dataesr/scientific-paragraphs-categorization huggingface dataset:
The second one is known as virtual screening (multiple ligands, one target)  [2]. This consists of selecting the small molecules with the best predicted affinity for a known protein and identifying the best complex. Such in silico protocol helps to characterize biological processes such as the inhibition of a specific metabolic pathway. The third one is inverse virtual screening (one ligand, multiple targets)  [3]. The latter can be used in high-throughput screening workflow to find hits for a chemical structure extracted from a chemical library and a protein dataset  [4]. The list of docking software is comprehensive and has grown during the last decades. Some are open source (AutoDock, CABS-dock, FlexAID)  [5] [6] [7] , others are commercial (Glide, GOLD, Surflex-Dock)  [8] [9] [10]  or are even linked to a web service (Click Docking, AADS, DockingServer)  [11] [12] [13].
They are generally suitable for classical docking, but they are not designed for high throughput in silico screening, for instance, they involve timeconsuming and tedious manual steps for docking parameter selection. Therefore, some projects exist to alleviate indicated counterparts, such as VirtualFlow orchestrator, which is based on AutoDock 4. 2 docking engine  [14]. Nevertheless, this software still does not support the newly released AutoDock-GPU  [15]. Finally, even if the literature attests to the development of many frameworks, most are in-house developed and not available online. Inverse docking is a powerful tool in target discovery. However, since conventional tools are mainly developed for simple docking or screening and do not directly allow multiple receptors, most available tools suffer from a lack of speed in execution time and thus prevent fast identification of relevant biological targets and pharmacological agents.
It is also easy to access and highly configurable. AutoDock 4 allows evaluation of the free energy of binding and accelerates the search of relevant solutions through Lamarckian Genetic Algorithms  [5]. This software is described as giving a fast prediction of bound conformations and free energy of binding based on molecular mechanics equation added of entropic terms  [18]  as shown by Equation (1): ∆G = ∆G vdw + ∆G hbond + ∆G elec + ∆G con f orm + ∆G tor + ∆G sol (1) Equation (  1 ): Main equation of AutoDock 4 defining the free energy of binding. The terms ∆G vdw , ∆G hbond , ∆G elec and ∆G conform are molecular mechanics terms (respectively representing van der Waals, hydrogen bonds, electrostatic and conformational energies) while ∆G tor and ∆G sol are AutoDock 4 added terms representing torsional and desolvation energies.
In addition, AMIDE v1 was only able to perform inverse docking of one ligand at a time, which made tedious its use for High-Throughput inverse docking (multiple ligands, multiple proteins). This limitation could be bypassed by manual intervention at the end of each ligand inverse docking, but was still not competitive for large ligands and receptors databases screening. A new version, called AMIDE v2, was thus developed to speed up the inverse docking process. This framework is now based on AutoDock-GPU  [15]  and has benefited from the total revision of the programming workflow. The advent of a new version of AutoDock based on calculation parallelization using GPUs (Graphics Processing Units) was the start of AMIDE v2. The performance gains were intended to be considerable. As previously mentioned, several front-end modifications to AMIDE were made beyond the use of this latest version of AutoDock.
Estimation of Performance Enhancement with AMIDE v2 Compared to AMIDE v1 AMIDE version performances were compared by computing the total calculation time of docking of the ligand (4) on the 12 docking boxes of the calcium-dependent protein kinase 1 from T. gondii (TgCDPK1, 6BFA ID in the Protein Data Bank). Three comparisons were made for 1, 10, and 100 runs, the other parameters being kept constant. For AMIDE v1, the grid preparation was also considered since it was part of the workflow process. For this comparison, the calculation processes with AMIDE v1 and AMIDE v2 were distributed on a 14 cores processor architecture and one GPU, respectively. In the case of AMIDE v1, all the cores were used to prepare the grids, and 12 cores were used for the 12 boxes docking. As highlighted in Figure  2 , AMIDE v2 allowed a significant speed-up as compared to AMIDE v1.
Inverse Docking with AMIDE v1 and AMIDE v2: Performance Enhancement Observed on the Toxoplasma gondii Dataset We also compared the computation time for the whole inverse docking process with each AMIDE version for ligand (4) and the 87 proteins of the T. gondii dataset (Figure  3 ). Using AMIDE v2 with a single GPU requires 23 min to perform the complete reverse docking process (one ligand and 87 proteins), while 300 cores are required to perform the same tasks as quickly on AMIDE version 1. As a comparison, a PC-like configuration (8 cores) led to a calculation time of 656 min.
Towards the Confrontation of Ligands and Proteins Databases: Performance of AMIDE v2 in High Throughput Inverse Screening AMIDE v2 was evaluated in a double screening process between the T. gondii target dataset (87 proteins) and the database of nine ligands presented in Section 4 to evaluate the potential of the improved AMIDE version for further high throughput inverse screening applications involving tens of proteins and thousands of ligands. The evolution of the computation time with the number of GPUs is shown in Figure  4. Although the ligand database could be considered small, and the T. gondii dataset of average size, the whole process performed with AMIDE v2 generated a relatively significant number of output files (AutoDock DLG format). Indeed 9396 DLG files (12 boxes × 87 proteins × 9 ligands) were associated with 783 (9 ligands × 87 proteins) different protein-ligand docking experiments.
The allocation of a single GPU led to a calculation time of 215 min. The latter was divided by 3. 4 and 19. 5 when 3 and 9 GPUs were allocated, respectively. The exponential nature of the performance gain as a function of the number of allocated GPUs confirms the potential of AMIDE v2 to support in silico high throughput inverse screening. Finally, to demonstrate the performance of the system, the 1018 compounds of the essential National Chemical Library (NCL) were submitted to the complete receptor database (87 proteins). A total of 50 GPUs was used, and it took 13 h and 30 min to AMIDE v2 to achieve the 88,566 dockings. A total of 1,062,792 docking files were generated (12 grids × 87 receptors × 1018 ligands) for 21,255,840 docking poses (1,062,792 dockings × 20 runs).
Analysis of High Throughput Screening The analyses of high throughput screening of the 1018 compounds of essential NCL against the 87 receptors of the dataset was performed and data analysis is presented hereafter. Table  1  shows the top ten results of the protein-ligand complex ranking. The free energies of binding and the population associated with these clusters indicate the high affinity of these ligands towards the considered targets. Further bioassays will be performed to investigate the in vitro activity of these compounds on Toxoplasma gondii. Nevertheless, the comparison of top ligand 393 and co-crystallized ligand 9DG on protein 1FSG (Figure  5 ) shows that they are co-localized in the binding site. Due to structure analogy, similar hydrogen bonds are highlighted as well as π-stacking interactions.
Pose Comparison Obtained with the Two AMIDE Versions Execution time is a crucial parameter when massive in silico screening workflows are used for drug candidate discovery. The other determinant aspect of a docking approach is its reliability and ability to accurately predict the 3D conformation of a molecular complex. As a final validation step of the AMIDE v2 process, the results obtained for the complexes "ligand (7)/TgCDPK1 (PDB ID 6BFA)" and "co-crystallized ligand (UW5)/TgCDPK1 (PDB ID 6BFA)" were analysed. The poses corresponding to the most populated cluster with the lowest free energy of binding with AMIDE v1 and AMIDE v2 were compared and are shown in Figure  6. Cluster allocation was processed with an RMS tolerance of 2. 0 Å between conformations. Figure  6  highlights that the best poses are in the co-crystallized ligand binding site, confirming the relevance of the complexes being compared.
Pose Comparison Obtained with the Two AMIDE Versions Execution time is a crucial parameter when massive in silico screening workflows are used for drug candidate discovery. The other determinant aspect of a docking approach is its reliability and ability to accurately predict the 3D conformation of a molecular complex. As a final validation step of the AMIDE v2 process, the results obtained for the complexes "ligand (7)/TgCDPK1 (PDB ID 6BFA)" and "co-crystallized ligand (UW5)/TgCDPK1 (PDB ID 6BFA)" were analysed. The poses corresponding to the most populated cluster with the lowest free energy of binding with AMIDE v1 and AMIDE v2 were compared and are shown in Figure  6. Cluster allocation was processed with an RMS tolerance of 2. 0 Å Figure  6. Comparison of experimental and docked poses of the protein associated with 6BFA PDB ID.
From AMIDE v1 to AMIDE v2: Modifications and Improvements AutoDock-GPU is an OpenCL-accelerated version of AutoDock 4. 2. 6 that is also CUDA compatible. It was advertised as capable of accelerating calculations up to 56x in Solis-Wets local search method  [20]. More and more sectors such as scientific drills, financial simulations, and even AI in the broad sense have adopted GPGPU (General-Purpose computation on Graphics Processing Units) calculation. Computing under GPU is based on an embarrassingly parallel architecture consisting of grouping several hundred calculation units (cores). A GPU architecture is based on the SIMT (Single Instruction Multiple Thread) model, making it possible to launch parallel instances of a program on n different datasets. The AMIDE v2 workflow has been made compatible with GPU computing and can handle hundreds of calculations (see Figure  1 ) compared to monotasked CPU cores.
This leads to a drastic decrease in input/output errors by limiting data flow overload. The amount of IT resources is then reduced, and workload manager user limitations are lowered. The management of ligand inputs/outputs, as well as the respective association files (temporary and result files), were also optimized to favor the use of GPU acceleration. A configuration script was created to facilitate AMIDE installation and deployment. In addition, a specific python version and associated packages were also embedded so as not to be confronted with dependency or incompatibility errors. Pre-docking files' (grids, atom map) generation time was also highly reduced. One file was generated for each ligand and each protein grid, while universal (i. e. , the grid parameters for all available atom types) pre-docking files are now generated once for the whole dataset.
A script was written in bash to automatically submit ligand and protein file lists to AutoDock GPU, preventing unsolicited errors during AMIDE calculations due to inputoutput conflicts. This also means that large libraries of ligands can be screened automatically. Another consequence of having a unique submission list is that it allows the use of a SLURM Job Array. A job refers to a list of dockings to be performed and addressed to the requested resources. In this way, the queue manager is not slowed down, and input/output communications do not impact the AMIDE framework's general performances. Because AMIDE uses AutoDock, certain limitations, such as the constraint introduced on certain types of ligands, have always to be considered. The extensive list of AMIDE v2 compatible atom types is available in Table  2.
Receptors Dataset The receptors set was generated from a list containing approximately 200 PDB structures from Protein Data Bank (PDB). A functional dataset of 87 structures was obtained by selecting only X-ray structures with resolution less than 3 Å. Water and other crystallographic residues were removed. Only chains of interest were preserved according to global stoichiometry. When necessary, apo-forms were generated from co factor-forms such as NAD or AMP. Three proteins (PDB identifier 4OKR, 1QK5, 1DGM) include one mutation compared to the reference sequence, and two mutations are present for the protein 4A5B. No energy minimization was performed. Polar hydrogen atoms were added prior to grid calculations, and the receptors were divided into 12 boxes with the AMIDE dataset preparation function.
Ligands Dataset Nine small molecules were selected for this study according to their various chemical classes (atom type, structure) and torsional degrees of freedom (TORSDOF). They were chosen from PNMRNP (Predicted carbon-13 NMR data of Natural Products), a curated and annotated small natural molecules database  [21]. All the relevant characteristics are presented in Table  3 , and 2D structures are available in Figure  8. For high-throughput screening assay, French essential National Chemical Library (essential NCL) (https://chembiofrance. cn. cnrs. fr/en/composante/chimiotheque (accessed on 5 April 2021)) was used. The NCL presents more than 75,000 compounds of natural, synthetic or hemisynthetic origin deposited by French academic partner laboratories. The essential NCL represents the chemical diversity of this database through 1040 compounds.
Ligands Dataset Nine small molecules were selected for this study according to their various chemical classes (atom type, structure) and torsional degrees of freedom (TORSDOF). They were chosen from PNMRNP (Predicted carbon-13 NMR data of Natural Products), a curated and annotated small natural molecules database  [21]. All the relevant characteristics are presented in Table  3 , and 2D structures are available in Figure  8. For high-throughput screening assay, French essential National Chemical Library (essential NCL) (https:// chembiofrance. cn. cnrs. fr/en/composante/chimiotheque (accessed on 5 April 2021)) was used. The NCL presents more than 75,000 compounds of natural, synthetic or hemisynthetic origin deposited by French academic partner laboratories. The essential NCL represents the chemical diversity of this database through 1040 compounds.
Acknowledgments: The experiments were performed on the  ROMEO Regional High-Performance Computing Center  (https://romeo. univ-reims. fr/). The authors also warmly thank  Jean-Marc NUZILLARD  for our productive exchanges and his precious help, and the  Chaire MAgICS (Université de Reims Champagne-Ardenne)  for scientific support. We thank the infrastructure  Chem-BioFrance  (http://chembiofrance. org/)and particularly the  Chimiothèque Nationale (National Chemical Library)  for providing the essential NCL compounds database.
15 Figure 3. 153 Figure 3. Comparison of the calculation time between the two AMIDE versions. Data were graphed for ligand (4) docked on the T. gondii dataset (87 proteins). The use of a personal PC was represented through the consideration of 8 cores and 1 GPU in the case of AMIDE v1 and AMIDE v2, respectively. The calculation time obtained with AMIDE v1 became as good as the one obtained with AM-IDE v2 using 300 cores. The number of docking experiments per box was equal to 20.
Living" evidence synthesis is of primary interest for decision-makers to overcome the COVID-19 pandemic. The COVID-NMA provides open-access living meta-analyses assessing different therapeutic and preventive interventions. Data are posted on a platform (https://covidnma. com/) and analyses are updated every week. However, guideline developers and other stakeholders also need to investigate the data and perform their own analyses. This requires resources, time, statistical expertise, and software knowledge. To assist them, we created the "metaCOVID" application which, based on automation processes, facilitates the fast exploration of the data and the conduct of analyses tailored to end-users needs. metaCOVID has been created in R and is freely available as an R-Shiny application. The application conducts living metaanalyses for every outcome.
Data, analyses, and evidence summaries posted on the platform are updated every week. However, guideline developers and other stakeholders, apart from real-time access to high-quality data, also need to be able to investigate the data and the impact of different characteristics on the results as well as to produce their preferred evidence summaries. For example, different countries have different policies concerning the inclusion or not of preprints when forming COVID-19 guidelines. To address all these important needs, we developed and made freely available the 'metaCOVID' R-shiny application (https://covid-nma. com/metacovid/). This web-application allows the endusers of the COVID-NMA platform (such as the UK National Institute for Health and Care Excellence and the South African National Department of Health) and other external researchers to directly use the most up-to-date database and perform their preferred meta-analyses in a user-friendly environment.
Data The COVID-NMA database for pharmacologic interventions consists of outcome data, risk of bias assessments, and information on study and population characteristics. We search, on a daily basis to identify new eligible randomized control trials (RCTs) with results and we update the database once a week  (1). As of 7 July 2021, the database included 286 RCTs and 213 pharmacologic interventions (monotherapies or drug combinations). We focus on binary and time-to-event outcomes as well as on both efficacy and safety. The list of available outcomes for synthesis in the application is available in Appendix Table  1. Every RCT in our database is accompanied by domain-specific risk of bias assessments (for randomization, missing data, outcome measurement, etc. ) as well as by an overall risk of bias assessment based on the Cochrane RoB 2.
Specifically, the users can optionally exclude from the analysis all trials assessed at an increased level of risk of overall bias (i. e. high risk and some concerns or high risk only) and check whether there are important changes in the results of the meta-analysis. Finally, there is a lot of discussion on whether preprints should be included in the COVID-19 meta-analyses. Therefore, users can also exclude preprints in a sensitivity analysis and check whether this exclusion impacts the results. For the synthesis of the results we use the R package metafor  (11)  and we add enhancements to the forest plots (e. g. risk of bias assessments) using self-written functions. The source code of metaCOVID is available at https://github. com/TEvrenoglou/metaCovid.
Hide population severity Allows for hiding the severity of the population reported in each study; this can be useful especially for busy forest plots with many RCTs Hide treatment dose Allows for hiding the treatment dose reported in each study; this can be useful especially for busy forest plots with many RCTs Briefly, the first step for the user is to select the treatment comparison and the outcome of interest. Note that after selecting a treatment comparison, metaCOVID scans the database and provides in the list only those outcomes that are available for the chosen treatment comparison. Afterwards, the user can press the button "Create forest plot" to obtain the results of the metaanalysis with the default options. The produced forest plot can be downloaded with by pressing "Download forest plot".
In other words, it seems that studies having conflict of interest tend to favor the drug. We further see in Figure  1 , that there is one RCT with high risk of overall bias. Conducting a sensitivity analysis without this RCT or performing an available case analysis did not change materially the results of the meta-analysis (see Appendix Figure  1  and Appendix Figure  2 ). In this paper, we present a new online application, called metaCOVID, that provides a userfriendly environment for performing living meta-analyses of COVID-19 trials. Through this application, everyone can use the continuously updated COVID-NMA database to investigate the effects of the different treatments and characteristics that may affect the results. The application offers the opportunity to stakeholders without technical background and software knowledge to easily perform and present complex analyses and data structures.
Discussion and conclusions This study is the first to develop a NER to predict clinical relevance of PIs. It has been shown that clinical NER built on training data have better performances than those using rules  [3]. The comparison between labelization proposed by the NER model and reference labelization allowed us to select three labels, drug, dosage, and kidney (high precision and correct recall) to mitigate any possible bias. The label disease has a low IAA score explained by an ambiguity among the pharmacists to define a disease. A larger dataset could achieve to select other labels and to enhance performance of the NER model  [4]. The development of this clinical NER model will lead to create a predictive algorithm of clinical relevance of PIs.
This percentage varied from 9 to 41% in preclinical model tumors for the most recent studies on structures transferred to clinical trials (Table  3 ). Dosimetry was calculated with the MIRD formalism, taking into account activity in the source and the Sfactor derived from computed values  (Bolch et al. , 2009). S-factors are defined as the mean absorbed dose in the target organ per decay in the source organ  (Stabin, 2003). These S-factors were calculated by MONTE-CARLO simulation to increase accuracy and to provide a closed fit with reality  (Perrot et al. , 2014) ; they are used for specific personal dosimetry in the ongoing MELRIV1 trial (NCT03784625). Various melanoma cell lines were used in the preclinical models but most investigations were carried out on highly pigmented murine B16 cell lines grafted subcutaneously into C57Bl/6 mice  (Poste et al.
[ 131 I]BA52, a molecule derived from [ 131 I]MIP-1145, was tested in metastatic melanoma patients. This unique TRT clinical trial showed that of the five patients, two were still alive 2 years after treatment (Mier et al. , 2014). These patients received the higher activity, emphasizing the importance of dosimetry. Furthermore, a phase I clinical trial is currently assessing the safety and efficacy of the [ 131 I]ICF01012 molecule (NCT03784625) (Table.
) 25 100%, 95% (Nicole Moins et al. , 2002) 10 (6 hrs p. i. ) 40 75% (Sillaire-Houtmann et al. , 2004) 87 23%, 86% (Cachin et al. , 2014) [ 18 F]Mel050 B16: 9±0. 6 (3 hrs p. i. ) NCT01620749 (Denoyer et al. , 2010) [ SK-Mel-3: 9% (4 hrs p. i. ) (Joyal et al. , 2010) 25 (3 hrs p. i. ) 12. 2 Gy per GBq (Mier et al. , 2014) 9 <2 years in 3/5 receiving >4. 5 GBq [ 131 I]ICF01012 B16-F0: 41% (6 hrs p. i. ) 108 Gy/37 MBq NCT03784625 (Chezal et al. , 2008; Bonnet- (B16-BL6) Recruiting Duquennoy et al. , 2009) SK-Mel-3: 27% (6 hrs p. i. ) 52 Gy/75 MBq (Bonnet et al. , 2010; Degoul et al. , (SK-Mel-3) 2013; Viallard et al. , 2015) [ 18 F]P3BZA B16-F10: 17% (2 hrs p. i. ) NCT03033485 88 (2 hrs p. i. ) 6 controls 37 (2 hrs p. i. ) 5 patients.
Acknowledgments: R. Juodagalvyt ė was supported by the  LABEX MILYON  ( ANR-10-LABX-0070 ) of  Université de Lyon  within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency. G. Panasenko  was supported by the  Russian Science Foundation  grant  19-11-00033  operated by the  Moscow Power Engineering Institute  and  K. Pileckas  by the  European Social Fund  (project No  09. 3. 3-LMT-K-712-17-0003 )under grant agreement with the  Research Council of Lithuania (LMTLT).