Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Widespread use of herbicides results in the global increase in weed resistance. The rotational use of herbicides according to their modes of action (MoAs) and discovery of novel phytotoxic molecules are the two strategies used against the weed resistance. Herein, Random Forest modeling was used to build predictive models and establish comprehensive characterization of structure–activity relationships underlying herbicide classifications according to their MoAs and weed selectivity. By combining the predictive models with herbicide-likeness rules defined by selected molecular features (numbers of H-bond acceptors and donors, logP, topological and relative polar surface area, and net charge), the virtual stepwise screening platform is proposed for characterization of small weight molecules for their phytotoxic properties. The screening cascade was applied on the data set of phytotoxic natural products. The obtained results may be valuable for refinement of herbicide rotational program as well as for discovery of novel herbicides primarily among natural products as a source for molecules of novel structures and novel modes of action and translocation profiles as compared with the synthetic compounds.

SummaryThree metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed www.ftb.com.hrPlease note that this is an unedited version of the manuscript that has been accepted for publication. This version will undergo copyediting and typesetting before its final form for publication. We are providing this version as a service to our readers. The published version will differ from this one as a result of linguistic and technical corrections and layout editing.2 MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM-profiles was developed to detect putative genes for hydrocarbon-degrading enzymes.Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic n-alkane degradation. The use of this system for bioprospecting was exemplified using potential alkB and almA genes from this library.

Background: Seminoma is a testicular tumor type, routinely diagnosed after orchidectomy. As cfDNA represents a source of minimally invasive seminoma patient management, this study aimed to investigate whether cfDNA methylation of six genes from liquid biopsies, have potential as novel seminoma biomarkers. Materials & methods: cfDNA methylation from liquid biopsies was assessed by pyrosequencing and compared with healthy volunteers' samples. Results: Detailed analysis revealed specific CpGs as possible seminoma biomarkers, but receiver operating characteristic curve analysis showed modest diagnostic performance. In an analysis of panels of statistically significant CpGs, two DNA methylation panels emerged as potential seminoma screening panels, one in blood CpG8/CpG9/CpG10 ( KITLG) and the other in seminal plasma CpG1( MAGEC2)/CpG1( OCT3/4). Conclusion: The presented data promote the development of liquid biopsy epigenetic biomarkers in the screening of seminoma patients.

Increasing efforts are being made in the field of machine learning to advance the learning of robust and accurate models from experimentally measured data and enable more efficient drug discovery processes. The prediction of binding affinity is one of the most frequent tasks of compound bioactivity modelling. Learned models for binding affinity prediction are assessed by their average performance on unseen samples, but point predictions are typically not provided with a rigorous confidence assessment. Approaches such as conformal predictor framework equip conventional models with more rigorous assessment of confidence for individual point predictions. In this paper, we extend the inductive conformal prediction (ICP) framework for the dyadic data, such as compound-target binding affinity prediction task. The new framework is based on dynamically defined calibration sets that are specific for each testing interaction pair and provides prediction assessment in the context of calibration pairs from its compound-target neighbourhood, enabling improved guarantees based on local properties of the prediction model. The effectiveness of the approach is benchmarked on several publicly available datasets and through testing in more realistic scenarios with increasing levels of difficulty on a bespoke, complex compound-target binding affinity space. We demonstrate that in such scenarios, novel approach combining applicability domain paradigm with conformal prediction framework, produces superior confidence assessment with informative prediction regions compared to other state of the art conformal prediction approaches.