy and project progression. This framework should help the process of making decisions in close-in analogue work.

A systematic analysis of ionization states of current bioactive compounds is presented. Ionization states were related to biological activities on the basis of high-confidence activity data. The majority of bioactive compounds were found to be basic or neutral under physiological conditions. In addition, chemical neighborhoods of active compounds frequently contained analogs with different ionization states that were activity-conservative. However, a variety of targets were identified that displayed clear preferences for specific ionization states in compounds active against them. In this context, notable differences in the distribution of ionization states were detected for compounds active against different target superfamilies. Furthermore, under physiological pH, differences in ionization states of active compounds were tolerated by many targets. However, in a number of instances, ionization states of highly and weakly potent compounds active against the same target were found to be distinct, providing guidelines for compound design and optimization.

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. The ability to foresee such small chemical changes with significant biological consequences would represent a major advance for drug design. Nevertheless, only few attempts have been made so far to predict whether a pair of analogues is likely to represent an AC-and even fewer went further to quantitatively predict how "deep" a cliff might be. This might be due to the fact that such predictions must focus on compound pairs. Matched molecular pairs (MMPs), defined as pairs of structural analogs that are only distinguished by a chemical modification at a single site, are a preferred representation of ACs. Herein, we report new strategies for AC prediction that are based upon two different approaches: (i) condensed graphs of reactions, which were originally introduced for modeling of chemical reactions and were here adapted to encode MMPs, and, (ii) plain descriptor recombination-a strategy used for quantitative structure-property relationship (QSPR) modeling of nonadditive mixtures (MQSPR). By applying these concepts, ACs were encoded as single descriptor vectors used as input for support vector machine (SVM) classification and support vector regression (SVR), yielding accurate predictions of AC status (i.e., cliff vs noncliff) and potency differences, respectively. The latter were predicted in a compound order-sensitive manner returning the signed value of expected potency differences between AC compounds.

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. For example, flexible docking of macrocyclic compounds is hindered by the limited ability of current docking approaches to optimize conformations of extended ring systems for pose prediction. Herein, we report predictions of bioactive conformations of macrocycles using conformational search and binding modes using docking. Conformational ensembles generated using specialized search technique of about 70 % of the tested macrocycles contained accurate bioactive conformations. However, these conformations were difficult to identify on the basis of conformational energies. Moreover, docking calculations with limited ligand flexibility starting from individual low energy conformations rarely yielded highly accurate binding modes. In about 40 % of the test cases, binding modes were approximated with reasonable accuracy. However, when conformational ensembles were subjected to rigid body docking, an increase in meaningful binding mode predictions to more than 50 % of the test cases was observed. Electrostatic effects did not contribute to these predictions in a positive or negative manner. Rather, achieving shape complementarity at macrocycle-target interfaces was a decisive factor. In summary, a combined computational protocol using pre-computed conformational ensembles of macrocycles as a starting point for docking shows promise in modeling binding modes of macrocyclic compounds.

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. However, the underlying multi-dimensional property spaces are typically complex and difficult to rationalize. Herein, an approach is introduced to visualize multi-property landscapes by adapting the concepts of star and parallel coordinates from computer graphics. The visualization method is designed to complement multi-objective compound optimization. We show that visualization makes it possible to further distinguish between numerically equivalent optimization solutions and helps to select drug-like compounds from multi-dimensional property spaces. The methodology is intuitive, applicable to a wide range of chemical optimization problems, and made freely available to the scientific community.