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-

Extending in Silico Protein Target Prediction Models to Include Functional Effects.

In silico protein target deconvolution is frequently used for mechanism-of-action investigations; however existing protocols usually do not predict compound functional effects, such as activation or inhibition, upon binding to their protein counterparts. This study is hence concerned with including functional effects in target prediction. To this end, we assimilated a bioactivity training set for 332 targets, comprising 817,239 active data points with unknown functional effect (binding data) and 20,761,260 inactive compounds, along with 226,045 activating and 1,032,439 inhibiting data points from functional screens. Chemical space analysis of the data first showed some separation between compound sets (binding and inhibiting compounds were more similar to each other than both binding and activating or activating and inhibiting compounds), providing a rationale for implementing functional prediction models. We employed three different architectures to predict functional response, ranging from simplistic random forest models ('Arch1') to cascaded models which use separate binding and functional effect classification steps ('Arch2' and 'Arch3'), differing in the way training sets were generated. Fivefold stratified cross-validation outlined cascading predictions provides superior precision and recall based on an internal test set. We next prospectively validated the architectures using a temporal set of 153,467 of in-house data points (after a 4-month interim from initial data extraction). Results outlined Arch3 performed with the highest target class averaged precision and recall scores of 71% and 53%, which we attribute to the use of inactive background sets. Distance-based applicability domain (AD) analysis outlined that Arch3 provides superior extrapolation into novel areas of chemical space, and thus based on the results presented here, propose as the most suitable architecture for the functional effect prediction of small molecules. We finally conclude including functional effects could provide vital insight in future studies, to annotate cases of unanticipated functional changeover, as outlined by our CHRM1 case study.LM thanks the Biotechnology and Biological Sciences Research Council (BBSRC) (BB/K011804/1); and AstraZeneca, grant number RG75821

MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products

The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for diferent enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difcult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2=0.96 overall for training and validation series. It involved a Monte Carlo sampling of>100,000 pairs of query and reference reac‑ tions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-trifylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo. This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.Ministerio de Ciencia e Innovación ( PID2019104148 GB-I00; PID2022-137365NB-I00), Gobierno Vasco IT1558-2

Serotonergic modulation of the ventral pallidum by 5HT1A, 5HT5A, 5HT7 AND 5HT2C receptors

Introduction: Serotonin's involvement in reward processing is controversial. The large number of serotonin receptor sub-types and their individual and unique contributions have been difficult to dissect out, yet understanding how specific serotonin receptor sub-types contribute to its effects on areas associated with reward processing is an essential step. Methods: The current study used multi-electrode arrays and acute slice preparations to examine the effects of serotonin on ventral pallidum (VP) neurons. Approach for statistical analysis: extracellular recordings were spike sorted using template matching and principal components analysis, Consecutive inter-spike intervals were then compared over periods of 1200 seconds for each treatment condition using a student’s t test. Results and conclusions: Our data suggests that excitatory responses to serotonin application are pre-synaptic in origin as blocking synaptic transmission with low-calcium aCSF abolished these responses. Our data also suggests that 5HT1a, 5HT5a and 5HT7 receptors contribute to this effect, potentially forming an oligomeric complex, as 5HT1a antagonists completely abolished excitatory responses to serotonin application, while 5HT5a and 5HT7 only reduced the magnitude of excitatory responses to serotonin. 5HT2c receptors were the only serotonin receptor sub-type tested that elicited inhibitory responses to serotonin application in the VP. These findings, combined with our previous data outlining the mechanisms underpinning dopamine's effects in the VP, provide key information, which will allow future research to fully examine the interplay between serotonin and dopamine in the VP. Investigation of dopamine and serotonins interaction may provide vital insights into our understanding of the VP's involvement in reward processing. It may also contribute to our understanding of how drugs of abuse, such as cocaine, may hijack these mechanisms in the VP resulting in sensitization to drugs of abuse