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Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the resultant prediction intervals to create as efficient (i.e., narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges, and the different approaches were evaluated on 29 publicly available data sets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals, but other approaches were almost as efficient. This approach afforded an average prediction range of 1.65 pIC50 units at the 80% confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence.

The ability to define the regions of chemical space where a predictive model can be safely used is a necessary condition to assure the reliability of new predictions. This implies that reliability must be determined across chemical space in the attempt to localize “safe” and “unsafe” regions for prediction. As a result we devised an applicability domain technique that addresses the data locally instead of handling it as a whole—the reliability-density neighbourhood (RDN). The main novelty aspect of this method is that it characterizes each single training instance according to the density of its neighbourhood in the training set, as well as its individual bias and precision. By scanning through the chemical space (by iteratively increasing the applicability domain area), it was observed that new test compounds are successively included into the applicability domain region in such a manner that strongly correlates to their predictive performance. This allows the mapping of local reliability across different locations in the training set space, and thus allows identifying regions where the model has low reliability. This method also showed matching profiles between two external sets, which is an indication that it performs robustly with new data. Another novel aspect in this technique is that it is paired with a specific feature selection algorithm. As a result, the impact of the feature set used was studied from which the top 20 features selected by ReliefF yielded the best results, as opposed to using the model’s features or the entire feature set as commonly done. As the third novel aspect, in this work we propose a new scoring function to help evaluate the quality of an applicability domain profile (i.e., the curve of accuracy vs the applicability domain measure in question). Overall, the RDN showed to be a promising method that can correctly sort new instances according to predictive performance. As a result, this technique can be received by an end-user as proof of concept for the performance of a QSAR model in new data, thus promoting the user’s trust on the QSAR output.Graphical abstract. Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0182-y) contains supplementary material, which is available to authorized users.

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Proteasome inhibitors have shown relevant clinical activity in several hematological malignancies, namely in multiple myeloma and mantle cell lymphoma, improving patient outcomes such as survival and quality of life, when compared with other therapies. However, initial response to the therapy is a challenge as most patients show an innate resistance to proteasome inhibitors, and those that respond to the therapy usually develop late relapses suggesting the development of acquired resistance. The mechanisms of resistance to proteasome inhibition are still controversial and scarce in the literature. In this review, we discuss the development of proteasome inhibitors and the mechanisms of innate and acquired resistance to their activity—a major challenge in preclinical and clinical therapeutics. An improved understanding of these mechanisms is crucial to guiding the design of new and more effective drugs to tackle these devastating diseases. In addition, we provide a comprehensive overview of proteasome inhibitors used in combination with other chemotherapeutic agents, as this is a key strategy to combat resistance.

Regulated necrosis or necroptosis, mediated by receptor-interacting kinase 1 (RIPK1), RIPK3 and pseudokinase mixed lineage kinase domain-like protein (MLKL), contributes to the pathogenesis of inflammatory, infectious and degenerative diseases. Recently identified necroptosis inhibitors display moderate specificity, suboptimal pharmacokinetics, off-target effects and toxicity, preventing these molecules from reaching the clinic. Here, we developed a cell-based high-throughput screening (HTS) cascade for the identification of small-molecule inhibitors of necroptosis. From the initial library of over 250,000 compounds, the primary screening phase identified 356 compounds that strongly inhibited TNF-α-induced necroptosis, but not apoptosis, in human and murine cell systems, with EC 50 < 6.7 μM. From these, 251 compounds were tested for RIPK1 and/or RIPK3 kinase inhibitory activity; some were active and several have novel mechanisms of action. Based on specific chemical descriptors, 110 compounds proceeded into the secondary screening cascade, which then identified seven compounds with maximum ability to reduce MLKL activation, IC 50 >100 μM, EC 50 2.5-11.5 μM under long-term necroptosis execution in murine fibroblast L929 cells, and full protection from ATP depletion and membrane leakage in human and murine cells. As a proof of concept, compound SN-6109, with binding mode to RIPK1 similar to that of necrostatin-1, confirmed RIPK1 inhibitory activity and appropriate pharmacokinetic properties. SN-6109 was further tested in mice, showing efficacy against TNF-α-induced systemic inflammatory response syndrome. In conclusion, a phenotypic-driven HTS cascade promptly identified robust necroptosis inhibitors with in vivo activity, currently undergoing further medicinal chemistry optimization. Notably, the novel hits highlight the opportunity to identify new molecular mechanisms of action in necroptosis.

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