dataesr/scientific-paragraphs-categorization huggingface dataset:
This holistic process could help make the studies easier to find for humans and computer tools (ontologies, IDs, rich and indexed metadata, etc. ), accessible to all (open, free, and universally applicable protocols), interoperable with other (meta)data and datasets, and optimized to be reused and useful to other scientists. The connection between metabolomics and the other omics (genomics, transcriptomics, and proteomics) towards multi-omics, which for the moment is still in its infancy, will be easier and more straightforward. Research institutes without direct access to multi-scientific platforms will be less penalized and limited because they might have access to FAIR datasets and can build directly on primary work. The real potential of big data, which is impossible to explore in one study, will be unlocked.
The reproducibility and transparency of the published knowledge will be improved due to the curation of the coded workflows (shared in public repositories) and revision (journal's peer revision). Metabolite annotation, which is now the bottleneck of most studies, will be substantially improved by systematically using metabolite IDs and reference spectra sharing and via access to existing, searchable databases for confirmation of the chemical features of interest in different experimental contexts. On the other hand, we acknowledge that this process is often complex and timeconsuming and that guidelines, tutorials, and training are necessary. Since the general guidelines are often complicated and difficult to understand, it is necessary to support the research community and not work in isolation.
COST CA17111 INTEGRAPE decided to write the first specific guidelines for grapevine and wine metabolomics FAIR data and metadata management. It is encouraging to discover that the great majority of the publicly available metabolomic datasets in grapevine and wine science, and especially the studies uploaded to Metabo-Lights (Supplementary Materials Table  S1 ), are filed under FAIR principles, even though some were released before the FAIR publication. They all include the appropriate protocols, the use of ontologies and IDs, and have raw files uploaded on the website. There is only one study on a different public repository, which lacks many important metadata parameters and does not include raw files (but an excel document), but this is one of the oldest ones since it was released in 2013.
4, 17 Therefore, different levels of human intervention are required to efficiently discard false positives, making human inspection of docking poses a vital part of SBVS campaigns. 18 Hence, ultra-large scale SBVS would greatly benet from automatic hit selection, considering that meaningful human inspection of millions of seemingly well-docked molecules is impossible. In order to speed up and automate conventional docking (without signicant loss of valuable information), we recently developed Deep Docking (DD)an AI-driven platform that can work in conjunction with any docking program and provides economical yet reliable access to billion-sized chemical libraries for SBVS. 19 Recently, similar approaches to DD that utilize AI to predict docking outcomes have emerged, including but not limited to MolPAL (molecular pool-based active learning),  20  lean-docking,  21  and AutoQSAR/DeepChem models.
These consensus-based protocols were applied to the stereoisomer-expanded Enamine Real SPACE library,  29  combined with the ZINC15 database,  30  totaling 40 billion molecular entries that were screened against the active site of SARS-CoV-2 main protease (Mpro). The performance of these automated hit-calling approaches was further benchmarked against two strategies, implemented by human experts and involving visual examination of docking poses.
To benchmark the performance of the ve docking programs (Glide SP, Autodock GPU, FRED, ICM and QuickVina2) on Mpro enzymatic site as a target, we collected a set of 214 conrmed inhibitors from the literature (listed in Table  S1  †) and generated computational decoys using the Directory of Useful Decoys -Enhanced (DUD-E) server. 41 Utilizing all ve docking programs, the resulting benchmark dataset was docked into the Mpro catalytic site of the PDB:6W63's crystal structure  42  (Fig. S1 † ). The ability of each program to identify actives was then assessed by the resulting top 1% enrichment factor (EF) values featured on Fig. 1. Notably, these values range from 1. 4 to 10. 4 for individual docking programs, illustrating their remarkable variability in retrospective performances on SARS-CoV-2 Mpro.
In parallel, a highly detailed pharmacophore model was constructed by an expert using the binding pose of X77 compound from the PDB:6W63 structure  42  of Mpro, and from the structure of Jun8-76-3A molecule found in PDB:7KX5. 44 This expert-designed pharmacophore model involved aromatic/ hydrophobic (F1) and hydrogen donor (F2) features respectively placed at P1 position, one aromatic/hydrophobic feature (F3) at P2, an aromatic/hydrophobic linker (F4) between P1 and P2, an aromatic/hydrophobic feature (F5) at P1 0 and a hydrogen bond acceptor feature (F6) at P3, as shown on Fig. 2c. Unlike the pharmacophore model generated by MOE, the design of this model was fundamentally expert-driven and did not rely on automatic detection of binding features from available crystallographic data.
Deep Docking of 40 billion molecules To carry out the ultra-large DD campaign, we generated a combined library of purchasable chemicals by merging ZINC15  30  with an enumerated Enamine REAL Space library,  29  resulting in 40 061 717 551 unique molecular entries. The conventional docking of a library of this size would require extraordinary resources, and up to 10 years of running time with the fastest Autodock GPU program implemented on 250 GPU units. Instead, we utilized the DD method to screen the library. In its essence, DD is a classication model that is trained with the chemical ngerprints and docking scores of a small sample of a chemical library, and used to predict the top scoring molecules (virtual hits) while discarding unfavorable molecules without requiring to dock the entire database.
Nevertheless, this yielded 10-fold reduction of a docking database by Autodock GPU with a remaining $4 billion molecules predicted as probable virtual hits. The same procedure was then repeated with Glide SP-DD using the 4 billion remaining molecules as the starting point, followed by FRED, ICM, and the slowest QuickVina2, until the total number of predicted virtual hits was reduced to $24 million molecules (Table  1 ). The consecutive DD runs with the ve programs took us approximately nineteen days of computing on 250 GPUs and 640 CPU cores, and resulted in a signicant 1500-fold reduction of the initial 40 billion docking library. It should be noted that the extend of reduction of the docking database is expected to depend on the order of the application of the docking programs. Therefore, we prioritize the high-throughput capability of each docking algorithm to achieve faster library reduction.
1 (resulted in a hitlist 1. 1_M) and 3 of 5 rule for path 1. 2 (hitlist 1. 2_M). The resulting lists on 1. 1 and 1. 2 paths (corresponding to most consistently docked and ranked compounds) were further ltered with the above-described pharmacophore model (resulting in hitlists 1. 1_N and 1. 2_N respectively), followed by best-rst structural  4  (O) and score tree-based (P) clustering procedures that aimed to increase diversity in the resulting hitlists. As Fig. 3  illustrates, the implemented less aggressive sequential ltering (path 1. 2) resulted in 14 000-fold reduction of the initial 24 million dataset, while a stricter 1. 1 ltering path led to 680 000-fold database reduction and yielded only 179 molecules that justied all consensus prediction criteria. Of note, the implemented ltering procedures were all highly automated and enabled handling raw docking results with minimal human intervention.
Chemical diversity ltering of docking results It is well known that combinatorial databases like REAL Space tend to contain redundant chemotypes, and therefore could benet from structure-based clustering to ensure chemical diversity. 4 For that reason, we implemented selection strategy 2. 0 (Fig. 3 ), where we began ltering of 24 million hits by selecting 100 000 molecules that were top-scored by each docking program (due to technical limitations of clustering algorithms), and grouped them based on structural similarity, selecting the top-ranked member as cluster representative and discarding the others 4 (see Methods section for details), resulting in hitlists marked as F, G, H, I and J on Fig. 3. Unsurprisingly, the generated hitlists were signicantly more diverse than the ones obtained from raw docking results, as illustrated by the comparison of the distribution of Tanimoto similarity scores (computed on 1024 bits, radius 2 Morgan ngerprints) for the top-100 ranked molecules prior and aer clustering, reported in Fig.
6c ). Importantly, none of the constructed hitlists returned a null hit rate, with the lowest observed value being 3% for hitlist C corresponding to unprocessed docking outcomes from FRED while the highest hit rate of 11% for a single program was achieved by Autodock GPU. The consequently applied consensus docking and scoring procedures, as well as automated pharmacophore ltering procedures did not signicantly improved hit returns (1. 0_K to 1. 2_P). Nevertheless, we observed that introducing best-rst clustering based on chemical ngerprints, as initially proposed by Lyu et al. 4  did consistently improve hit rates for individual docking program outcomes (F to J) as well as for consensus-based strategies (2. 0_K to 2. 2_N), resulting in hit rates as high as 13%.
Docking grids The structure of Mpro with X77 noncovalent ligand bound in the active site (PDB:6W63)  42  was optimized using the Protein Preparation Wizard 58 of Maestro suite from Schrödinger 59 with default parameters. Autodock maps for all supported atom types were generated with autogrid4, 60 using a box of 18 Å Â 19. 50 Å Â 21. 75 Å centered on X77, and a grid spacing value of 0. 375 Å. The Glide docking grid was prepared using the Receptor Grid Generation tool from Schrödinger 59 dening a 10 Å box centered on the ligand. For ICM docking, the grid was prepared using the Receptor Setup tool 61 on the ligand-receptor complex. The FRED grid was prepared using the Make Receptor tool. 62 The box shape was dened around the ligand with dimensions of 16 Å Â 20 Å Â 21 Å. QuickVina2 box's dimension and coordinates were set to the same values as for Autodock.
Molecular docking QUACPAC 62 was used to generate a dominant ionized, tautomer form at pH 7. 4 for each compound to dock. Omega's pose mode  63  was used to generate multiple 3D conformations for FRED docking. Omega's classic mode was used to generate single low energy conformations as inputs for the other programs. Openbabel 64 was used to translate compounds from sdf to pdbqt format for Autodock and QuickVina2 docking. Docking was performed with ve programs, using default parameters unless differently specied: Autodock GPU (https:// github. com/scottlegrand/Autodock-GPU/tree/relicensing version),  24  with 5 million energy evaluations and 10 Lamarckian genetic algorithm  65  runs for each compound, considering the lowest energy pose from the most populated cluster as the best solution; Glide SP module; 25 FRED; 26 ICM 27 with an effort value of 2; and QuickVina2.
28 nchmarking 214 Mpro ligands with available crystal structures were collected from the Protein Data Bank (RCSB PDB)  66  and the Fragalysis database. 14, 35 50 decoys per known active ligand were generated using the DUD-E server. 41 Compounds were docked with the ve programs discussed above. EF 67 at 1% were calculated as EF ¼.
Pharmacophore modeling Structures of 54 covalent and noncovalent Mpro active site binders were downloaded from RCSB PDB's COVID-19 Resources collection. In addition, 295 crystals of Mpro complexed with ligands were obtained from the Fragalysis database  14, 35  and ltered to retain only noncovalent active site inhibitors with reported IC 50 below or equal to 5 mM. The protein sequences were aligned and superimposed on the pocket residues of the reference compound X77 in MOE. 68 harmacophore modeling was performed using the MOE Ph4 Query tool with the application of Extended Hückel Theory annotation scheme. 69 Consensus pharmacophore features were automatically computed using a default tolerance distance of 1. 2 Å from the ligands' atoms and a 50% threshold of overlapping features. Features were then selected based on visual inspection of known actives in complex with Mpro.
Library preparation At the time of this research, the REAL Space library consisted of 12. 3 billion compounds in SMILES format. Stereochemical expansion of the database was performed using ipper  63  to enumerate unspecied stereocenters, resulting in 38. 7 billion unique molecules. Morgan ngerprints (considering chirality) of 1024 bits and radius 2 were calculated using rdkit chemoinformatic package. 70 The resulting library was then merged with the previously prepared 19 ZINC15 set (1. 36 billion compounds). 30 ep Docking DD was run using a recall of 0. 9 for virtual hits. The sizes of validation, test and training (both initial and augmentation batches) sets were set equal to 0. 07% of the starting database, as previously described. 19 For all the runs, virtual hits were dened as top 1% ranked molecules of the library in the rst iteration, 0.
Acknowledgements This work was funded by  Canadian Institutes of Health Research (CIHR) ,  Canadian 2019 Novel Coronavirus  (2019-nCoV) Rapid Research grants ( OV3-170631  and  VR3-172639 ), and generous donations for COVID-19 research from  TELUS ,  Teck Resources , 625  Powell Street Foundation ,  Tai Hung Fai Charitable Foundation ,  Vancouver General Hospital Foundation. F. G. 's work is supported by fellowships from the  CIHR  ( MFE-171324 ),  Michael Smith Foundation for Health Research/ VCHRI & VGH UBC Hospital Foundation  ( RT-2020-0408 ), and  Ermenegildo Zegna Foundation. The authors thank the  Dell Technologies HPC and AI Innovation Lab  for their support and partnership in providing the HPC platform (PowerEdge servers).
In COMSOL Multiphysics, the piezoelectricity module and electrical circuit module were coupled with each other by applying proper terminal conditions. Specifically, a capacitor was connected in parallel to the composite, so as to collect and accumulate the piezoelectric charges in response to compressive stress. This method allowed us to easily estimate the effective piezoelectric constant (d33) along the vertical, as illustrated in Figure  17a. With the abovementioned approach described in the modeling (Section 3. 2, Figure  10 ), 25 × 25 periodic unit cells embedded in the finite air were chosen to avoid the edge effects. It should be noted that, in a linear regime (i. e. , subjected to moderate mechanical stress), the effective piezoelectric coefficient of the composite does not depend on the input compression, but rather on the intrinsic properties, such as the designed structure and material properties.
Therefore, to estimate the d 33 of a single ZnO nanowire, FEM under an arbitrary compression was performed to describe the effective piezoelectric response of the composite. In COMSOL Multiphysics, the piezoelectricity module and electrical circuit module were coupled with each other by applying proper terminal conditions. Specifically, a capacitor was connected in parallel to the composite, so as to collect and accumulate the piezoelectric charges in response to compressive stress. This method allowed us to easily estimate the effective piezoelectric constant (d 33 ) along the vertical, as illustrated in Figure  17a. With the abovementioned approach described in the modeling (Section 3. 2, Figure  10 ), 25 × 25 periodic unit cells embedded in the finite air were chosen to avoid the edge effects.
This space allows a visually interpretable insight into the dynamics of complexes, by calculating 3D averages of subtomograms with similar conformations from selected (densest) regions and by recording movies of the 3D reference's displacement along selected trajectories in the densest regions. In this article, we describe HEMNMA-3D and show its validation using synthetic datasets. Additionally, we show an application of HEMNMA-3D with an experimentally obtained dataset for in situ nucleosome conformational variability. HEMNMA-3D software is available freely (open-source) as part of ContinuousFlex plugin of Scipion V3. 0 (http://scipion. i2pc. es). The article is organized as follows: Section 2 describes building blocks of HEMNMA-3D workflow, notably, in 2. 5 we describe the software developed for this method.
(4) Visualization of the computed conformations, after projecting the conformational parameters obtained for all subtomograms onto a low-dimensional space. In the remaining part of this section, we describe these steps in more detail. Please note that the first two steps of the workflow are exactly as those of HEMNMA and were thoroughly presented, tested and discussed in our previously published works on HEMNMA, its tools and applications  (Harastani et al. (2020) ;  Jin et al. (2014) ;  Jonić and Sorzano (2016) ;  Nogales-Cadenas et al. (2013) ;  Sorzano et al. (2014) ). However, for completeness of the present article, we here recall their basic principles. We close this section by a brief description of the software implemented for HEMNMA-3D.
Software implementation and technical details The software of HEMNMA-3D method proposed here is freely available (open-source). It is a part of ContinuousFlex plugin for the open-source software Scipion3 De la  Rosa-Trevín et al. (2016). ContinuousFlex was introduced in  Harastani et al. (2020)  and also contains HEMNMA software. The software provides a graphical user interface (GUI) and is empowered with a C++ backend with a message passing interface (MPI) parallelization scheme to efficiently analyze large datasets (simultaneous analysis of N subtomograms using N computing threads). We tested the software on our local workstations and on supercomputer centres. On our local workstations (2. 2GHz Intel Xeon Silver 4214 CPU processors), the current implementation takes around 10 and 30 minutes to analyze a subtomogram of size 64 3 voxels with three and six normal modes, respectively.
These times are reported for the two types of complexes used in this article, together with the number of normal modes used in these two cases. They are the average times required for all iterations of analyzing one subtomogram using a single computing thread while the different subtomograms are analyzed in parallel using different computing threads (if the time is measured for the entire dataset, it will vary with the size of the dataset). It should be noted that the software allows the use of any number of MPI threads and any number of normal modes. However, the more modes are used, the slower the processing. Finally, it should be noted that there is no constraint regarding the size of the dataset (the number of subtomograms) or the size of the individual subtomograms (the number of voxels) that can be analyzed with our software.
Synthesizing datasets for testing the method performance For testing HEMNMA-3D in general, and the combined elastic and rigid-body 3D-to-3D alignment module in particular (which is the core module of the proposed method), we synthesized two datasets of conformationally heterogeneous subtomograms that mimic discrete and continuous conformational variability, called "Discrete" and "Continuous" datasets respectively. The flowchart for the data generation procedure is shown in Figure  4  and is detailed in the following. The "Discrete" dataset comprises 900 synthetic subtomograms representing three different (synthetic) conformations of the atomic PDB:4AKE structure  (Müller et al. (1996) ) of adenylate kinase chain A (1656 atoms), i. e. 300 subtomograms per conformation. We generated this dataset using the atomic PDB:4AKE structure and its first two non-rigid-body normal modes, i.
e. modes 7 and 8. Precisely, the three conformations are represented by the following amplitudes of modes 7 and 8: (mode 7, mode 8) ∈ {(-150, 0), (+150, 0), (0, +150)}. The "Continuous" dataset comprises 1000 synthetic subtomograms representing a continuum of conformations of the same PDB:4AKE structure. We generated this dataset using this atomic structure and its modes 7 and 8 using a linear relationship between the amplitudes of the two modes. More precisely, the synthesized amplitudes of modes 7 and 8 were identical and randomly distributed in the range  [-200, +200]  (uniform distribution). Normal-mode amplitudes do not have a physical unit. Nonetheless, the Root Mean Square Deviation (RMSD)  Kufareva and Abagyan (2011)  between the reference atomic coordinates and these coordinates displaced using normal-mode amplitudes transforms the normal mode amplitudes in physical units.
To provide a basis for further evaluation of the method performance, we found a RMSD of 6. 95 Å corresponding to the displacement using the amplitude of 200 for each of the two combined modes 7 and 8 (this represents one half of the full range of the synthesized motion). To generate a subtomogram, first, we deform the atomic structure using appropriate amplitudes for the selected normal modes depending on the dataset in hand, i. e. we use (mode 7, mode 8) = (+150, 0) or (-150, 0) or (0, +150) to create a subtomogram in the "Discrete" dataset, while we assign a random value in the range  [-200, 200]  for both mode 7 and mode 8 to generate a subtomogram in the "Continuous" dataset. Then, we convert the deformed structure to a volume of size 64 3 voxels and the voxel size of 2. 2 Å3  (Peng et al.
Synthetic discrete-type conformational variability In this experiment, our goal is to retrieve the ground-truth amplitudes of normal modes 7 and 8 by the combined elastic and rigid-body alignment (the core module of HEMNMA-3D) of a reference model with the subtomograms in the "Discrete" dataset. In other words, the goal is to find a solution for the challenging inverse problem of finding the conformation of the structure in each subtomogram. Since the proposed method can use two choices for the reference model, namely, an atomic structure and a density map (e. g. , an EM map or a subtomogram average), we performed two types of tests. In the first test type, the atomic structure used to generate the synthetic subtomograms (chain A of the PDB:4AKE) was used as a reference for retrieving normal mode amplitudes of the synthetic subtomograms.
Synthetic continuous-type conformational variability Similarly to the previous experiment, our goal in this experiment is to find a solution for the inverse problem of finding the conformation of the structure in each subtomogram using the combined elastic and rigid-body alignment of a reference model with the subtomograms in the "Continuous" dataset. We used the same two reference models as in the previous experiment to estimate the normal-mode amplitudes: an atomic structure (chain A of PDB:4AKE) and a density map from this atomic structure. Also, as in the previous experiment, we used three modes for both tests (atomic or pseudoatomic modes 7, 8 and 9). Figure  8  presents the estimated amplitudes of modes 7 and 8 (the estimated amplitude of mode 9 is close to 0 and is not shown in the plots).