cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.12805811
title: CovDocker

description: The preprocessed CovDocker dataset for paper "CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions" with associated code at https://github.com/PoloWitty/CovDocker.

The dataset files are saved as lmdb file for the convenience of use.

File structure:

processed
    ├── bonded
    │   ├── 1A0L
    │   │     ├── 1A0L_10Apocket.pdb 
    │   │     ├── 1A0L_5Apocket.pdb
    │   │     ├── 1A0L_8Apocket.pdb
    │   │     ├── 1A0L_chain_within_10A.pdb
    │   │     ├── 1A0L_ligand.pdb # ligand part from original complex pdb file
    │   │     ├── 1A0L_ligand.sdf # ligand pdb structure aligned with coresponding SMILES
    │   │     └── 1A0L_protein.pdb # protein part from original complex pdb file
    .....
    │   └── 9XIA
    ├── dataset # lmdb files used for deep learning model
    │   ├── docking 
    │   ├── reaction
    │   └── reactive_site
    ├── dataset.csv # used for task2 and task3
    └── dataset.filtered.csv # used for task1

Paper Abstract:



Molecular docking is a widely used computational tool to predict the binding mode of a ligand to a target protein. Covalent interactions, which involve the formation of a covalent bond between the ligand and the target, have gained significant importance due to their strong and durable binding. However, most traditional docking methods and existing deep learning approaches hardly account for the formation of covalent bonds and the resultant structural changes.

In this paper, we introduce a comprehensive benchmark for covalent docking. We decompose the covalent docking process into three main tasks: reactive site prediction, covalent reaction prediction, and covalent docking (cov-docking). By adapting state-of-the-art models such as Uni-Mol and Chemformer, we establish baseline performances, demonstrating the benchmark's efficacy in accurately predicting interaction sites and modeling the molecular changes involved in covalent binding.

These initial findings provide a foundation for further research, facilitating the development of advanced computational methods to expedite the discovery of covalent drugs. Positioned as a valuable resource for the scientific community, this benchmark serves as a catalyst for innovation in covalent drug design methodologies. By addressing the unique challenges of covalent docking through tailored tasks and datasets, our work paves the way for more accurate and efficient computational techniques, ultimately contributing to the acceleration of covalent inhibitor discovery and development. Our code is available at https://github.com/PoloWitty/CovDocker.
DOI: 10.5281/zenodo.14738851
title: CovDocker

description: The preprocessed CovDocker dataset for paper "CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions" with associated code at https://github.com/PoloWitty/CovDocker.

The dataset files are saved as lmdb file for the convenience of use.

File structure:



processed

    ├── bonded

    │   ├── 1A0L

    │   │     ├── 1A0L_10Apocket.pdb

    │   │     ├── 1A0L_5Apocket.pdb

    │   │     ├── 1A0L_8Apocket.pdb

    │   │     ├── 1A0L_chain_within_10A.pdb

    │   │     ├── 1A0L_ligand.pdb # ligand part from original complex pdb file

    │   │     ├── 1A0L_ligand.sdf # ligand pdb structure aligned with coresponding SMILES

    │   │     └── 1A0L_protein.pdb # protein part from original complex pdb file

    .....

    │   └── 9XIA

    ├── dataset # lmdb files used for deep learning model

    │   ├── docking

    │   ├── reaction

    │   └── reactive_site

    ├── dataset.csv # used for task2 and task3 (n=2754)

    ├── dataset.filtered.csv # used for task1 (n=2717)

    ├── dataset.filtered.unseen.csv # used for task1 unseen test set (67 unseen test samples)

    ├── dataset.unseen.csv # used for task2 and task3 unseen test set (68 unseen test samples)

    └── pdb2mechanism.csv # (n=2754)


Paper Abstract:



Molecular docking is a widely used computational tool to predict the binding mode of a ligand to a target protein. Covalent interactions, which involve the formation of a covalent bond between the ligand and the target, have gained significant importance due to their strong and durable binding. However, most traditional docking methods and existing deep learning approaches hardly account for the formation of covalent bonds and the resultant structural changes.

In this paper, we introduce a comprehensive benchmark for covalent docking. We decompose the covalent docking process into three main tasks: reactive site prediction, covalent reaction prediction, and covalent docking (cov-docking). By adapting state-of-the-art models such as Uni-Mol and Chemformer, we establish baseline performances, demonstrating the benchmark's efficacy in accurately predicting interaction sites and modeling the molecular changes involved in covalent binding.

These initial findings provide a foundation for further research, facilitating the development of advanced computational methods to expedite the discovery of covalent drugs. Positioned as a valuable resource for the scientific community, this benchmark serves as a catalyst for innovation in covalent drug design methodologies. By addressing the unique challenges of covalent docking through tailored tasks and datasets, our work paves the way for more accurate and efficient computational techniques, ultimately contributing to the acceleration of covalent inhibitor discovery and development. Our code is available at https://github.com/PoloWitty/CovDocker.
DOI: 10.5281/zenodo.15524346
title: CovDocker

description: The preprocessed CovDocker dataset for paper "CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions" with associated code at https://github.com/PoloWitty/CovDocker.

The dataset files are saved as lmdb file for the convenience of use.

File structure:



processed

    ├── bonded

    │   ├── 1A0L

    │   │     ├── 1A0L_10Apocket.pdb

    │   │     ├── 1A0L_5Apocket.pdb

    │   │     ├── 1A0L_8Apocket.pdb

    │   │     ├── 1A0L_chain_within_10A.pdb

    │   │     ├── 1A0L_ligand.pdb # ligand part from original complex pdb file

    │   │     ├── 1A0L_ligand.sdf # ligand pdb structure aligned with coresponding SMILES

    │   │     └── 1A0L_protein.pdb # protein part from original complex pdb file

    .....

    │   └── 9XIA

    ├── dataset # lmdb files used for deep learning model

    │   ├── docking

    │   ├── reaction

    │   └── reactive_site

    ├── dataset.csv # used for task2 and task3 (n=2754)

    ├── dataset.filtered.csv # used for task1 (n=2717)

    ├── dataset.filtered.unseen.csv # used for task1 unseen test set (67 unseen test samples)

    ├── dataset.unseen.csv # used for task2 and task3 unseen test set (68 unseen test samples)

    └── pdb2mechanism.csv # (n=2754)


Paper Abstract:



Molecular docking plays a crucial role in predicting the binding mode of ligands to target proteins, and covalent interactions, which involve the formation of a covalent bond between the ligand and the target, are particularly valuable due to their strong, enduring binding nature. However, most existing docking methods and deep learning approaches hardly account for the formation of covalent bonds and the associated structural changes. To address this gap, we introduce a comprehensive benchmark for covalent docking, CovDocker, which is designed to better capture the complexities of covalent binding. We decompose the covalent docking process into three main tasks: reactive location prediction, covalent reaction prediction, and covalent docking. By adapting state-of-the-art models, such as Uni-Mol and Chemformer, we establish baseline performances and demonstrate the effectiveness of the benchmark in accurately predicting interaction sites and modeling the molecular transformations involved in covalent binding. These results confirm the role of the benchmark as a rigorous framework for advancing research in covalent drug design. It underscores the potential of data-driven approaches to accelerate the discovery of selective covalent inhibitors and addresses critical challenges in therapeutic development. Our code is available at https://github.com/PoloWitty/CovDocker.
DOI: 10.15468/dl.yuaguu
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ceratozamia huastecorum Avendaño, Vovides & Cast.-Campos"
 ]
}

 The dataset includes 15 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002916-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kra263
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Fossil",
 "OccurrenceStatus is Present",
 "TaxonKey is Phellodendron Rupr."
 ]
}

 The dataset includes 29 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002915-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w6ck9b
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Herbarium specimens, Harmas J.H. Fabre (FABR - Sérignan-du-Comtat) of the Muséum national d'Histoire naturelle (MNHN - Paris)",
 "Country is Croatia"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002919-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ckrqrq
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is University of Cincinnati, Margaret H. Fulford Herbarium - Vascular Plants",
 "Country is Croatia"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002914-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15412891
title: LanguageStructure/Xavante-dicionario: Dicionário e aplicativo web

description: Esta versão acompanha uma página HTML e um script (JS) para ser usado no navegador.
DOI: 10.5525/gla.researchdata.1925
title: Towards decentralized nitrogen fixation using pulsed ultrasound
DOI: 10.71486/km66-0659
title: Dataset of the work “Multiamperometric-SERS detection of melamine on gold screen-printed electrodes”

description: A new, simple and fast protocol to generate gold-based SERS substrates is presented in this work. Melamine is a compound widely used in the industry that can be toxic for humans if consumed even in low concentrations. EC-SERS is an excellent alternative to classical methods to detect and quantify this compound because Raman spectroscopy provides a fingerprint of the molecules, providing very good sensitivity. In this work, time-resolved Raman spectroelectrochemistry is employed to generate a SERS substrate and to detect melamine, all in a single experiment. The dynamic character of this technique provides valuable information about the interaction of the molecule with the generated substrate. An optimization of the spectroelectrochemical method based on a multi-pulse amperometric detection has been performed. The new protocol presented in this work shows significant figures of merit in a very short experiment time, just 25 s.
DOI: 10.15468/dl.dws2mk
title: Occurrence Download

description: A dataset containing 9879 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tremarctos ornatus (F.G.Cuvier, 1825)"
 ]
}

 The dataset includes 9879 records from 117 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002920-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.anud62
title: Occurrence Download

description: A dataset containing 354 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Abies guatemalensis Rehd."
 ]
}

 The dataset includes 354 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002921-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57724/jci_20241229_dinglac_mbev
title: DINGLAC: Mass balance, extent and velocity – Hurd & Johnsons glaciers 2024-2025
DOI: 10.25919/4em8-8s72
title: Canola establishment data 2024. Geraldton core research site CSP2212-005RTX

description: Data is the result of field experiment(s) conducted in 2024 to examine factors affecting canola establishment.
DOI: 10.18150/359jlb
title: Krauze M., Bączyk M., Drzymała-Celichowska H., Krutki P. Ia excitatory postsynaptic potentials are potentiated in slow-type motoneurons after a 5-week treadmill endurance training in rats

description: The dataset includes data from the electrophysiological study on rat spinal motoneurons. The aim of the study was to investigate whether endurance training evoked adaptive changes in Ia afferent synaptic transmission from muscle spindles to motoneurons.Files contain: rat body and muscle mass of the medial gastrocnemius, lateral gastrocnemius and soleus muscles, and motoneuron passive membrane properties and characteristics of monosynaptic EPSPs (from homonymous and heteronymous synergistic muscles) recorded intracellularly.This study was supported by the Polish National Science Centre (Grant No. 2022/45/B/NZ7/00102).
DOI: 10.15468/dl.66ny7d
title: Occurrence Download

description: A dataset containing 57 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Davidsea Soderstr. & R.P.Ellis"
 ]
}

 The dataset includes 57 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0303114-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x2uqja
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Myioborus chrysops Salvin, 1878)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002860-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uuyuv9
title: Occurrence Download

description: A dataset containing 191979 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lybius torquatus (Dumont, 1805), Lanius torquatus Latham, 1801, Labrus torquatus Walbaum, 1792)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 191979 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0298161-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kbuwda
title: Occurrence Download

description: A dataset containing 371433 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((-106.06678 26.81696,-106.55674 25.94591,-106.57489 25.5739,-106.81079 25.58298,-106.98319 25.65556,-107.13744 25.51946,-107.15558 25.36521,-107.11022 25.18375,-107.02856 24.97506,-106.76543 24.73915,-106.54767 24.50324,-106.55674 24.37621,-106.49323 24.26733,-106.24824 24.35807,-106.01234 24.18567,-105.93067 23.97698,-105.89438 23.55053,-105.73106 23.48702,-105.57681 23.13316,-105.43164 23.14223,-105.30461 22.97891,-104.96889 22.96076,-104.92353 22.743,-105.03241 22.66134,-104.92353 22.48895,-104.61503 22.45265,-104.3519 22.35285,-104.13414 22.83374,-104.01619 23.44165,-103.84379 24.0405,-103.3992 24.39436,-102.86387 24.7573,-103.44456 25.24726,-103.38105 25.62834,-103.24495 26.2907,-103.50808 26.64456,-104.17951 26.72622,-104.53337 26.38144,-105.3409 26.54476,-106.06678 26.81696))"
 ]
}

 The dataset includes 371433 records from 596 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0303000-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.npwdma
title: Occurrence Download

description: A dataset containing 1528 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phylloscopus trochiloides (Sundevall, 1837)"
 ]
}

 The dataset includes 1528 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069964-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n8x6pn
title: Occurrence Download

description: A dataset containing 105865 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Callocephalon fimbriatum (J.Grant, 1803))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 105865 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002866-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gyjewv
title: Occurrence Download

description: A dataset containing 183 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Magnolia denudata Desr.",
 "Year is greater than or equal to 1981",
 "HasCoordinate is true"
 ]
}

 The dataset includes 183 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0303125-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wucku4
title: Occurrence Download

description: A dataset containing 6555 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Timalia pileata Horsfield, 1821)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6555 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0298196-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.seq7g8
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Adonis tianschanica (Adolf) Lipsch. ex Bobrov"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001723-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d5tdmu
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Stipa saxicola Hitchc."
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0145149-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f3929j
title: Occurrence Download

description: A dataset containing 182 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Synallaxis zimmeri Koepcke, 1957)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 182 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0298197-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fvx7mb
title: Occurrence Download

description: A dataset containing 80 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Decaryochloa A.Camus"
 ]
}

 The dataset includes 80 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0303119-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xzmz6u
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aeschynanthus poilanei Pellegr."
 ]
}

 The dataset includes 19 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002878-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ufghca
title: Occurrence Download

description: A dataset containing 5448 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Discosura popelairii",
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "Year 2011-2021",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 5448 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069977-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.my5w7u
title: Occurrence Download

description: A dataset containing 281 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Synallaxis hypochondriaca (Salvin, 1895))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 281 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0298193-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vwjzyn
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Didymogonyx (L.G.Clark & Londoño) C.D.Tyrrell, L.G.Clark & Londoño"
 ]
}

 The dataset includes 108 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0303140-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.