Stereo Signature Molecular Descriptor

We present an algorithm to compute molecular graph descriptors considering the stereochemistry of the molecular structure based on our previously introduced signature molecular descriptor. The algorithm can generate two types of descriptors, one which is compliant with the Cahn–Ingold–Prelog priority rules, including complex stereochemistry structures such as fullerenes, and a computationally efficient one based on our previous definition of a directed acyclic graph that is augmented to a chiral molecular graph. The performance of the algorithm in terms of speed as a canonicalizer as well as in modeling and predicting bioactivity is evaluated, showing an overall better performance than other molecular descriptors, which is particularly relevant in modeling stereoselective biochemical reactions. The complete source code of the stereo signature molecular descriptor is available for download under an open-source license at http://molsig.sourceforge.net

Motivation for return to work and actual return to work among people on long-term sick leave due to pain syndrome or mental health conditions

<p><b>Purpose:</b> The purpose of this study was to investigate associations between motivation for return to work and actual return to work, or increased employability among people on long-term sick leave.</p> <p><b>Materials and methods:</b> Data by responses to questionnaires was collected from 227 people on long-term sick leave (mean = 7.9 years) due to pain syndrome or mild to moderate mental health conditions who had participated in a vocational rehabilitation intervention. The participants’ motivation for return to work was measured at baseline. At 12-month follow-up, change in the type of reimbursement between baseline and at present was assessed and used to categorise outcomes as: “decreased work and employability”, “unchanged”, “increased employability”, and “increased work”. Associations between baseline motivation and return to work outcome were analysed using logistic and multinomial regression models.</p> <p><b>Results:</b> Motivation for return to work at baseline was associated with return to work or increased employability at 12-month follow-up in the logistic regression model adjusting for potential confounders (OR 2.44, 95% CI 1.25–4.78).</p> <p><b>Conclusions:</b> The results suggest that motivation for return to work at baseline was associated with actual chances of return to work or increased employability in people on long-term sick leave due to pain syndrome or mild to moderate mental health conditions.Implication for rehabilitation</p><p>High motivation for return to work seems to increase the chances of actual return to work or increased employability in people on sick leave due to pain syndrome or mild to moderate mental health conditions.</p><p>The potential impact of motivation for return to work is suggested to be highlighted in vocational rehabilitation.</p><p>Rehabilitation professionals are recommended to recognise and take into consideration the patient’s stated motivation for return to work.</p><p>Rehabilitation professionals should be aware of that the patient’s motivation for return to work might have an impact on the outcome of vocational rehabilitation.</p><p></p> <p>High motivation for return to work seems to increase the chances of actual return to work or increased employability in people on sick leave due to pain syndrome or mild to moderate mental health conditions.</p> <p>The potential impact of motivation for return to work is suggested to be highlighted in vocational rehabilitation.</p> <p>Rehabilitation professionals are recommended to recognise and take into consideration the patient’s stated motivation for return to work.</p> <p>Rehabilitation professionals should be aware of that the patient’s motivation for return to work might have an impact on the outcome of vocational rehabilitation.</p

Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms

A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project

Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms

A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project

Machine Learning in Drug Discovery

Machine Learning in Drug Discovery, Swedish e-Science Academy 2015, Arlandastad, Stockhol

Förskoleklass och skolklass - samsyn med förhinder

Syftet med denna uppsats är att utifrån intervjuer, observationer och en textanalys undersöka språkutveckling och samverkan i förskoleklass och årskurs 1 genom följande forskningsfrågor: Hur ser pedagoger och lärare på förskoleklassens verksamhet och uppdrag utifrån barnets språkutveckling? Hur kan en samverkan påverka kontinuiteten i barnets övergång från förskoleklass? Hur är kontinuitet, samsyn och samverkan framskriven i läroplanens mål och riktlinjer? Resultaten av det empiriska materialet visade att pedagogernas och lärarnas syn på förskoleklassens verksamhet och uppdrag utifrån språkutveckling skiljdes åt på flera sätt. Det saknades en samverkan som bl.a. vid övergången hade kunnat ge pedagoger och lärare en bättre insyn i vad som tidigare gjorts/ska göras gällande elevernas språkutveckling. Då hade det kunnat skapas en kontinuitet där man ser till varje barn och dess förutsättningar och tidigare erfarenheter som på så sätt tas tillvara. Att pedagogerna i förskoleklass kan känna sig osäkra på vad de ska uppnå och att lärarna är stressade på grund av Lgr11, är förståeligt då riktlinjerna i kapitel två inte är tydliga nog med vad pedagogernas uppdrag är och där ett större ansvar läggs på läraren. Min slutsats blir därmed att det inte är utan svårigheter och utmaningar som vi skapar en samverkan mellan två verksamheter, där det finns en samsyn på barnet, dess lärande. Detta kan dels bero på olika föreställningar och attityder, men även på brist av tid och styrning uppifrån. Genom detta arbete är min förhoppning att lyfta frågan om samverkan igen, vars möjligheter vi antagligen redan känner till men som vi behöver bli påminda om. Detta för att vi ska kunna komma fram till ett samarbete där lärandemiljön är anpassad till barnets behov och inte dess ålder, en bro av möjligheter, en samsyn utan några förhinder

Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines

QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: <i>C</i>, γ, and signature height. <i>C</i> is a penalty parameter that limits overfitting, γ controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using <i>C</i> in the range of 1 to 100 and γ in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected

Conformal Regression for Quantitative Structure–Activity Relationship ModelingQuantifying Prediction Uncertainty

Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the resultant prediction intervals to create as efficient (i.e., narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges, and the different approaches were evaluated on 29 publicly available data sets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals, but other approaches were almost as efficient. This approach afforded an average prediction range of 1.65 pIC50 units at the 80% confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence

Beyond the Scope of Free-Wilson Analysis. 2: Can Distance Encoded R‑Group Fingerprints Provide Interpretable Nonlinear Models?

In a recent study, we presented a novel quantitative-structure–activity-relationship (QSAR) approach, combining R-group signatures and nonlinear support-vector-machines (SVM), to build interpretable local models for congeneric compound sets. Here, we outline further refinements in the fingerprint scheme for the purpose of analyzing and visualizing structure–activity relationships (SAR). The concept of distance encoded R-group signature descriptors is introduced, and we explore the influence of different signature encoding schemes on both interpretability and predictive power of the SVM models using ten public data sets. The R-group and atomic gradients provide a way to interpret SVM models and enable detailed analysis of structure–activity relationships within substituent groups. We discuss applications of the method and show how it can be used to analyze nonadditive SAR and provide intuitive and powerful SAR visualizations

Ligand-Based Target Prediction with Signature Fingerprints

When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power