Papyrus: a large-scale curated dataset aimed at bioactivity predictions.

With the ongoing rapid growth of publicly available ligand-protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers' time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. To meet these challenges, we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million data points. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high-quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways and also perform some examples of quantitative structure-activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing an accessible data source for research.

Impact of cancer-associated mutations in CC chemokine receptor 2 on receptor function and antagonism.

CC chemokine receptor 2 (CCR2), a G protein-coupled receptor, plays a role in many cancer-related processes such as metastasis formation and immunosuppression. Since âˆ¼ 20 % of human cancers contain mutations in G protein-coupled receptors, ten cancer-associated CCR2 mutants obtained from the Genome Data Commons were investigated for their effect on receptor functionality and antagonist binding. Mutations were selected based on either their vicinity to CCR2's orthosteric or allosteric binding sites or their presence in conserved amino acid motifs. One of the mutant receptors, namely S101P2.63 with a mutation near the orthosteric binding site, did not express on the cell surface. All other studied mutants showed a decrease in or a lack of G protein activation in response to the main endogenous CCR2 ligand CCL2, but no change in potency was observed. Furthermore, INCB3344 and LUF7482 were chosen as representative orthosteric and allosteric antagonists, respectively. No change in potency was observed in a functional assay, but mutations located at F1163.28 impacted orthosteric antagonist binding significantly, while allosteric antagonist binding was abolished for L134Q3.46 and D137N3.49 mutants. As CC chemokine receptor 2 is an attractive drug target in cancer, the negative effect of these mutations on receptor functionality and drugability should be considered in the drug discovery process.

Pan-cancer functional analysis of somatic mutations in G protein-coupled receptors.

G Protein-coupled receptors (GPCRs) are the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used a dataset of mutations found in patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 genomes project. We explored cancer-related mutation patterns in all GPCR classes combined and individually. While the location of the mutations across the protein domains did not differ significantly in the two datasets, a mutation enrichment in cancer patients was observed among class-specific conserved motifs in GPCRs such as the Class A "DRY" motif. A Two-Entropy Analysis confirmed the correlation between residue conservation and cancer-related mutation frequency. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). Our approach was confirmed by re-discovery of established cancer targets such as the LPA and mGlu receptor families, but also discovered novel GPCRs which had not been linked to cancer before such as the P2Y Receptor 10 (P2RY10). Overall, this study presents a list of GPCRs that are amenable to experimental follow up to elucidate their role in cancer.

GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics.

Many contemporary cheminformatics methods, including computer-aided de novo drug design, hold promise to significantly accelerate and reduce the cost of drug discovery. Thanks to this attractive outlook, the field has thrived and in the past few years has seen an especially significant growth, mainly due to the emergence of novel methods based on deep neural networks. This growth is also apparent in the development of novel de novo drug design methods with many new generative algorithms now available. However, widespread adoption of new generative techniques in the fields like medicinal chemistry or chemical biology is still lagging behind the most recent developments. Upon taking a closer look, this fact is not surprising since in order to successfully integrate the most recent de novo drug design methods in existing processes and pipelines, a close collaboration between diverse groups of experimental and theoretical scientists needs to be established. Therefore, to accelerate the adoption of both modern and traditional de novo molecular generators, we developed Generator User Interface (GenUI), a software platform that makes it possible to integrate molecular generators within a feature-rich graphical user interface that is easy to use by experts of diverse backgrounds. GenUI is implemented as a web service and its interfaces offer access to cheminformatics tools for data preprocessing, model building, molecule generation, and interactive chemical space visualization. Moreover, the platform is easy to extend with customizable frontend React.js components and backend Python extensions. GenUI is open source and a recently developed de novo molecular generator, DrugEx, was integrated as a proof of principle. In this work, we present the architecture and implementation details of GenUI and discuss how it can facilitate collaboration in the disparate communities interested in de novo molecular generation and computer-aided drug discovery.

Using machine learning techniques to characterize sleep-deprived driving behavior.

OBJECTIVE: Sleep deprivation is known to affect driving behavior and may lead to serious car accidents similar to the effects from e.g., alcohol. In a previous study, we have demonstrated that the use of machine learning techniques allows adequate characterization of abnormal driving behavior after alprazolam and/or alcohol intake. In the present study, we extend this approach to sleep deprivation and test the model for characterization of new interventions. We aimed to classify abnormal driving behavior after sleep deprivation, and, by using a machine learning model, we tested if this model could also pick up abnormal driving behavior resulting from other interventions. METHODS: Data were collected during a previous study, in which 24 subjects were tested after being sleep-deprived and after a well-rested night. Features were calculated from several driving parameters, such as the lateral position, speed of the car, and steering speed. In the present study, we used a gradient boosting model to classify sleep deprivation. The model was validated using a 5-fold cross validation technique. Next, probability scores were used to identify the overlap of driving behavior after sleep deprivation and driving behavior affected by other interventions. In the current study alprazolam, alcohol, and placebo are used to test/validate the approach. RESULTS: The sleep deprivation model detected abnormal driving behavior in the simulator with an accuracy of 77 ± 9%. Abnormal driving behavior after alprazolam, and to a lesser extent also after alcohol intake, showed remarkably similar characteristics to sleep deprivation. The average probability score for alprazolam and alcohol measurements was 0.79, for alcohol 0.63, and for placebo only 0.27 and 0.30, matching the expected relative drowsiness. CONCLUSION: We developed a model detecting abnormal driving induced by sleep deprivation. The model shows the similarities in driving characteristics between sleep deprivation and other interventions, i.e., alcohol and alprazolam. Consequently, our model for sleep deprivation may serve as a next reference point for a driving test battery of newly developed drugs.

The use of machine learning improves the assessment of drug-induced driving behaviour.

RATIONALE: Car-driving performance is negatively affected by the intake of alcohol, tranquillizers, sedatives and sleep deprivation. Although several studies have shown that the standard deviation of the lateral position on the road (SDLP) is sensitive to drug-induced changes in simulated and real driving performance tests, this parameter alone might not fully assess and quantify deviant or unsafe driving. OBJECTIVE: Using machine learning we investigated if including multiple simulator-derived parameters, rather than the SDLP alone would provide a more accurate assessment of the effect of substances affecting driving performance. We specifically analysed the effects of alcohol and alprazolam. METHODS: The data used in the present study were collected during a previous study on driving effects of alcohol and alprazolam in 24 healthy subjects (12 M, 12 F, mean age 26 years, range 20-43 years). Various driving features, such as speed and steering variations, were quantified and the influence of administration of alcohol or alprazolam was assessed to assist in designing a predictive model for abnormal driving behaviour. RESULTS: Adding additional features besides the SDLP increased the model performance for prediction of drug-induced abnormal driving behaviour (from an accuracy of 65 %-83 % after alprazolam intake and from 50 % to 76 % after alcohol ingestion). Driving behaviour influenced by alcohol and alprazolam was characterised by different feature importance, indicating that the two interventions influenced driving behaviour in a different way. CONCLUSION: Machine learning using multiple driving features in addition to the state-of-the-art SDLP improves the assessment of drug-induced abnormal driving behaviour. The created models may facilitate quantitative description of abnormal driving behaviour in the development and application of psychopharmacological medicines. Our models require further validation using similar and unknown interventions.

QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping.

An affinity fingerprint is the vector consisting of compound's affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.

Body mass index related electrocardiographic findings in healthy young individuals with a normal body mass index.

INTRODUCTION: An increased body mass index (BMI) (>25 kg/m2) is associated with a wide range of electrocardiographic changes. However, the association between electrocardiographic changes and BMI in healthy young individuals with a normal BMI (18.5-25 kg/m2) is unknown. The aim of this study was to evaluate the association between BMI and electrocardiographic parameters. METHODS: Data from 1,290 volunteers aged 18 to 30 years collected at our centre were analysed. Only subjects considered healthy by a physician after review of collected data with a normal BMI and in sinus rhythm were included in the analysis. Subjects with a normal BMI (18.5-25 kg/m2) were divided into BMI quartiles analysis and a backward multivariate regression analysis with a normal BMI as a continuous variable was performed. RESULTS: Mean age was 22.7 ± 3.0 years, mean BMI was 22.0, and 73.4% were male. There were significant differences between the BMI quartiles in terms of maximum P-wave duration, P-wave balance, total P-wave area in lead V1, PR-interval duration, and heart axis. In the multivariate model maximum P-wave duration (standardised coefficient (SC) = +0.112, P < 0.001), P-wave balance in lead V1 (SC = +0.072, P < 0.001), heart axis (SC = -0.164, P < 0.001), and Sokolow-Lyon voltage (SC = -0.097, P < 0.001) were independently associated with BMI. CONCLUSION: Increased BMI was related with discrete electrocardiographic alterations including an increased P-wave duration, increased P-wave balance, a leftward shift of the heart axis, and decreased Sokolow-Lyon voltage on a standard twelve lead electrocardiogram in healthy young individuals with a normal BMI.

Number of ECG Replicates and QT Correction Formula Influences the Estimated QT Prolonging Effect of a Drug.

INTRODUCTION: The present analysis addressed the effect of the number of ECG replicates extracted from a continuous ECG on estimated QT interval prolongation for different QT correction formulas. METHODS: For 100 healthy volunteers, who received a compound prolonging the QT interval, 18 ECG replicates within a 3-minute window were extracted from 12-lead Holter ECGs. Ten QT correction formulas were deployed, and the QTc interval was controlled for baseline and placebo and averaged per dose level. RESULTS: The mean prolongation difference was >4 ms for single and >2 ms for triplicate ECG measurements compared with the 18 ECG replicate mean values. The difference was <0.5 ms after 14 replicates. By contrast, concentration-effect analysis was independent of replicate count and also of the QT correction formula. CONCLUSION: The number of ECG replicates impacted the estimated QT interval prolongation for all deployed QT correction formulas. However, concentration-effect analysis was independent of both the replicate number and correction formula.

Optogenetic activation of intracellular adenosine A2A receptor signaling in the hippocampus is sufficient to trigger CREB phosphorylation and impair memory.

Human and animal studies have converged to suggest that caffeine consumption prevents memory deficits in aging and Alzheimer's disease through the antagonism of adenosine A2A receptors (A2ARs). To test if A2AR activation in the hippocampus is actually sufficient to impair memory function and to begin elucidating the intracellular pathways operated by A2AR, we have developed a chimeric rhodopsin-A2AR protein (optoA2AR), which retains the extracellular and transmembrane domains of rhodopsin (conferring light responsiveness and eliminating adenosine-binding pockets) fused to the intracellular loop of A2AR to confer specific A2AR signaling. The specificity of the optoA2AR signaling was confirmed by light-induced selective enhancement of cAMP and phospho-mitogen-activated protein kinase (p-MAPK) (but not cGMP) levels in human embryonic kidney 293 (HEK293) cells, which was abolished by a point mutation at the C terminal of A2AR. Supporting its physiological relevance, optoA2AR activation and the A2AR agonist CGS21680 produced similar activation of cAMP and p-MAPK signaling in HEK293 cells, of p-MAPK in the nucleus accumbens and of c-Fos/phosphorylated-CREB (p-CREB) in the hippocampus, and similarly enhanced long-term potentiation in the hippocampus. Remarkably, optoA2AR activation triggered a preferential p-CREB signaling in the hippocampus and impaired spatial memory performance, while optoA2AR activation in the nucleus accumbens triggered MAPK signaling and modulated locomotor activity. This shows that the recruitment of intracellular A2AR signaling in the hippocampus is sufficient to trigger memory dysfunction. Furthermore, the demonstration that the biased A2AR signaling and functions depend on intracellular A2AR loops prompts the possibility of targeting the intracellular A2AR-interacting partners to selectively control different neuropsychiatric behaviors.

Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness.

Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness. From The ADNI database, 185 AD, and 225 healthy controls (HC) were randomly split into training and testing datasets. 165 subjects with mild cognitive impairment (MCI) were distributed according to the month of conversion to dementia (4-year follow-up). Structural 1.5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction. Using the resulting output, AD/HC classifiers were trained. Training included model tuning and performance assessment using out-of-bag estimation. Subsequently the classifiers were validated on the AD/HC test set and for the ability to predict MCI-to-AD conversion. Models' between-cohort robustness was additionally assessed using the AddNeuroMed dataset acquired with harmonized clinical and imaging protocols. In the ADNI set, the best AD/HC sensitivity/specificity (88.6%/92.0% - test set) was achieved by combining cortical thickness and volumetric measures. The Random Forest model resulted in significantly higher accuracy compared to the reference classifier (linear Support Vector Machine). The models trained using parcelled and high-dimensional (HD) input demonstrated equivalent performance, but the former was more effective in terms of computation/memory and time costs. The sensitivity/specificity for detecting MCI-to-AD conversion (but not AD/HC classification performance) was further improved from 79.5%/75%-83.3%/81.3% by a combination of morphometric measurements with ApoE-genotype and demographics (age, sex, education). When applied to the independent AddNeuroMed cohort, the best ADNI models produced equivalent performance without substantial accuracy drop, suggesting good robustness sufficient for future clinical implementation.

Importance of the extracellular loops in G protein-coupled receptors for ligand recognition and receptor activation.

G protein-coupled receptors (GPCRs) are the major drug target of medicines on the market today. Therefore, much research is and has been devoted to the elucidation of the function and three-dimensional structure of this large family of membrane proteins, which includes multiple conserved transmembrane domains connected by intra- and extracellular loops. In the last few years, the less conserved extracellular loops have garnered increasing interest, particularly after the publication of several GPCR crystal structures that clearly show the extracellular loops to be involved in ligand binding. This review will summarize the recent progress made in the clarification of the ligand binding and activation mechanism of class-A GPCRs and the role of extracellular loops in this process.