dataesr/scientific-paragraphs-categorization huggingface dataset:
Median of the differences The median difference for the GOME-2 vs. zenith-sky UV-Vis instrument comparisons shows strong deviations from zero, with both seasonal and irregular components. While the simulations predict some non-zero medians, they do not match the observed statistics, except for a few particular features at selected stations, e. g. at Scoresbysund and at the Observatoire de Haute Provence. Surprisingly, the best agreement is in fact observed at high southern latitudes (Dumont d'Urville and Rothera). In general though, most stations show some level of pathology, be it strong seasonality (e. g. Zhigansk), a drift (e. g. Aberystwyth), or any other erratic behaviour (e. g. Bauru). The SAOZ data obtained at Sodankylä were analyzed in detail by  Hendrick et al. (2011) , who find a similar disagreement with the Brewer located at the same station.
They become comparable to the measurement noise only for the comparisons with zenith-sky measurements in atmospheric conditions with particularly large gradients (e. g. near the polar vortex border). 5. By correcting the observed differences with the simulated metrology errors, the comparison spread and median become almost independent of co-location criterium, illustrating that the OSSSMOSE simulations are not only meaningful in a statistical sense, but also at the level of individual comparison pairs. 6. Uncertainties provided with the satellite data records contain only the formal spectral-fit uncertainties and as such underestimate the full (random) measurement uncertainty. The random uncertainties estimated by  Lerot et al. (2014) , however, are found to be too conservative. For the GODFITv3 GOME-2/MetOp-A product, a random uncertainty between 0.
7 % (tropics) and 1 % (mid and high latitudes) is shown here to suffice for comparison error budget closure. 7. Random uncertainties for the ground-based measurements appear reliable for most Brewers and Dobsons, except for the few stations that do not provide uncertainties. For the zenith-sky measurements, only DOAS fit uncertainties are provided with the data, and these clearly make up only a small part of the random uncertainty. The detailed uncertainty estimate by  Hendrick et al. (2011)  however, is found to be too conservative, as 1 % (tropics) to 2. 5 % (mid and high latitudes) random uncertainty suffices for comparison error budget closure. 8. The median of the differences, used to gauge systematic errors in the data sets over periods of the order of months and longer, often deviates much further from zero than can be accounted for by the OSSSMOSE simulations.
Acknowledgements. Part of this work was funded by the  Belgian Science Policy Office (BELSPO)  and  ESA  via the ProDEx projects A3C and ACROSAT, and by the  EU   H2020  project  GAIA-CLIM (Ares ( 2014 ) 3708963/Project 640276 ). GOME, SCIAMACHY and GOME-2A data reprocessing at IASB-BIRA was funded by  ESA's CCI Ozone project. ECMWF  and  NASA/GMAO  are thanked for providing IFS-MOZART and MERRA reanalysis, respectively. The Brewer, Dobson, and ZLS-DOAS data used in this publication were obtained as part of  WMO's Global Atmosphere Watch (GAW)  and the  Network for the Detection of Atmospheric Composition Change (NDACC). They are publicly available via the NDACC Data Host Facility (http://ndacc. org) and the  World Ozone  and  Ultraviolet Data Centre  (http://woudc. org). The authors acknowledge the dedication of the PIs and staff at the stations to acquire and maintain long-term ozone data records of high quality, as well as supporting projects like  ESA 's  CEOS Intercalibration of Ground-Based Spectrometers and Lidars.
21, 22 Thus, to better understand intra-family and inter-species selectivity both the target and the compound space need to be considered simultaneously. In ligand space, chemogenomic approaches relying only on ligand data have shown that there is an unequal distribution of ligand data. This is due to the fact that some target classes (e. g. GPCRs or kinases) have been traditionally regarded as more interesting from a medicinal chemistry standpoint, and are thus overrepresented in bioactivity databases. 23 Moreover, while some chemogenomic methods implicitly consider target information using bioactivity proles of groups of similar ligands, i. e. the interaction between these compounds and a panel of targets, they are outperformed by techniques that explicitly consider target information. 24, 25 In addition, bioactivity proles for related compounds are not always available.
Yet, target can also refer to a certain protein binding pocket (to allow distinction between binding modes, protein conformations, or allosteric/orthosteric binding), to a protein complex, or even to a cell line. 28, 29  Each binding site and each binding mode can be regarded (computationally) as a 'different target'. A PCM model is trained on a dataset composed of a series of targets and compounds, where ideally compounds have been measured on as many targets as possible (illustrated in Fig. 1 ). The simultaneous modelling of the target and the ligand space permits to better understand complex drug-target interactions (e. g. selectivity)  [30] [31] [32] [33]  than would be possible with chemogenomics as the effect of target and chemical variability can be evaluated (e. g. protein mutations or the effect of chemical substructures on bioactivity).
Thus, the aim of PCM is the complete modelling of the compound-target interaction space (Fig. 1 ), including also the prediction of the bioactivity of novel compounds on yet untested targets. Initial attempts to incorporate description of several proteins and their ligands in a single QSAR model involved modelling of the interaction between mutated glucocorticoid receptors and DNA. 34, 35 The rst full scale PCM study involving different proteins was devoted to the interaction of chimeric melanocortin receptors with chimeric peptides at Uppsala University. 36 The name "proteochemometrics" was coined later by the same research group. 15 Since then PCM has been applied on various diverse datasets (Table  1 ). 37, 38 While the current review will focus on recent developments in the eld, a comprehensive discussion of PCM-related work has been presented in a previous review by van Westen et al.
inuence of target variability on compound activity. For instance, predicting compound activity on a cancer cell line panel can identify selective compounds towards a particular cell line. 17 Similarly, the inuence of viral proteins mutations in compound activity can be quantied. 39 Therefore, PCM opens new avenues: (i) to mine drug affinity databases with the goal to create multi-target and multispecies models, (ii) to integrate toxicogenomics and phenotypic data in predictive models, (iii) to identify designed or natural ligands for orphan receptors (receptor deorphanization), (iv) and to design personalized medicine for viral infections or a dened cancer type based on genotypic information. The ability of PCM to model these data depends on the structure of the input matrix, as we will elaborate on below, and concrete examples referring to the above elds will be presented in the subsequent sections.
22, 54, 55 To date, binding sites in PCM models have been derived from single crystallographic structures,  22, 42, 55, 56  thus ignoring the intrinsically dynamic nature of proteins. However, databases such as Pocketome 57 might facilitate the introduction of dynamic properties of protein binding sites in PCM models as they contain ensembles of conformations for druggable binding sites extracted from co-crystal structures in the Protein Data Bank. To the knowledge of the authors, descriptors accounting for the dynamic properties of binding site amino acids have not been reported in the literature. Including this dynamic information might lead to a better description of protein targets in cases where small molecule binding is dependent on the binding site conformation, e. g. kinases. 4] [65] [66]  The availability of a plethora of target descriptors enables the application of PCM to target families where, for instance, little structural information is available.
Validation of PCM models ] [25]  The authors have trained PCM models on sparse datasets with a degree of matrix completeness in the 2-3% range that demonstrated good performance on the test set. 75 The statistical metrics proposed by Golbraikh and Tropsha 76 can be used (similar to QSAR) to validate models using observed and predicted values on the test set. 8] [79] [80]  In NCV, two validation loops are nested: the inner one serves to optimize the values of the hyperparameters through traditional k-fold cross-validation, whereas the outer loop serves to assess the predictive ability of the model trained on the whole training set. This procedure is repeated k 0 times, each time changing the composition of the training and the test sets. Thus, NCV does not provide the best parameter combination, as in each k 0 round the best values of the hyperparameters might change due to the variance of the different training sets.
Still, it provides the best estimate of the CV error as it provides an error interval, which can be wide depending on the dataset modeled. 80 owever, the degree of completeness of the ligand-target interaction matrix is only one parameter inuencing the predictive ability of a model. The variability on the chemical and the target side are the other two factors that need to be considered both in model validation and to assess its applicability domain. 75 Hence, the authors strongly suggest validating PCM models following a number of basic guidelines, which are in line with the recommendations from Park and Marcotte. 77 irstly, external validation (e. g. 70-30 validation), a model is trained on 70% percent of the data (training set) and the bioactivity for the remaining 30% (test set) is predicted.
Similarly to the LOTO validation, the PCM model trained on the remaining data is used to predict the bioactivity for the hold-out compound on each target. This data availability scenario corresponds to a Park and Marcotte C2 validation and resembles the situation where a PCM model is applied to novel chemistry in a e. g. prospective validation screening campaign. If the number of compounds in the training dataset is large, compound clusters can be used instead of single compounds, thus leading to the Leave-Once-Compound-Cluster-Out validation scenario (LOCCO). 17 n addition to these scenarios, the authors suggest to compare the performance of the PCM model trained on all data to single-target QSAR models. The goal of this validation is twofold. Firstly a direct comparison to QSAR can determine whether it is wise to apply PCM to a data set.
This approach corresponds with a Park and Marcotte C3 validation. C3 validation is considered extrapolation rather than interpolation, as both parts of the pair (the ligand and the target) have not been seen in the training set by the model. Taken together, these validation scenarios enable a thorough and earnest validation of PCM models and a comparison to the state of the art. Finally, the authors also suggest to calculate the statistical metrics on, at least, the predictions calculated with three models trained on different subsets of the complete dataset, and to accompany them with the standard deviation observed over the repetitions. 75 Similarly, it is advisable to carefully estimate the maximum achievable performance given the uncertainty of the data. 17,75.
Nonetheless, the radial kernel still constitutes a common option when inducting bioactivity models given the necessity to tune only one kernel parameter, i. e. s, which in practice means shorter training times. Based on those results, the experienced user should keep in mind that although the radial kernel is a robust option with reliable results (in the experience of the authors), a proper kernel choice should be made on the basis of the data at hand. 93 ual Component SVMs (DC-SVM) are an extension of the classical SVM and have been applied by Niijima et al. 94  to a kinase dataset spanning the whole kinome. They proposed a dual component naïve Bayesian model in which kinase-inhibitor pairs are represented by protein residues and ligand fragments that form dual components. Hence the probability of being active is simply estimated as the ratio of bioactivity values between active and inactive pairs.
This method was further extended to SVMs by modifying a Tanimoto kernel to include compound fragments. PCM DC-SVMs outperformed ligand based SVMs (QSAR) in internal validation, as accuracies of 90. 9% and 86. 2% were respectively obtained. However the same level of accuracy was not achieved when using external datasets, which produced accuracies of 73. 9% and 81. 3% for DC-SVM and ligand based SVM. Therefore, these results do not permit to conclude that DC-SVM outperform SVM although this might happen with other datasets. A second type of SVMs, Transductive SVMs (TSVMs), have been applied to model 10 small (between $1000 and $3000 datapoints) and unbalanced QSAR datasets from the Directory of Useful Decoys (DUD)  95  repository displaying a balanced accuracy higher than 30% on some datasets with respect to SVM.
96  The concept relies on transduction, allowing the modelling of partially labeled data which cannot be included using regular SVM. TSVMs could be potentially extended to PCM and have been shown to outperform SVMs in some cases. 97, 98  third avor of SVMs are Relevance Vector Machines (RVMs). 99 The added value of RVM is the interpretability of the models, which is a consequence of their Bayesian nature. Each descriptor is associated to a coefficient, which determines its relevance for the model. Coefficients associated to low relevance descriptors are close to zero, hence the model becomes sparse and therefore permits shorter prediction times. Although the predicted variance is not informative in regression studies, class probabilities can be efficiently determined in classication. 100 VMs have been demonstrated by binary classiers trained on a subset of the MDDR database.
4, number of atoms <20, and molecular weight <300. Conversely, chemical substructures with a number of ring bonds between 18 and 32, without atoms with positive charge, and with a log D value between 3. 4 and 7. 5 were found to favour OATP inhibition. Although RFs have a high interpretability it should be noted that they do not output error estimates (as is also the case with SVM), although recent papers suggest the usefulness of the variance along the trees of a random forest model to determine its applicability domain. 102, 103 Error estimates are of tremendous importance given the high levels of noise and error annotations in public bioactivity databases. Thus, fully informative predictions should be accompanied by individual uncertainties. This issue can be remediated by applying Quantile Regression Forests (QRF) which infer quantiles from the conditional distribution of the response variable.
Gaussian Processes (GP) The determination of the applicability domain (AD) of a model (when are model predictions reliable or when can a model extrapolate) is one of the major concerns in bioactivity modelling (see previous studies  [105] [106] [107]  for comprehensive reviews). Major obstacles to the AD determination are the errors and uncertainties contained in bioactivity databases,  [108] [109] [110] [111]  which are mainly due to data curation and experimental errors,  110  as well as the accurate quantication of distances in the descriptor and the biological space, which would enable to anticipate prediction errors. Gaussian processes (GP) aim to address these concerns by permitting to handle data uncertainty as input into a probabilistic model. Fig. 3  illustrates the basic idea underlying GP modelling.
The prior probability distribution (Fig. 3A ) covers all possible functions candidate to model the data, each of which has a different weight determined by the kernel (covariance) parameters. Subsequently, only those functions from the prior distribution in agreement with the experimental data are kept (Fig. 3B ). The mean of this function is considered as the best t to the data. Given that each prediction is a Gaussian distribution, different condence intervals can be dened from its variance (Fig. 3B ). For a new compound-target combination, the bioactivity is predicted as a Gaussian distribution, in which the mean is the best prediction and its variance the uncertainty. A radial-kernelled GP with s ¼ 1 was employed to generate the figure. The python infpy package helped to produce the plots.
207 ao et al. 112  showed that SVMs performed, in general, slightly better than GPs when modelling a dataset composed of 128 ligand and 9 human amine GPCRs, although the models trained on the best combination of descriptors exhibited Q 2 values of 0. 744 and 0. 742 for GP and SVM respectively. Worth of mention, the difference in performance between GP and SVM was not assessed neither statistically nor by comparing the results of a series models trained on different resamples of the whole dataset. Moreover, the predicted error bars by the GP PCM models were not considered. More recently, Cortes-Ciriano et al. 75  showed the actual potential of GPs by applying both SVMs and GPs implemented with a panel of diverse kernels to multispecies PCM datasets, namely: human and rat adenosine receptors, mammal GPCRs and Dengue virus proteases.
Collaborative Filtering (CF) One of the requirements for PCM is that target (protein) features need to be dened explicitly (usually by physicochemical characterization of amino acids). While this approach is effective, it nevertheless requires a certain level of information about target sequences and structures. An alternative approach would be to infer target features from an unsupervised approach and not use them as model input a priori. This was done quite recently in multi-target QSAR study of multiple cell lines for the hedgehog signalling pathway. 115 ao et al. 115  incorporated a CF approach between 93 cyclopamine derivatives and four cell lines (BxPC-3, NCI-H446, SW1990 and NCI-H157), and showed that collaborative ltering multi-target QSAR outperforms normal QSAR for their dataset.
G protein-coupled receptors Early PCM virtual screening studies by Bock and Gough to identify ligands of orphan GPCRs (oGPCRs) used physiochemical properties of the amino acids of the entire primary sequence of GPCRs, such as accessible surface area or surface tension, rather than binding site residues. The authors screened 1. 9 million ligand-oGPCRs combinations and were able to identify 4357 highly active ligands of oGPCRs. The method, based on SVM, outputs a ranked list of putative oGPCRs ligands. In practice, the most relevant feature of their predictive pipeline is the description of GPCRs with only physicochemical descriptors, thus avoiding the usage of exact 3dimensional information of the receptors. 38 Subsequently, Jacob et al. 118  demonstrated that the usage of bioactivity data from 4051 GPCR-ligand combinations (80 human GPCRs from classes A, B and C, and 2446 ligands) extracted from the GLIDA GPCR ligand database  119  in PCM models improves the performance over single receptor models, leading to more reliable predictions.
These ngerprints were applied to two GPCRs datasets, namely: (i) 168 536 GPCR-ligand combinations (160 286 inactive and 8250 active combinations), and (ii) 234 137 GPCR-ligand combinations (202 019 inactive and 32 118 active combinations). The total number of GPCRs considered was 160. The authors reported a cross-validated classication accuracy higher than 0. 9 when using SVM, though the most predictive models on external datasets were not those presenting the highest accuracy values in cross-validation. 122 verall, PCM models trained on GPCRs binding site amino acid descriptors have proven to be a powerful approach to identify the GPCRs targets for a given compound, and to predict ligands for orphan GPCRs. The increasing availability of bioactivity data on GPCRs of interest and orthologous sequences,  75  as well as the development of novel methodologies to assess GPCRs similarity, is likely to increase the application of PCM on this target family in drug discovery campaigns.
These compounds are more selective and thus attractive as drug candidates. Given the ability of PCM to model bioactivities against related targets, it is very well suited to model the affinity of small molecule inhibitors to the kinase family. 16 Different PCM models have been reported to analyze drug selectivity and predict bioactivity proles against kinases. 66, 126 n a recent study by Cao et al. ,  126  the full kinase sequence space was described by alignment-independent 'Composition, Transition and Distribution' (CTD) features,  127  along with topological features of compounds. The dataset comprised a total number of kinase-compound interactions of 54 012, with data from 22 229 compounds and 372 kinases. The best RF model exhibited a classication accuracy in ve-fold crossvalidation of 93.
Interestingly, kinases similar in the sequence space exhibited high dissimilarity when assessing their similarity with the inhibitors bioactivities. This was assessed using 120 kinases with more than 15 bioactivity annotations, 14 400 datapoints in total. Thus, these data highlights the adequacy of considering chemical and target space to optimize kinase inhibitors. While high affinity is generally desired for drugs (except possibly in case of multicomponent therapeutics),  128  selectivity is equally important when targeting a protein family with highly similar binding sites, such as in this case kinases. Subramanian et al. 66  applied PCM models to a kinase dataset comprising 50 different proteins in the DFG-in conformation to better understand both the residue and compound features which determined whether the ATP-binding site of kinases are involved in compound binding.
The resulting PLS models, which included cross-terms (see Section 2. 3), demonstrated the added value of PCM over ligand based approaches, as statistically satisfactory QSAR models were reported for only 44% of the targets. More importantly, the models could be visually interpreted, thus enhancing the practical usefulness of PCM for the optimization of compound selectivity. (Further details on the study are given in Section 4. 4, as models targets were encoded with 3-dimensional information. ) The distinction between Type I and Type II inhibitors has been proved to be amenable to PCM by Mendez-Lucio et al. 129  In order to distinguish between Type I and Type II inhibitors, the authors trained a PCM model on a dataset consisting of 463 data points from the interaction matrix dened by 50 known kinase Type I (ATP-competitive) inhibitors against 12 different sequences of ABL1 (ve of them) in both the phosphorylated and non-phosphorylated state.
PCM models were trained on 92 compounds and 47 HIV-1 protease variants with about 160 K i values. The best PCM model exhibited a Q 2 value of 0. 827 on the external set. Next to these applications, PCM has been used to model the sensitivity of viral mutants to antiretroviral drugs, which could potentially guide HIV treatment. 145 Resistance testing and prediction using these models is achieved by incorporating genotypic (protein) and drug (chemical) data and subsequently linking them to phenotypic data (resistance). PCM then allows the prediction of optimal treatment regimens. 7] [148] [149]  For instance, van Westen et al. 145  trained PCM models based on a large clinical dataset composed of circa 300 000 datapoints combining both phenotypic and genotypic data. The application of PCM enabled the integration of the similarity of marketed drugs together with protein sequence similarity.