dataesr/scientific-paragraphs-categorization huggingface dataset:
5. 8% respectively). A weak angle between NAR and CLogP vectors is consistent with the fact that CLogP increases with the number of aromatic rings. In view of these results, PCA confirms our preliminary observations that there are few outliers in PKI_ChEMBL dataset (dots on the upper right quadrant). It appears that these compounds correspond to either small-modified peptides or macrocyclic lactones with high TPSA values. These molecules, such as everolimus, were removed from PKIDB since they do not inhibit protein kinases directly, however the macrocycles in PKI_ChEMBL are active on protein kinases and thus were not removed from the dataset. Regarding compounds in PKIDB, semaxanib, has the lowest MW (yellow dot bottom-left). The two dots outside the circle and on the middle right of the quadrant corresponds to barasertib (Clinical_PKI in yellow) and fostamatinib (Approved_PKI in red).
Some of the MEK inhibitors are targeting an allosteric site adjacent to the ATP site. The three molecules from PKIDB closest to the extreme vertices are mubritinib near the rod, mavelertinib near the disc and galunisertib near the sphere. They are all in clinical trials, in phase 1, 0 and 2 respectively. Unlike approved PKI, a few compounds in development tend to adopt a disc shape that explores a new molecular space in PKIs. We also observe some compounds from PKI_ChEMBL dataset getting closer to the sphere vertex, showing that some spherical molecules could also inhibit protein kinases. These ones could open the way to the exploration of a potential novel chemical space. Here again, there is a great resemblance between the two datasets, PKIDB being well encompassed in PKI_ChEMBL regarding shape diversity.
The different types of molecular scaffolds are shown in Figure  5. For this study we used two types of scaffolds: Bemis and Murcko (BM) and graph framework issued from BM. As a reminder, Bemis and Murcko scaffold corresponds to the core of a molecule after removing side chains  [20]. The graph framework corresponds to BM scaffold where each heteroatom was substituted by a carbon and each multiple bond by a single one. Therefore, such frameworks cover topologically equivalent BM scaffolds differentiated by heteroatom substitutions and bond types. In Tanimoto similarity of about 0. 5 (Table  4 ). However, in the PKI_ChEMBL dataset, the scaffold diversity corresponding to the total number of molecules over the total number of BM scaffolds, is much lower with about a BM scaffold for about 2. 7 molecules in average.
Regarding the graph frameworks, their number tends to decrease compared to BM scaffolds i. e. one GF for 1. 1 and 5. 7 molecules in PKIDB and PKI_ChEMBL respectively. The most represented BM scaffold in PKIDB, the indolinone derivative (Figure  6 ), is retrieved in 3 inhibitors and differs from the one in PKI_ChEMBL, which is found 644 times. This scaffold is prominent compared to others in PKI_ChEMBL: the second most retrieved scaffold, the quinazoline derivative, is only found 239 times. It shows the importance of that scaffold in PKIs which is found only in erlotinib in PKIDB. The search for molecules containing K D highest occurrence of BM scaffold in PKI_ ChEMBL only returns 10 compounds, revealing a major difference between the two datasets. Then, for each unique BM scaffold in PKIDB, we checked how many PKIs are obtained in PKI_ChEMBL.
From the 207 unique BM scaffolds available in PKIDB, only 107 are present in PKI_ChEMBL which represent 2,423 molecules out of a total of 76,504 (3. 2%). This result is surprising. Firstly, we might expect that many analogues would be systematically provided for each PKI and thus would be available in a public database. Secondly, because PKIDB covers similar chemical space to PKI_ChEMBL according to PCA and PMI comparisons. Finally, using all unique graph frameworks from PKIDB, we were able to match 7,686 compounds (10. 0%) in PKI_ChEMBL. a Calculated with MACCS keys (166 bits) and the Tanimoto coefficient. N-phenylquinazolin-4-amine.
Ring analysis In PKIs, rings are making hydrogen bonds, van der Waals o π-stacking interactions with residues of the active site. As example, an heterocycle may form hydrogen bonds as does adenine in ATP with protein kinase  [30]. We applied a molecular decomposition method using RDKit to fragment molecules and retain only rings (Figure  7 ). After collecting all rings for both datasets, we searched for the most represented ones by gathering them using their smiles representation. We focused on fused heteroaromatic rings since such fragments are present as a main scaffold in most kinase inhibitors. Moreover, fused rings offer favorable interactions (van der Waals and hydrogen bonds) into the ATP binding site compared to non-fused rings  [31]. In both datasets, we found bicycles in around 65% of the molecules, demonstrating their importance as a core during hit to lead or lead optimization steps.
In PKIDB, we found 56 unique bicyclic scaffolds among the total of 172. More surprising, 31 out of these 53 bicycles are singletons, i. e. the bicyclic scaffold is found only once in the dataset. For the PKI_ChEMBL dataset, there are 918 unique bicycles for a total of 57,438. However, among those 918 unique bicycles, only 26 are singletons. Since the PKI_ChEMBL dataset contains more analogues of chemical series compared to PKIDB, this could explain the lowest ratio of unique fused rings. The number and the frequency of the top 10 most retrieved bicycles are illustrated in Figure  8. In both datasets, the quinazoline scaffold is the most represented bicycle, it remains an important core and its substituted analogues such as the 4-anilinoquinazoline have been extensively studied (acceptor or donor), with the hinge region of the kinase.
Interestingly, the PKIDB and the PKI_ChEMBL datasets contain almost the same top ten bicyclic scaffolds. Curiously, unlike BM scaffolds where more than half scaffolds from PKIDB were not retrieved in PKI_ChEMBL, here only three bicycles (not shown) are not found in PKI_ChEMBL dataset. We also performed an analysis of the bicyclic scaffolds by considering the attached atom position and atom type ( Figure  9 ). Atoms involved in a double bond linked to the scaffold were not modified. However, all atoms were replaced by a dummy atom labelled differently according to the atom type ( Figure  9 ). In this case, the 3-substituted (4,6,7) quinazoline is the most retrieved core in both datasets. Such a scaffold is found in twelve inhibitors in PKIDB, and an ether group (often a methoxy) is always attached on the 7 position.
Conclusion PKIDB is a freely available database containing all kinase inhibitors on the market or in clinical trials gathered using their international nonproprietary name (INN). This database, regularly updated, contains information on the structure of the kinase inhibitors, their physicochemical properties, their protein kinase targets as well as their therapeutic indications. It also contains links to various external databases. We analyzed this dataset and compared it to active PKIs found in the ChEMBL database. Classical physicochemical descriptors such as Lipinski or Veber showed that a significant part of protein kinase inhibitors, either approved or in clinical trials, does not follow the recommended drug-like thresholds, especially regarding molecular weight and calculated LogP. The projection of the chemical space resulting from a principal component analysis shows that most of the inhibitors shared the same chemical space.
However, the PKIs available in ChEMBL fill a larger chemical space in the PCA plot compared to PKIs in PKIDB. The distribution of the physicochemical descriptors for both datasets do not present major differences. This suggests that most active PKIs available in the ChEMBL have drug-like properties. Concerning the molecular shape of the PKIs, the PMI plot reveals that PKIs from ChEMBL exhibit a larger shape diversity compared to the ones in PKIDB. However, the majority of PKIs remain clustered around the rod-disc axis because they target a common ATP binding site in the kinase domain, which is highly conserved in this protein family. Yet, PKIs under development tend to explore wider topology, particularly near the disc edge. More frequent macrocyclic structures could contribute to this specific molecular shape.
Moreover, moving to new chemical space will help medicinal chemists to escape from a crowded intellectual property (IP) space. Regarding PKIs in ChEMBL, we also found some compounds escaping from this rod-disc axis and get closer to the spherical form. This information could be used to design new chemically-diverse kinase inhibitors. Concerning molecular scaffold analysis of the two datasets, it appears that PKIs in PKIDB The PKIDB database is regularly updated and is accessible from this website: http://www. icoa. fr/pkidb. We hope that this resource will assist researchers in their quest for novel kinase inhibitors.
exhibit a great molecular scaffold diversity compared to the ones in ChEMBL. More than 100 scaffolds from PKIDB are not present in the ChEMBL. Each molecule present in PKIDB and more particularly the corresponding scaffold, was patented preventing the design of analogues. Thus, each molecule present in PKIDB is in fact a representative of a chemical series, but only one new molecular entity (NME) was selected to continue its development in clinical phases. Most pharmaceutical companies will not unveil all chemical analogues of the selected NMEs limiting information on the chemical series. On the opposite, in a public database such as ChEMBL, there are often lots of available analogues for a specific scaffold. The ring analysis performed on the two datasets indicates a similar number of bicycle singletons despite the large size difference in the two datasets, 218 vs 76,504 molecules for PKIDB and PKI_ChEMBL respectively.
[26]arison of Lipinski's rules violation between PKIs approved, in clinical trials and inWe found that 62% and 68% of PKIs in PKIDB and in ChEMBL respectively do not violate any L p k. One single violation occurs in 28% and 24% of the PKIs for PKIDB and ChEMBL respectively and two violations occur for about 10% of the PKIs in the two datasets. Finally, few PKIs in ChEMBL dataset violates more than two rules (0. 2%) and none for the PKIs in PKIDB. These results may vary depending on how the LogP is calculated. Here, we used Wildman-Crippen approach[26]. Compared to the initial study, we removed the counter ion during the standardisation of the molecules such as the bromide ion in tarloxotinib. Despite the large different number of compounds in both datasets (76,504 molecules in ChEMBL and 218 in PKIDB) we reveal that the two datasets exhibit similar rule of five violation profiles.
PKIDB dataset, among 218 molecules, 207 present a unique BM scaffold and 195 a unique graph framework (GF). Whereas for the 76,504 PKIs present in ChEMBL, only 28,732 and 13,331 BM scaffolds and GF respectively are found (Table 4). In other words, in PKIDB almost each compound has a unique scaffold (218/207). The pairwise molecular similarity mean between PKIDB and PKI_ChEMBL, calculated with MACCS keys indicates that both datasets are diverse with mean of.
and PMI were calculated with RDKit o '2 8-09-. Scaffolds analysis and clustering were performed with RDKIT and with Butina algorithm [33] using Tanimoto similarity and Morgan Fingerprint with a radius of two (equivalent of FCPF4). The PCA was calculated with an in house library derived from Prince [34] and Scikit-learn [35] packages. For PMI analysis, 3D conformations were generated using ETKDG method [36] followed with an energy minimization using the MMFF94 forcefield [37]. To delimit the dots of the PMI triangle, three compounds (diacetylene, benzene and adamantane) were considered and added to the dataset. All the figures are made using matplotlib [38] and seaborn [39] packages. Molecules were drawn with Biovia Draw 2018. must have at least one recorded activity, either IC50, Ki or Kd, on a protein kinase with a pchembl value > 6 (< 1000 nM).
We then removed duplicates, empty SMILES and molecules from PKIDB. It is composed of 76,504 molecules. Both datasets (PKIDB and PKI_ChEMBL) have been prepared and standardized with VSPrep [40] and for each compound we kept the best tautomer as defined in VSPrep. The PKI_ChEMBL dataset results f o E L o ' E L_2. To be included in this dataset a compound.
Emission inventories based on provincial or national level data We used two inventories that are based on province-level data. China's carbon emissions from MEIC are developed by a technology-based methodology based on provincial energy consumption, combustion/industrial/control technologies and emission factor databases covering 2000 to 2016 at 0. 25, 0. 5, and 1 degree spatial resolutions by Tsinghua University  [18, 31, 50]. NJU calculated China's CO 2 emissions using provincial energy statistics and spatially distributed emissions based on the location of large point sources (power plants and cement plants) and various proxy data, i. e. , using GDP for industry-related emissions and population for transportation and other emissions, at 0. 25 degree resolution from 2000 to 2016  [5, 18].
Methods These inventories were first extracted by a JJJ mask (in shapefile format) from the National Geomatics Center of China using ArcGIS 10. 02 software (ESRI, 2012). The emissions from urban and non-urban areas were separated by using an urban mask from the European Space Agency (ESA) Climate Change Initiative (CCI) land cover maps with a 300 m resolution (https ://www. esa-landc over-cci. org), and the urban area here mainly refers to impermeable surfaces, with high coherence (stability) and bright reflections, maintained in time and under varying angles detected by satellite. This means there is a density of human structures such as houses, commercial buildings, roads, bridges, and railways, while a city refers to prefectural level zones with a legal definition that defines a physical geographic boundary.
A city with high productivity, rapid economic growth, and a large population tends to have high carbon emissions. Emissions from metropolises (e. g. , Beijing and Tianjin) with advanced economics and high urbanization rates greatly contributed to the JJJ total emissions (23-43%, Fig. 1 ). In Hebei Province, high emissions were also located in the provincial capital and in industrial cities, such as Shijiazhuang, Tangshan, Handan, and Baoding, which accounted for 57%-68% of the total emissions in Hebei. The 7 remaining cities accounted for 32-43% of the total emissions in Hebei. Since CHRED and CEADs use city-level statistical data and these two datasets are more consistent with each other than with the other datasets, they are used as references for national and provincial data-based inventories.
The emissions of Beijing from EDGAR, MEIC, and NJU are comparable to those from CHRED, with differences ranging from 1 to 8 (1-7%) Mt CO 2 in 2012. However, compared to the values estimated by CHRED, the emissions were 115% and 32% higher when estimated by ODIAC and FFDAS, respectively, and 31% lower when estimated by PKU. These trends were also found in Tianjin, and the emissions from ODIAC were equal to 227 Mt CO 2 , a value that was 47% higher than that from CHRED but 53% lower than that from PKU. Emissions in prefectural cities with coal mines and heavy-intensity industries also showed large differences, such as Tangshan and Handan. Tangshan's emissions varied largely across these datasets. CHRED and CEADs both produced larger estimates (182 and 282 Mt), while estimates from PKU, EDGAR, and FFDAS were only 69-87 Mt, or 52-62% lower, than that by CHRED.
Spatial pattern of CO 2 emissions The uneven spatial distribution of CO 2 emissions reflects the highly diverse conditions in population, economic development, and natural environment in the JJJ region. The spatial distributions showed reasonably good agreement in patterns such as the high emissions areas of Beijing-Tianjin-Tangshan, although they varied in detail (Fig. 2 ). Low emissions (e. g. , < 100 ton CO 2 km -2 ) from different datasets were mainly located in the northwestern part of the region. This result is because cities in the north, such as Zhangjiakou and Chengde, have a low population density and have lower economic development  [39]. High-emission areas (> 10,000 t CO 2 km -2 ) Specifically, the spatial distributions of CO 2 emissions from EDGAR and FFDAS are consistent with the pattern from CHRED.
Here, according to the urban extent from the ESA CCI land cover maps with a 300 m resolution (https ://www. esa-landc over-cci. org) (Fig. 2 ), we extracted urban emissions from the seven gridded inventories, namely, CHRED, NJU, MEIC, PKU, ODIAC, EDGAR and FFDAS. As illustrated in Fig. 3 , the proportion of urban emissions shows obvious differences among datasets, ranging from 17% (NJU) to 50% (ODIAC). As the largest contributors, the proportions of urban emissions in Beijing and Tianjin varied between 37% (NJU) -73% (ODIAC) and 23% (NJU) -63% (ODIAC), respectively. ODIAC tended to overestimate emissions in urban areas, resulting in the strongest urban-non-urban emissions gradients, especially in Tianjin (63% from urban) and Hengshui (79% from urban). This result is due mainly to the use of the nightlight proxy  [14, 58].
The time series of emissions in Tianjin from ODIAC, MEIC, NJU, CEADs, and CHRED all showed an apparent growing trend during 2009-2014, which mainly resulted from the increment of coal use (22%) and crude oil (90%)  [61]. The decline between 2005 and 2010 for CEADs was because of the change in statistical methods and dimensions, and blast furnace gas was lacking from the energy balance sheet during this period. For other periods, high emissions and large fluctuations in Tangshan's emissions from CEADs were partially caused by its heavy industrial system  [12, 39] , whose iron and steel production accounted for 60-70% of Hebei's total production. The emissions intensity (CO 2 emissions per unit of GDP) in the JJJ region showed a decreasing trend since 2000 (Fig. 4 ). Interannual changes in emissions intensity among datasets were consistent with the total emissions.
Comparison of city-level CO 2 emissions from different inventories to CHRED and CEADs CHRED and CEADs were used as references to evaluate other datasets because they are both based on citylevel energy statistics and consistent with each other. Furthermore, CHRED included unique comprehensive point sources (over 1. 5 million enterprises)  [33] , and CEADs used measured local emission factors from 602 coal samples and 4243 coal mines  [49]. As expected, the city-level CO 2 emissions showed the best correlation between CEADs and CHRED, with a correlation coefficient (R) as high as 0. 9 (Figs. 5a  and 6a ) and with a slope close to 1 and a smaller intercept than the others. The emissions from MEIC and NJU were both highly correlated with those from CEADs and CHRED, with R values ranging from 0.
8 to 0. 9 and slopes ranging from 0. 5 to 0. 7. These results were probably due to MEIC and NJU using provincial energy statistics as input data. CHRED and CEADs also used provincial inventories to calculate some city-level CO 2 emissions because of the lack of consistent and accurate energy statistics in certain cities  [12, 30]. PKU was strongly correlated with CEADs and CHRED, with R values both equal to 0. 8. The slopes were only 0. 2 and 0. 4 for the correlation relationships of PKU and CEADs and of PKU and CHRED, respectively (Figs. 5  and 6 ). The lower emissions estimated from PKU were mainly due to the rescaled energy data for China's total by IEA statistics. Emissions from EDGAR had a relatively weak relationship with those of CEADs and CHRED, with R values of 0. 6 and 0. 8, respectively.
Differences in prefectural-city-, provincialand national-statistical data-based inventories The differences between provincial-and national-statistical data-based emissions have been well discussed in previous studies  [19, 62, 63]. However, the differences between provincial and prefectural city statistical databased emissions are poorly understood. Thus, it is difficult to conclude which is closer to the truth. Of these inventories, CEADs provided both prefectural-city-and provincial-statistical data-based estimates. We found that prefectural-city-statistical data-based estimates were 60% higher than provincial-statistical data-based estimates (Fig. 7 ) from 2000-2010 and gradually decreased to approximately zero after 2012, i. e. , the sum of prefectural-statistical data became more consistent with provincial data in recent years due to the wider coverage of large-volume industries in local statistical authorities, indicating the improvement of statistical data between these two levels.
At the prefectural city level for JJJ and other cities, the estimated differences generally increased from less than 10% to more than 300% when more inventories were included (Table  2 )  [5, [64] [65] [66]. Generally, the prefecturalcity-statistical data-based inventories of CHRED and CEADs were more consistent than the others. For Hebei prefectural cities (such as Tangshan and Handan), the spread was relatively large among the three methods (i. e. , the national, provincial and prefectural statistics databased inventories), with differences ranging from 69 to 282 Mt CO 2 for Tangshan and from 61 to 130 Mt CO 2 for Handan in 2012 (Table  2 ). For Tangshan, the national inventories (ODIAC, EDGAR, PKU and FFDAS) were 69-117 Mt CO 2 , which were all smaller than the prefectural-city level and the provincial estimates of 138-282 Mt CO 2 (MEIC, CHRED, MEIC and NJU).
The ESA 300 m data showed more urban details, while Cai et al. [33]  used a homemade dataset based mainly on the county/district and town/township GIS data, which are more continuous in space, to depict the urban areas. However, our study and Cai et al. [33]  are largely consistent in their urban extent estimates for Beijing, Tianjin and Hebei and were approximately 20% lower than the CO 2 emissions for urban areas calculated by Cai et al. [33]. Specifically, at the prefectural city level, emissions from national-databased inventories were 53-75% lower in the industrial cities of Tangshan and Handan and 47-160% higher in Beijing and Tianjin than those from city-data-based inventories, and this difference was consistent with Gately and Hutyra  [66]. The implication to the community and society is that national-based inventories have more biases in prefectural city levels than in local databased inventories, and thus, we should be cautious when using national-based inventories for city-level use, such as in emissions evaluation, modeling research and policy making.
Point source contributions Point emissions consist of a large proportion of total emissions  [15, 33]. For the JJJ regional total, CHRED estimated the highest point emissions proportion of 78. 1%, including 12,991 industrial key emission sources and 187 industrial process sites  [33]. Other inventories produced much smaller proportions ranging from 19% for NJU to 43% for EDGAR (Fig. 8 ), and MEIC and EDGAR both had more than 100 large emission grids associated with power plants. Point sources for CHRED consisted of Fig. 7  Percentage differences between prefectural-city-based and provincial-based estimates for CEADs. For the years without data, values were interpolated using data from nearby years power plants, industries and industrial processes, while other inventories (e. g. , PKU, ODIAC) mostly used the CARMA dataset, which included only power plants.