Using machine learning techniques for rationalising phenotypic readouts from a rat sleeping model

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Target prediction utilising negative bioactivity data covering large chemical space.

BACKGROUND: In silico analyses are increasingly being used to support mode-of-action investigations; however many such approaches do not utilise the large amounts of inactive data held in chemogenomic repositories. The objective of this work is concerned with the integration of such bioactivity data in the target prediction of orphan compounds to produce the probability of activity and inactivity for a range of targets. To this end, a novel human bioactivity data set was constructed through the assimilation of over 195 million bioactivity data points deposited in the ChEMBL and PubChem repositories, and the subsequent application of a sphere-exclusion selection algorithm to oversample presumed inactive compounds. RESULTS: A Bernoulli Naïve Bayes algorithm was trained using the data and evaluated using fivefold cross-validation, achieving a mean recall and precision of 67.7 and 63.8 % for active compounds and 99.6 and 99.7 % for inactive compounds, respectively. We show the performances of the models are considerably influenced by the underlying intraclass training similarity, the size of a given class of compounds, and the degree of additional oversampling. The method was also validated using compounds extracted from WOMBAT producing average precision-recall AUC and BEDROC scores of 0.56 and 0.85, respectively. Inactive data points used for this test are based on presumed inactivity, producing an approximated indication of the true extrapolative ability of the models. A distance-based applicability domain analysis was also conducted; indicating an average Tanimoto Coefficient distance of 0.3 or greater between a test and training set can be used to give a global measure of confidence in model predictions. A final comparison to a method trained solely on active data from ChEMBL performed with precision-recall AUC and BEDROC scores of 0.45 and 0.76. CONCLUSIONS: The inclusion of inactive data for model training produces models with superior AUC and improved early recognition capabilities, although the results from internal and external validation of the models show differing performance between the breadth of models. The realised target prediction protocol is available at https://github.com/lhm30/PIDGIN.Graphical abstractThe inclusion of large scale negative training data for in silico target prediction improves the precision and recall AUC and BEDROC scores for target models.The authors thank Krishna C. Bulusu for proof reading the manuscript. LHM would like to thank BBSRC and AstraZeneca and for their funding. GD thanks EPSRC and Eli Lilly for funding.This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1186/s13321-015-0098-

Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes.

Traditional Chinese medicine (TCM) still needs more scientific rationale to be proven for it to be accepted further in the West. We are now in the position to propose computational hypotheses for the mode-of-actions (MOAs) of 45 TCM therapeutic action (sub)classes from in silico target prediction algorithms, whose target was later annotated with Kyoto Encyclopedia of Genes and Genomes pathway, and to discover the relationship between them by generating a hierarchical clustering. The results of 10,749 TCM compounds showed 183 enriched targets and 99 enriched pathways from Estimation Score ≤ 0 and ≥ 5% of compounds/targets in a (sub)class. The MOA of a (sub)class was established from supporting literature. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase nonreceptor type 2 (PTPN2) and digestive system such as mineral absorption. We found two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif, which agreed with the important treatment principle of TCM, "the foundation of acquired constitution" that includes spleen and stomach. In short, the TCM (sub)classes, in many cases share similar targets/pathways despite having different indications.Peer Reviewe

Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts.

BACKGROUND: An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. RESULTS: The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. CONCLUSION: Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.This is the author's accepted manuscript. The final version is available from Future Science in Future Medicinal Chemistry (http://dx.doi.org/10.4155/fmc.14.137)

MOESM1 of Target prediction utilising negative bioactivity data covering large chemical space

Additional file 1.  Supplementary data describing the scaffold composition, size of models and performance of actives-only ranking positions

Evaluation of IOTA-ADNEX Model and Simple Rules for Identifying Adnexal Masses by Operators with Varying Levels of Expertise: A Single-Center Diagnostic Accuracy Study

Objectives The discrimination of ovarian lesions presents a significant problem in everyday clinical practice with ultrasonography appearing to be the most effective diagnostic technique. The aim of our study was to externally evaluate the performance of different diagnostic models when applied by examiners with various levels of experience

Polypharmacological <i>in Silico</i> Bioactivity Profiling and Experimental Validation Uncovers Sedative-Hypnotic Effects of Approved and Experimental Drugs in Rat

In this work, we describe the computational (“<i>in silico</i>”) mode-of-action analysis of CNS-active drugs, which is taking both <i>multiple simultaneous hypotheses</i> as well as <i>sets of protein targets</i> for each mode-of-action into account, and which was followed by successful prospective <i>in vitro</i> and <i>in vivo</i> validation. Using sleep-related phenotypic readouts describing both efficacy and side effects for 491 compounds tested in rat, we defined an “optimal” (desirable) sleeping pattern. Compounds were subjected to <i>in silico</i> target prediction (which was experimentally confirmed for 21 out of 28 cases), followed by the utilization of decision trees for deriving polypharmacological bioactivity profiles. We demonstrated that predicted bioactivities improved classification performance compared to using only structural information. Moreover, DrugBank molecules were processed <i>via</i> the same pipeline, and compounds in many cases not annotated as sedative-hypnotic (alcaftadine, benzatropine, palonosetron, ecopipam, cyproheptadine, sertindole, and clopenthixol) were prospectively validated <i>in vivo</i>. Alcaftadine, ecopipam cyproheptadine, and clopenthixol were found to promote sleep as predicted, benzatropine showed only a small increase in NREM sleep, whereas sertindole promoted wakefulness. To our knowledge, the sedative-hypnotic effects of alcaftadine and ecopipam have not been previously discussed in the literature. The method described extends previous single-target, single-mode-of-action models and is applicable across disease areas