dataesr/scientific-paragraphs-categorization huggingface dataset:
Ranking the impact of drugs in multi-omics data Finally, we investigated the effect sizes of the individual drugs across the multi-omics datasets. We found that metformin and omeprazole, in general, had the most pronounced effects on the multi-omics data (cumulative rank scores) and that the two statins ranked 14 and 20 out of the 20 drugs (Fig. 3e ) where simvastatin had the lowest overall rank of cumulative effect sizes. This analysis was not confounded by the number of individuals taking a particular drug as there was no correlation (PCC = 0. 14) between the number of individuals and drug effect. This was opposed to when investigating only significant associations where statins ranked 2 and 4 with high effect sizes (Supplementary Figs. 22  and 23 ). This observation may indicate that statins had fewer strong effects, whereas, for instance, both metformin and omeprazole with the highest average rank had larger systemic effects.
The cohort The cohort and available data included in the study are described in detail in Koivula et al. 62, 63  and Wesolowska-Andersen and Brorsson et al. (ref. 7 ). In brief, we used the newly diagnosed sub-cohort of the IMI-DIRECT study consisting of 789 participants. Fifty-eight percent of participants was male and participants had the following characteristics at baseline: age 62 (8. 1) years; body mass index 30. 5 (5. 0) kg m -2 ; fasting glucose 7. 2 (1. 4) mmol l -1 ; 2 h glucose 8. 6 (2. 8) mmol l -1. Participants were diagnosed within 2 years before recruitment and had glycated hemoglobin (HbA1c) < 60. 0 mmol mol -1 (<7. 6%) within the previous 3 months. All samples represent distinct individuals. Furthermore, while Wesolowska-Andersen and Brorsson et al. 7  used data from baseline and follow up at 18 and 36 months we only used baseline data for modeling.
In addition to the baseline data from Wesolowska-Andersen and Brorsson, we carried out extensive curation and harmonization of the medication records included in the electronic case forms by the research nurses in the different recruitment centers and thus used standardized ATC annotated medication data for the individuals (see further detail below). Approval for the study protocol was obtained from each of the regional research ethics review boards separately (Lund, Sweden: 20130312105459927; Copenhagen, Denmark: H-1-2012-166 and H-1-2012-100; Amsterdam, Netherlands: NL40099. 029. 12; Newcastle, Dundee, and Exeter, UK: 12/NE/0132) and all participants provided written informed consent at enrollment. The research conformed to the ethical principles for medical research involving human participants outlined in the declaration of Helsinki.
Pre-processing of data From the clinical, environmental, and questionnaire data only variables with variation across the dataset that were present in at least 10% of the individuals were included. The genomic data was included as the genotypes of risk alleles identified in Mahajan et al. 64. In total 393 risk alleles were identified in our cohort out of the 403 associations mentioned in the paper. The genotypes were included as homozygous for risk allele, heterozygote, not having the allele, or missing if the locus was not identified for the individual. Diet data was included as 47 features on self-reported total intake of macronutrients and vitamins across a 24-h period. The wearables measured with an accelerometer included 25 measurements that summarize the movement and heart rate during the day.
Transcriptomics data (RNA sequencing) from fasting whole blood samples were processed with RailRNA (v0. 2. 4b)  65  to obtain scaled counts for all samples and only the most variable genes were included. The variable genes were selected by calculating the standard deviation across all individuals for each gene and selecting genes with an above-average standard deviation. Both targeted and untargeted metabolomics data in fasting plasma were included for all measurements passing quality control. In the proteomics data, all measurements within the measurable range based on the OLINK antibody panel were included and residualized for plate layout. The metagenomics data was only available for approximately one-third (256) of the individuals and were included as normalized read counts of identified Metagenomic Species 66.
Categorical data, including questionnaire responses, drug data, and genomics, was one-hot encoded. The continuous data were residualized by the collection center as the data was collected from six different European countries and, thus, handled by different nurses and lab technicians, as well as differences in the time-of-day samples were taken, which could have a large effect on the measurements. Additionally, the data were residualized for age and sex as these could be biological non-disease-related confounders in the data. Lastly, each continuous dataset was z-scale normalized per feature to ensure that each feature was distributed around zero.
Classification of drugs using the ATC system The ATC system is the WHO classification system for therapeutic drugs. The system has a hierarchical structure, where the topmost level, 'level 1-Anatomical main group', specifies the target organ or tissue, and the lowermost level, 'level 5-chemical substance', specifies the active chemical compound. The three levels in between specify the therapeutic, pharmacological, and chemical levels, respectively. We, therefore, mapped all drugs to the lowest possible level to prevent information loss. A total of 4,155 entries could be mapped to level 5. For 55 entries, only a higher-level mapping was possible owing to lack of specificity and 43 entries could not be mapped to the ATC system, either because of the compound not existing in the database, for example nutraceutical compounds, or when we were unable to identify which drug was registered for the participant.
Drug data collection and clean-up The study participants were asked to register their current drug usage at screening and baseline. Drug names were registered as free text together with administration route, dosage and frequency, and indication. Metformin was recorded separately from other anti-diabetic and non-anti-diabetic drugs. The collected data was variable in quality, using both generic and brand names, which were in many cases specific to the country of the participant. The data was cleaned in four steps: (1) removal of special characters, company names, formulations, and other non-relevant information; (2) automatic mapping to the PubChem database; (3) manual mapping to generic drug names; and (4) mapping to the ATC system. Indications of placebo use, for example participation in clinical drug trials, were noted as such.
Design of the VAE The VAE framework was constructed to account for a variable number of fully connected hidden layers in both the encoder and decoder and a latent layer that samples from a Gaussian distribution N(0, 1) of two vectors of size N L representing the means, µ, and standard deviations, σ. Each hidden layer included both batch normalization and dropout  67  and with leaky rectified linear units (LeakyReLU)  68  as activation function. Each dataset was concatenated to one input layer of both categorical and continuous variables. To allow for dataset-specific weights the error calculation was done separately for each dataset. Here we applied cross-entropy loss for categorical data and mean squared error for continuous data as implemented in PyTorch  69. The loss was normalized by dataset input size and batch size.
Deviance from the Gaussian distribution was penalized by adding the Kullback-Leibler divergence (KLD) to the loss. The final loss was defined as L = W cat × E cat + W con × E con + W KLD × KLD https://doi. org/10. 1038/s41587-022-01520-x Here, E cat and E con are vectors of normalized reconstruction error for each of the continuous and categorical datasets. W cat and W con are vectors as well of the same length as the errors to introduce dataset-specific weights. We applied an equal weight of 1 for all datasets except for continuous clinical data where we used a weight of 2. W KLD is a weight put on the KLD defined as W KLD = β × N L -1 for which we used a β of 0. 0001 for the final model. The KLD was defined as KLD = ∑- 1 2 (1 + ln (σ) -μ 2 -σ) To efficiently handle missing data for the continuous features we encoded them as mean values across a particular feature during training and excluded the missing data points during back-propagation.
Hyperparameter optimization for multi-omics integration We initially divided the dataset into training (90%) and test (10%) sets to identify the optimal hyperparameter settings to efficiently capture the data structure without losing the ability to generalize on the test data (Supplementary Figs. 2  and 3 ). We tested different combinations of sizes of hidden layers, the number of hidden layers, size of latent space, dropout, and weight on the KLD. We then evaluated the model on the basis of both test log-likelihood and reconstruction accuracy. For the number of hidden neurons, the variations used were 200, 500, 800, 1,000, and 1,200, with the number of layers ranging between 1 and 5. The tested latent sizes were between 20 and 400 as well as dropout of 10%, 20%, and 30% and KLD weights of 0.
Both approaches were benchmarked against randomized datasets where all the input data matrices were shuffled on rows and columns. We simulated effects in the shuffled data by randomly sampling a combination of a drug, a multi-omics dataset, and a feature within that omics dataset. For each combination, we then sampled an effect from the standard normal distribution N(0,1) and added this value to the omics feature whenever the selected drug was taken by an individual. We, therefore, did not expect that all effects would be significant in the statistical tests because we sample from N(0,1) and some effects will be close to 0. We added a total of 100 effects to the shuffled data and repeated the entire procedure to generate two shuffled datasets each with their unique added effects. Additionally, we investigated if the number of significant associations, effect size estimates and model uncertainty in the reconstruction were not biased by individual dataset uncertainties.
Benchmarking of t-test, MOVE t-test and MOVE Bayes To be able to compare the number of significant associations between methods we used the two randomized datasets to estimate FDR from the ground truth, that is the added drug-omics effects (Supplementary Table  3 ). Here we found that a t-test with Benjamini-Hochberg FDR of 0. 01 had ground-truth FDR of 0. 00 and 0. 06 on the two randomized datasets, corresponding to 52 and 67 true positives as well as 0 and 4 false positives, respectively. For MOVE t-test, we benchmarked the number of refits of the 4 models and found 10 refits to have a ground-truth FDR of 0. 02 and 0. 06, with 48 and 61 true positives as well as 1 and 3 false positives, respectively. For MOVE Bayes we benchmarked the number of refits for a model with 150 latent neurons and found FDR from the cumulative evidence to be well aligned with FDR of the ground truth.
Calculation of drug associations using other methods We compared our findings to associations identified with standard statistical approaches using Student's t-test for unrelated samples and an ANOVA between two groups of individuals 'not receiving' and 'receiving' each drug. Here we used Benjamini-Hochberg correction for FDR  73  with an adjusted P < 0. 01. Additionally, we tested if a least absolute shrinkage and selection operator (LASSO) model was able to identify features with significant impact on predicting the 'not receiving' or 'receiving' groups for each drug. However, the LASSO model was unable to converge possibly owing to the high input feature dimensionality. All statistical tests were done with Python SciPy (v. 1. 3. 1)  71.
Drug effect size and similarities across omics data Drug effect sizes were determined as the difference between the baseline and drug-perturbed variational reconstructions, that is, as the average difference across the VAE ensemble refits reported with 95% confidence intervals. Drug similarities were calculated as the cosine similarity as implemented in Python SciPy (v. 1. 3. 1)  71 between the average effect sizes on all features identified as significantly associated for at least one of the drugs both across and within each dataset. The difference was only calculated for non-missing data and individuals not already on the drug or a drug in the same ACT group. The rank of drug effect sizes was determined for each omics dataset ranking the effect sizes from 1 to 20. A rank of 20 indicates that the drug had the highest average effect size in this omics dataset compared to the other drugs.
Molecular-focused analysis of the multi-omics data To get a better understanding of the molecular profiles identified in the associations for the transcriptomics and proteomics data we tested for enriched Gene Ontology terms as well as molecular pathways. For the transcriptomics data, we assessed the molecular patterns of biological processes and pathways from Reactome 74 (v. 3. 7) using the significantly associated genes for each drug against a background list of all genes included in the data integration. We used WebGestaltR 75 (v. 0. 4. 4) for the analysis with default settings (hypergeometric test) and evaluated all results with an FDR < 0. 05. The targeted metabolomics data was analyzed for potential metabolite enrichments using MetaboAnalyst 76 (v. 5) over-representation analysis using a hypergeometric test and FDR of 0.
Association differences within diabetes archetypes As mentioned, previous work by Wesolowska-Andersen and Brorsson et al. performed archetype analysis of the multi-omics data with only metformin medication data  7. Here they based the archetypes on clinical markers and identified four distinct and one 'mixed' T2D archetypes with clinical and omics profiles. To investigate if these distinct ar chetypes differed in their drug associations we used a t-test on the average effect size change for the individuals of each archetype against the remaining individuals. The analysis was only done for the significant drug associations for each drug. All analysis was only done for individuals not taking the drug or a drug within the same ATC therapeutical class similarly to the main analysis.
Drug-drug interactions We used an in-house drug-drug interaction compendium generated from publicly available sources (Supplementary Table  11 ) to assess whether drug combinations had been reported previously to be interacting or not  77. The compendium contains interactions from 26 different datasets of pharmacovigilance, clinically oriented information, schemas for NLP corpora, and drug-Cytochrome P450 relationships sources. For 12 of the drug-drug pairs in our dataset we could identify drug-drug interactions with reported severity (major, moderate, minor, possible, undetermined, and none) indicating clinical significance.
Data availability Owing to the informed consent given by study participants, the various national ethical approvals for the present study, and the European General Data Protection Regulation (GDPR), individual-level clinical and omics data cannot be transferred from the centralized IMI-DIRECT repository. Requests for access to summary statistics of the IMI-DIRECT data, including those presented here, can be made to https://doi. org/10. 1038/s41587-022-01520-x directdataaccess@dundee. ac. uk. Requesters will be informed on how summary-level data can be accessed via the DIRECT secure analysis platform following submission of an appropriate application. The IMI-DIRECT data access policy is available at https://directdiabetes. org. Example data is available at https://github. com/RasmussenLab/MOVE/ for testing of MOVE.
As described in the methods section we used ATC (https://www. who. int/tools/atc-ddd-toolkit/atc-classification) and WebGestalt (v. 0. 4. 4 at http://www. webgestalt. org) for analysis of Gene Ontologies, Reactome (v. 3. 7 at https://reactome. org) for analysis of molecular pathways, and MetaboAnalyst (v. 5 at https://www. metaboanalyst. ca) for analysis of targeted metabolomics data. The 25 databases of drug-drug interactions are listed in Supplementary Table  11. Source data are provided with this paper.
Acknowledgements We are grateful to IMI-DIRECT study participants who volunteered for phenotyping, and clinical and technical staff across involved European study centers who contributed to recruitment and clinical assessment of study participants. The work leading to this publication has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement  115317  (DIRECT), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. Information on the initiatives and activities of the IMI-DIRECT research consortium is available at https://directdiabetes. org. R. L. A. , A. T. L, R. H. M. , J. J. , V. B. , H. W. , J. N. N. , C. B. , G. M. , L. N.
A method is introduced for the automated analysis of reactivity exploration for extended in-silico databases of transition metal catalysts. The proposed workflow is designed to tackle two key challenges for bias-free mechanistic explorations on large databases of catalysts: (1) automated exploration of the chemical space around each catalyst with unique structural and chemical features and (2) automated analysis of the resulting large chemical datasets. To address these challenges, we have extended the application of our previously developed ReNeGate method for bias-free reactivity exploration and implemented an automated analysis procedure to identify the classes of reactivity patterns within specific catalyst groups. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed correlations between 1.
The common practice is to assume that the variations in the chemistry of the ligand environment (ligand functionalization) does not affect the main mechanistic and reactivity properties, but only the energetics of the associated paths. Therefore, one can first investigate in detail the mechanism and reactivity of a particular selected catalyst, followed by high-throughput screening using the descriptors or targets identified for this specific complex. However, one can expect that the chemical modification of a catalyst can open new mechanistic possibilities. The catalytic properties of the organometallic complexes are governed by a much wider reactivity space. Therefore, to enable the high-throughput in silico catalyst screening, one ideally has to explore the reactivity of each member of the catalyst library, with the associated problem of the combinatorial explosion resulting in an extremely large and complex datasets of results that need to be analyzed.
Modern computational chemistry methods are instrumental for such a task and have been used successfully in the past to screen through large databases of functionalized TM complexes  11, 12  for activity, regioselectivity, and ligand effects. [13] [14] [15]  In our studies aiming at understanding the design principles for homogeneous hydrogenation catalysis by manganese,  16, 17  Krieger et al. have carried out a high-throughput thermodynamic analysis of a virtual library of Mn pincer catalysts within the constraints of pre-defined deactivation chemistry. 18  Here, we aim at removing expert bias from the reactivity analysis by integrating the automated procedures for organometallic complexes generation, reactivity exploration and analysis into the unified HiREX workflow (Figure  1 ). The ChemSpaX 19 fully automated procedure is used for ligand functionalization and generation in silico catalyst library that form the input for the dynamic reactivity explorations with the ReNeGate 20 procedure.
Similar to the original ReNeGaTe 20 implementation, RMSD biased metadynamics simulations implemented in CREST  21  workflow have been used to exhaustively explore the chemical space around each catalyst. The reactivity exploration results are organized in a database with structural and reactive properties observed for every record as features in the database. The database is then analyzed to find correlations between structural and reactive features, followed by the extraction of reaction classes identified for each catalyst entry and family. Such systematic, automated and bias-free exploration and analysis has provided insights in defining reaction classes correlated with specific combinations of backbone and ligand modifications. This should be noted that with the application of our proposed methodology, we still operate within the assumption that the computational methodologies used for chemical space exploration and electronic structure calculations (e.
g. DFT functionals) are sufficiently accurate for the given chemical problem. Thus, our workflow inherits the inaccuracies derived from the description of the model system using specific level of theory. However, we have designed our workflow to be able to analyze trajectories of arbitrary accuracy and complexity and are agnostic in our analysis to any specific level of accuracy. 20  The paper is organized as follows: first, we provide an extensive description of the methodological aspects of the presented workflow. We start with the introduction of the functionalization strategy ChemSpaX used to construct the extended synthetic pincer catalyst set, followed by the description of the automated dynamic exploration workflow ReNeGate applied to this dataset. The methodology section is concluded by the presentation of the automated analysis method applied to multiple reactive trajectories generated for the extended synthetic catalyst dataset.
The individual entities in the database are automatically provided in parallel as input to the exploration step, where the chemical space around each structure is exhaustively explored for alternative chemical structures and new reactions. The detailed description of the ReNeGate workflow is provided elsewhere. 20  Further details on relevant molecular graph theory terminology and molecular graph theory in reaction identification are included in section S1 of the supporting information. We shortly summarize the key elements of the procedure below. The procedure involves an exhaustive reaction space exploration through root-meansquare-deviation (RMSD) biased meta-dynamics at the semiempirical xTB level of theory using the CREST functionality in the GFN-xTB code. 21  Recent studies demonstrate the sufficient accuracy of the xTB for high-throughput screening of TM complexes.
46  Implementation of the RMSD bias in the metadynamics simulations helps with the exhaustiveness of the exploration. CREST workflow sets a penalty on the configurations that have already been visited by calculating relative structural RMSD values for new configurations. Reactive trajectories from all metadynamics runs for all input structures from the ChemSpaX library are provided in parallel to the ReNeGate graph-theoretical analysis tool to identify unique structures and compare the observed configurations with the respective reference structures (Section 2. 3). Alternative explored structures for all starting geometries in the database are then collected in a global database. Each element in the database is labelled based on the known structural features of the reference structure (backbone scaffold, ligands at R 1 and R 2 position, adduct) as well as the calculated features including relative energies (∆E, kcal.