dataesr/scientific-paragraphs-categorization huggingface dataset:
After filtering, there were 504,755 database molecules with a total of 4,167 known targets coming from 887,435 known ligand-target associations. Tested target prediction methods. Each ligand-centric target prediction method differently measures the similarity of the query molecule to a very large set of target-annotated molecules (the database molecules). Such similarity is defined by the adopted description of the chemical structure of each molecule (the fingerprint). The fingerprints available at RDKit (http://www. rdkit. org/) were used as the basis of this similarity calculation: Morgan fingerprints and FeatMorgan fingerprints  50  (ECFP-and FCFP-like fingerprints, respectively) with different radius (2 and 3) and encoding lengths (512, 1024 and 2048 bits); HashedMorgan fingerprint with 2048 bits and radius 2; RDKit fingerprints with minimum and maximum numbers of bonds (2 and 7, respectively) using 1024 and 2048 bits and number of bits per hash 1, 2 or 3; and MACCS keys.
Acknowledgements This study was financially supported by the  Austrian Science Fund  (project number  P28223-N34 ),  Research and Development Agency of the Slovak Republic  under the contracts No. APVV-15-0079  and  APVV-15-0053 ,  Scientific Grant Agency of the Slovak Republic  ( VEGA  Project  1/0871/16 ) and  Slovak University of Technology in Bratislava  ( Young Researcher Grant ,  M. Milunović, PhD ). This work was also supported by  Ministry of Education, Science, Research and Sport of the Slovak Republic  within the  Research and Development Operational Program  for the project " University Science Park of STU Bratislava ", ITMS 26240220084, co-funded by the  European Regional Development Fund.
ACKNOWLEDGMENTS The authors thank especially:  Bill R. Appleton  ( University of Florida ,  USA  and  Co-Editor  of  APR ) for the invitation and advice;  J. Fridmann  ( Raith America, Troy, NY, USA ) for proof reading and together with  S. Bauerdick  ( Raith GmbH, Dortmund, Germany ) and  A. Nadzeyka  ( Raith GmbH, Dortmund, Germany ) for the tremendous application work;  J. E. Sanabia  ( Raith America, Islandia, NY, USA ) and  R. Jede  ( Raith GmbH, Dortmund, Germany ) for proof reading, discussions, scientific guidance and suggestions;  K. E. Burcham ,  J. Fridmann  and  J. Klingfus  ( all Raith America, Islandia, NY, USA ) for native speaker proof reading;  H. Romijn  and  M. Butler  ( Raith BV, the Netherlands ) for assisting the EBL history section;  R. Schott  ( Ruhr-Universit€ at Bochum, Germany ), as well as  M.
Acknowledgements Research supported by the  Singapore National Research Foundation and Ministry of Education  under the  Research Centre of Excellence Programme. PJ was supported by  IGS-NTU. RC is funded through a ' Singapore Ministry of Education, Academic Research Fund Tier   1 RG141/15 '. Additional support was provided by the  National Research Foundation of Singapore  ( MSRDP-12 ). We thank  Dr. Hongjie An  for his help with AFM measurements.
4, EDTA 5 mM, Triton × 100 1%, protease inhibitor cocktail (Roche)) for 20 min on ice. Protein concentration was determined by the Bradford method and 50 µg of protein were loaded on 10% SDS-PAGE gels and transferred to PVDF membrane. Membranes were blocked with 5% non-fat milk in TBST buffer (20 mM Tris-HCl, 120 mM NaCl, 0. 1% Tween 20) for 1 h and then incubated with a cleaved-PARP primary antibody at 1/1000 (552596, BD Biosciences) and tubulin at 1/2500 (AB52866, Abcam) overnight at 4 °C. After washing, membranes were incubated with HRP-coupled secondary antibody for 1 h and signals were revealed with a chemiluminescence system (Biorad) and recorded with the Chemidoc Touch system (Biorad). Densitometric analysis was performed using Image J software.
TD-DFT spin-orbit couplings  62  were calculated with the Dalton2016 63, 64 suite of programs. At the TD-DFT level of theory, the electronic density reorganization of the excited states was analyzed in terms of natural transition orbitals (NTO)  65  obtained with the Nancy_EX code  66. Molecular Dynamics. All molecular dynamics simulations were carried out with the AMBER12 program  67. The 10-bp poly(A-T) oligonucleotide was built using the NAB module. DNA force field parameters were taken from parm99  68  with bsc1 corrections  69. Ibuprofen and ketoprofen atomic point charges were computed following the RESP protocol, while parameters were generated using the GAFF force field  68. The solvent was modeled by a TIP3P  70  water box. Starting structures of the DNA/NSAID complexes were taken from a former study of benzophenone interactions with DNA  71.
Results To determine the biological effect of UV exposure (that is by itself an important death inducer), on ibuprofen and ketoprofen pretreated cells, their viability was evaluated by measuring metabolically active cells that are supposed to reflect their intrinsic viability. As indicated in Fig. 2 , while no significant difference of cell death was observed after 15 and 30 seconds of UV irradiation alone, pretreatment of MCF-7 cells with either ibuprofen or ketoprofen significantly affected the number of metabolically active cells in the control condition as well as after UV treatment. To understand this effect, an analysis of cell death was performed by detecting the presence of cleaved PARP (Fig. 3 ), a marker of apoptosis. Our data showed a significant increase of cleaved PARP in MCF-7 cells pretreated with ibuprofen or ketoprofen compared to control cells, after UV irradiation.
Acknowledgements This research was supported by the  COST action  " MOLIM: Molecules in Motion ". M. M. is thankful to the  French and Austrian National Research Agencies (ANR and FWF, respectively)  for a grant under the  "DeNeTheor" project. Support  from the  COST "Action Biomimetic Radical Chemistry " in the form of a STSM is also acknowledged. L. A. E. gratefully acknowledges financial support from the  Faculty of Science at University of Gothenburg , and the  Swedish Research Council (VR). We acknowledge grants of computing time at the C3SE supercomputing center, via the  Swedish National Infrastructure for Computing (SNIC).
Real-time cell impedance measurement (XCELLigence technology). A background resistance of the E-plates (ACEA Biosciences, Inc. , San Diego, CA) was determined with 100 mL culture medium. HepaRG TM cells were seeded at 44 000 cells per well. E-plates were placed into the Real-Time Cell Analyzer (RTCA) station (ACEA Biosciences, Inc. , San Diego, CA) and incubated at 37 C. The adhesion phase of cells was recorded every 1 min during the first 12 h and then every 15 min up to 7 days. After 7 days, cells were exposed (n ¼ 3) to pristine, PEG-, and DMPC-coated M-MSNs at 1. 6, 16, and 80 mg/ cm 2 , and impedance data were monitored every 1 min for 24 h (early effects), then every 15 min for 96 h (late effects). The impedance of unexposed control cells was also recorded. Cell index (CI) raw data values were calculated by the RTCA software 2.
Hybridization was performed using Human SurePrint V3 8 Â 60K microarrays (Agilent). Fluorescence was recorded (Agilent Scanner) and signal data were extracted with Feature Extraction software (Agilent). Statistical analysis for transcriptomics. Raw fluorescence data files were submitted to GeneSpring GX 13 software (Agilent Technologies) using a widespread and robust method for determining the significant modulation of gene expression  (Wright et al. 2012; Pisani et al. 2015). Eighteen independent analyzes were conducted, namely for each experimental condition (18), three fluorescence data files from exposed cells were compared with three fluorescence data files from unexposed cells. Genes significantly up-or downregulated were determined using a Student's t-test with a p value set at 0. 05 and a Benjamini-Hochberg false discovery rate correction.
The fold-change cutoff was set at 2. We, thus, obtained lists of genes that were significantly induced or repressed after exposure to NPs. Integrative biological analysis. Lists of genes significantly induced or repressed (datasets) obtained after exposure to different types of NPs were uploaded into Ingenuity Pathway Analysis (IPA) software (Qiagen) for biological analysis. These datasets were processed to investigate gene functional annotation and distribution into known cell functions and canonical pathways, according to the IPA knowledge database. The significance of highlighted functions and pathways was calculated using a Fisher's exact test with a p value set at 0. 05. For each dataset, associations with known canonical pathways were expressed by ratios (in percentage), meaning the number of genes in a dataset that belong to a canonical pathway divided by the total number of known genes mapping this pathway.
A dose of 16 mg/cm 2 induced a steady moderate effect between 24 and 48 h but the highest dose, 80 mg/cm 2 , gave rise to a drastic increase in the number of altered genes between 24 and 48 h exposure. All fold-changes and p values of modulated transcripts by condition (doses and timepoints) are listed in ESI, Tables  S1-S3. We identified the most relevant canonical pathways involved, in the hepatic environment, after 24 and 48 h exposure, for all NP doses, using the IPA software. For each dose and time-point, we calculated several ratios indicating the percentage of modulated genes in our dataset belonging to known canonical pathways. The significance of these pathways was tested according to a Fisher's statistical test (p value <0. 05). Figure  6  reports the six most relevant canonical pathways altered by pristine, PEG-, and DMPC-coated M-MSNs (80 mg/ cm 2 , at 24 and 48 h).
2008). The choice of doses is essential in toxicology and must be as close as possible from the in vivo situation. Three concentrations to be tested were chosen for all types of NPs, that is 1. 6, 16, and 80 mg/cm 2 (corresponding to 6, 60, and 300 mg/mL, respectively) in accordance with both literature and our viability assays data  (Hudson et al. 2008; Witasp et al. 2009). In particular, this choice was based on the concentration of 40 mg/kg already used for biodistribution studies by injection in mice  (Lu et al. 2010; Liu et al. 2011; Rascol et al. , 2017). One milligram per mouse of 25 g with 2 mL of blood corresponds approximately to a concentration of 500 mg/ mL. This concentration did not alter liver tissues as observed by Rascol et al. , despite M-MSNs presence in the liver, attested by the silica content measured by ICP-MS.
2010). Thus, due to the presence of a protein corona around them, NPs may 'mislead' the cells, which may recognize these NPs as pathogen-like invaders, such as lipopolysaccharides (LPS) originating from bacterial outer membranes. The membrane receptors that act as 'molecular switches' may control the NP uptake and promote, as for LPS, the secretion of proinflammatory cytokines. Precisely, in our datasets, IL-1, TNFa, IL-6, IL-8 were heavily induced (Table  2 ). The gene encoding the C-reactive protein was heavily upregulated (FC ¼ 12). This protein is involved in several host defense-related functions based on its ability to recognize foreign pathogens or injured cells and to initiate their elimination by interacting with humoral and cellular effector systems in the blood. Consequently, the level of this protein in plasma increases greatly during acute phase response to tissue injury, infection, or other inflammatory stimuli such as NP invasion.
Hepatobiliary system disruption We performed biological data mining for all doses and time-points to extract all molecular signatures with no a priori hypothesis. We then analyzed and compared these responses, not in terms of gene-togene comparison but in terms of altered molecular pathways. The most relevant canonical pathways involved in the hepatic environment and revealed by this analysis are depicted in Figure  6. From this comparative pathway-driven analysis, we highlighted the possible sequence of events that leads to the disruption of hepatobiliary system, as represented schematically in Figure  7. All the genes mentioned in the text below are reported in Table  2.
Nuclear receptors The 'PXR/RXR pathway' is one of the most pertinent pathways elicited by M-MSN exposure in our results. Retinoid X receptors (RXRs) are nuclear receptors that mediate strong biological effects by dimerization with type II nuclear receptors, such as the pregnane X receptor (PXR/NR1L2), the constitutive androstane receptor (CAR/NR1L3), and the peroxisome proliferator-activated receptor (PPAR/PPARA). Many of these nuclear receptors were modulated in our datasets, including the small heterodimer partner (SHP/NR0B2), a regulator of bile acid metabolism  (Miao et al. 2009) , as shown in Table  2.
Figure6. Canonical pathways elicited by each M-MSN (80 mg/cm 2 ). The percentage of modulated transcripts of our datasets belonging to six major altered canonical pathways after (A) 24 h and (B) 48 h exposure to 80 mg/cm 2 M-MSNs. These pathways were all significant according to a Fisher's statistical test (p value <0. 05), revealed with Ingenuity Pathway Analysis (IPA, QIAGEN).
Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.
6% hit rate, whereas that of Vina only 16. 2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0. 1% achieves 88. 6% hit rate for 27. 5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0. 56 and -0. 18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github. com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http://github. com/ oddt/rfscorevs_binary). Structure-based Virtual Screening (VS) 1,2 aims at identifying compounds with previously unknown affinity for a target from its three-dimensional (3D) structure. Docking techniques are typically used to carry out this in silico prediction using their embedded scoring functions (SFs).
Here we present an in-depth analysis of machine-learning SFs with respect to their classical counterparts, both in terms of VS and binding affinity prediction. We use the full DUD-E 5 data sets for model building and performance assessment across 102 targets using three docking tools to generate the corresponding poses. Three machine-learning SFs using structural features with different degrees of complexity are used  12, 14, 22  and compared to five classical SFs. We assess the VS performance of the SFs in both established-target and novel-target settings, either tailored for broad application or for a specific target.
Data provenance. Benchmarking platform Directory of Useful Decoys -Enhanced (DUD-E) resource was used to generate various classes of non-overlapping training and testing sets intended to simulate possible application scenarios (data accessed 01. 08. 2016). The data set consisted of 102 protein targets, with a group of active molecules for each target (224 ligands on average) and decoys (50 decoys per active ligand). A ligand is considered active if its affinity (IC 50 , EC 50 , K i , or K d ) is 1 μ M or better. DUD-E decoys comprise ligands selected based on similarity to physical properties of known actives (for a particular target), but dissimilar in terms of 2D-topology. Though the inactivity of decoys has not been experimentally confirmed, these are likely to be inactive and thus this procedure minimizes the risk of selecting false negatives  5.
Training and test sets. Classical SFs are used off-the-shelf on the DUD-E benchmark test sets, which means that there might be a number of protein-ligand complexes in common with their training sets. However, this overlap should be small as these training sets contain at most a few hundred complexes (this is because the underlying linear regression model cannot learn beyond this point  12  ). By contrast, machine learning is able to exploit much larger training data sets, which would lead to a much larger overlap. Consequently, we use cross-validations on the DUD-E sets to avoid any protein-ligand complexes in common between training and test sets. With the purpose of representing different common scenarios with respect to how much data is available for the target, we have introduced three different kinds of stratified 5-fold cross-validations (see Fig.
Validation. The k-fold cross-validation (CV) is a commonly used strategy to reduce model overfitting. It also serves to assess applicability and generalization of predictions. Throughout this work we used 5-fold CV, which corresponds to an 80:20 test and training set ratio. This means that 80% of the data is used for training the machine-learning SF, which is afterwards tested on the remaining 20%. Such cross validation is repeated 5 times, meaning the whole dataset is divided into 5 groups (folds) of target-ligand complexes of approximately equal size. The folds are stratified -the proportions of actives/inactives preserved from original dataset. The function is learned on 4/5 of the groups and tested on 1/5 of the remaining complexes (on average). Importantly, each protein-ligand complex was present in training and test sets at some point, but never in both sets concurrently (i.
Ligand 3D conformations were generated with three docking programs; AutoDock Vina (the Smina implementation, [http://smina. sf. net/])  35, 36  , Dock 3. 6  37  and Dock 6. 6  [37] [38] [39]. Dock 3. 6 was not run in-house, instead the DUD-E docked conformations and scores were downloaded from the database website (http://dude. docking. org). Vina and Dock 6. 6 software were used with default parameters. Target proteins were prepared using UCSF Chimera  23  DockPrep tool. Docking box was constrained to 10 Å around the ligand (which was included in the crystallographic structure of every protein target). Ligands were prepared and sanitized using OpenBabel  40. 50 conformations were generated with each docking methodology. Classical Scoring Functions. Ligand-receptor complexes were scored using several classical, commonly used SFs.
We used internal SFs implemented in the three docking programs used (Vina, Dock3. 6, Dock6. 6). Additionally we used the CScore module of SybylX 2. 1  41  which implements: D_Score  38  , G_score (known as GoldScore  42  ), ChemScore  43  and PMF_Score  44. All of the above SFs have been widely used in VS. Descriptors and machine learning models. RF-Score is currently one of the best performing SFs at binding affinity prediction  12, 13. We used the descriptors from RF-Score versions v1 14 , v2 22 and v3  12. All versions use the same distance cutoff; a pair is tallied as interacting when the distance between the atoms falls within the 12 Å cutoff. They differ in the number of bins used. Bins divide the basal cutoff into smaller ranges, e. g. 2 Å bins (used by v2) translates to bins of following sizes: 0-2 Å, 2-4 Å, 4-6 Å, 6-8 Å, 8-10 Å, 10-12 Å.
Versions v1 and v3 use only one bin. Finally, v3 is enriched with Autodock Vina partial scores. Each RF-Score-VS version was trained on one best scoring ligand pose (meaning the lowest score from corresponding docking software). The number of trees in random forest (RF) was set to 500 as in the original implementation (this setting has been shown to be robust  45  ). The number of features to consider when looking for the best split in each RF tree ("mtry") was optimized using out-of-bag predictions (OOB). The optimized values are: 15 for v1 and v3 and 100 for v2 used in this study (i. e. we do not tune RF to DUD-E data). All RF-Score-VS calculations were done using ODDT  28. External dataset validation. DEKOIS 2. 0 3 database was used as external validation dataset. Four overlapping structures between DUD-E and DEKOIS 2.
0 were filtered out: A2A: "2p54", HDAC2: "3l3m", PARP-1: "3eml", PPARA: "3max". Protein SIRT2 had no crystal ligand, thus was also excluded. The final, non-overlapping dataset consisted of 76 targets. In addition, we have filtered out any ligand or decoy, which was found to be nearly identical (Tanimoto score of at least 0. 99; OpenBabel FP2 fingerprints) to any ligand/decoy present in DUD-E. Originally each DEKOIS protein was associated with 40 ligands and 1200 decoys, our pruning removed on average 18. 6 (46. 5%) ligands and 188 (15. 7%) decoys. Protein files were prepared according to DEKOIS 2. 0 publication 3 using Schrödinger Maestro suite. Afterwards ligands and decoys were docked using Autodock Vina with default settings, as previously done with DUD-E. Docked poses were rescored with RF-Score-VS v2 and v3 scoring functions.
Metrics. Enrichment factor (EF) is a fraction of active molecules within a given percentile of ranking list divided by random hit-rate. In DUD-E database there are 50 decoys per active ligand, hence random hit-rate is ~2%. DEKOIS database after pruning on average contains 21 actives and 1012, which translates to the same ~2% random hit rate as in DUD-E. Enrichment Factor, the area under Receiver Operating Characteristic curve (ROC AUC) and other metrics were calculated using ODDT  28.
Results and Discussion The aim of the work was to mimic VS campaigns using diverse targets having a number of active and inactive ligands. It is well known that in practice the number of inactives of a target is much larger than that of actives. DUD-E dataset  5  was built primarily to test performance of docking and scoring software, but it also fits into the constrains of a screening dataset described above. It contains of 102 targets associated with 22,886 ligands with measured activity. The target types are quite diverse and consist of receptors (GPCR, chemokine and nuclear), globular enzymes, kinases and virus proteases among others. It is also heterogeneous in case of ligand abundance; Catechol O-methyltransferase (COMT) has only 41 active compounds, compared to MAP kinase p38 alpha (MK14) which has 578 unique, dissimilar compounds.
On average there are 224 ligands per target and for each of them 50 decoys were generated according to procedure described by Mysinger et al. 5. These decoy compounds are presumed inactive because their chemical structures are dissimilar to those of known ligands. However, they are designed to share the same physiochemical features (number of donors/acceptors, etc. ), so that discrimination between actives and inactives is not trivial. So in this setup, an average screening campaign would search through 11 200 compounds to find 224 active ligands. The most common measure of retrospective VS performance is the enrichment factor (EF) of a method applied to a particular benchmark. When a large database of compounds is screened one takes the best scored compounds at the top of the ranked list for further evaluation.