C-SPADE : a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms

The advent of polypharmacology paradigm in drug discovery calls for novel chemoinformatic tools for analyzing compounds' multi-targeting activities. Such tools should provide an intuitive representation of the chemical space through capturing and visualizing underlying patterns of compound similarities linked to their polypharmacological effects. Most of the existing compound-centric chemoinformatics tools lack interactive options and user interfaces that are critical for the real-time needs of chemical biologists carrying out compound screening experiments. Toward that end, we introduce C-SPADE, an open-source exploratory web-tool for interactive analysis and visualization of drug profiling assays (biochemical, cell-based or cell-free) using compound-centric similarity clustering. C-SPADE allows the users to visually map the chemical diversity of a screening panel, explore investigational compounds in terms of their similarity to the screening panel, perform polypharmacological analyses and guide drug-target interaction predictions. C-SPADE requires only the raw drug profiling data as input, and it automatically retrieves the structural information and constructs the compound clusters in real-time, thereby reducing the time required for manual analysis in drug development or repurposing applications. The web-tool provides a customizable visual workspace that can either be downloaded as figure or Newick tree file or shared as a hyperlink with other users. C-SPADE is freely available at http://cspade.fimm.fi/.Peer reviewe

Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies

Owing to the intrinsic polypharmacological nature of most small-molecule kinase inhibitors, there is a need for computational models that enable systematic exploration of the chemogenomic landscape underlying druggable kinome toward more efficient kinome-profiling strategies. We implemented Virtual-KinomeProfiler, an efficient computational platform that captures distinct representations of chemical similarity space of the druggable kinome for various drug discovery endeavors. By using the computational platform, we profiled approximately 37 million compound-kinase pairs and made predictions for 151,708 compounds in terms of their repositioning and lead molecule potential, against 248 kinases simultaneously. Experimental testing with biochemical assays validated 51 of the predicted interactions, identifying 19 small-molecule inhibitors of EGFR, HCK, FLT1, and MSK1 protein kinases. The prediction model led to a 1.5-fold increase in precision and 2.8-fold decrease in false-discovery rate, when compared with traditional single-dose biochemical screening, which demonstrates its potential to drastically expedite the kinome-specific drug discovery process.Peer reviewe

Computational-experimental approach to drug-target interaction mapping : A case study on kinase inhibitors

Peer reviewe

Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells

Summary: Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes

Drug Target Commons 2.0: a community platform for systematic analysis of drug target interaction profiles

Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound-target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology and drug-repurposing applications, DTC version 2.0 includes updated clinical development information for the compounds and target gene-disease associations, as well as cancer-type indications for mutant protein targets, which are critical for precision oncology developments

Drug Target Commons : A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.Peer reviewe

Crowdsourced mapping of unexplored target space of kinase inhibitors

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. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.</p

Crowdsourced mapping of unexplored target space of kinase inhibitors

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. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Crowdsourced mapping of unexplored target space of kinase inhibitors

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

Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery

Introduction: Polypharmacology has emerged as an essential paradigm for modern drug discovery process. Multiple lines of evidence suggest that agents capable of modulating multiple targets in a selective manner may offer also improved balance between therapeutic efficacy and safety compared to single-targeted agents. Areas covered: Herein, the authors review the recent progress made in experimental and computational strategies for addressing the critical challenges with rational discovery of selective multi-targeted agents within the context of polypharmacological modelling. Specific focus is placed on multi-targeted mono-therapies, although examples of combinatorial polytherapies are also covered as an important part of the polypharmacology paradigm. The authors focus mainly on anti-cancer treatment applications, where polypharmacology is playing a key role in determining the efficacy-toxicity trade-off of multi-targeting strategies. Expert opinion: Even though it is widely appreciated that complex polypharmacological interactions can contribute both to therapeutic and adverse side-effects, systematic approaches for improving this balance by means of integrated experimental-computational strategies are still lacking. Future developments will be needed for comprehensive collection and harmonization of systems-wide target selectivity data, enabling better utilization and control for multi-targeted activities in the drug development process. Additional areas of future developments include model-based strategies for drug combination screening and improved pre-clinical validation options with animal models.Peer reviewe