Deep Learning-Ready Voxel Representation of Protein-Ligand Complexes from an Enhanced PBDbind v.2020 Dataset Article Swipe
Matheus Müller Pereira da Silva
,
Isabella Alvim Guedes
,
Fábio Lima Custódio
,
Laurent E. Dardenne
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2023-f4q6k
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2023-f4q6k
A critical aspect of successful deep learning (DL) modelling in computer-aided drug discovery (CADD) is the representation of biomolecular data. Voxel grid representations have emerged as a straightforward method for depicting 3D molecular structures of protein-ligand complexes. Proper structural preparation of these complexes is also crucial, particularly in models where the orientation of hydrogen atoms and the accurate assignment of protonation/tautomeric states are vital. The PDBbind, a widely used dataset, can be improved in this regard. This work presents an enhanced version of the PDBbind v.2020 refined set concerning structural preparation, a voxel representation of these structures suitable for DL model training and a diverse set of docking-generated poses that could be used to develop new scoring functions for pose prediction. We also introduce DockTGrid, a software library developed to generate these voxel representations, which can be adapted to create new molecular features. With this work, we aim to provide the CADD community with high-quality, accessible resources to facilitate the development of DL models for drug discovery.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2023-f4q6k
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/657346e75bc9fcb5c9638a29/original/deep-learning-ready-voxel-representation-of-protein-ligand-complexes-from-an-enhanced-pb-dbind-v-2020-dataset.pdf
- OA Status
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- Cited By
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- References
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- OpenAlex ID
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https://openalex.org/W4389564930Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv-2023-f4q6kDigital Object Identifier
- Title
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Deep Learning-Ready Voxel Representation of Protein-Ligand Complexes from an Enhanced PBDbind v.2020 DatasetWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-11Full publication date if available
- Authors
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Matheus Müller Pereira da Silva, Isabella Alvim Guedes, Fábio Lima Custódio, Laurent E. DardenneList of authors in order
- Landing page
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https://doi.org/10.26434/chemrxiv-2023-f4q6kPublisher landing page
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/657346e75bc9fcb5c9638a29/original/deep-learning-ready-voxel-representation-of-protein-ligand-complexes-from-an-enhanced-pb-dbind-v-2020-dataset.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/657346e75bc9fcb5c9638a29/original/deep-learning-ready-voxel-representation-of-protein-ligand-complexes-from-an-enhanced-pb-dbind-v-2020-dataset.pdfDirect OA link when available
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Computer science, Voxel, Representation (politics), Artificial intelligence, Set (abstract data type), Deep learning, Drug discovery, Software, Bioinformatics, Programming language, Biology, Politics, Political science, LawTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in | 9, 47, 73 |
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| abstract_inverted_index.of | 3, 17, 34, 40, 52, 59, 82, 94, 106, 161 |
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| abstract_inverted_index.we | 146 |
| abstract_inverted_index.The | 64 |
| abstract_inverted_index.aim | 147 |
| abstract_inverted_index.and | 55, 102 |
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| abstract_inverted_index.can | 70, 135 |
| abstract_inverted_index.for | 29, 98, 118, 164 |
| abstract_inverted_index.new | 115, 140 |
| abstract_inverted_index.set | 87, 105 |
| abstract_inverted_index.the | 15, 50, 56, 83, 150, 159 |
| abstract_inverted_index.(DL) | 7 |
| abstract_inverted_index.CADD | 151 |
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| abstract_inverted_index.also | 44, 122 |
| abstract_inverted_index.deep | 5 |
| abstract_inverted_index.drug | 11, 165 |
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| abstract_inverted_index.this | 74, 144 |
| abstract_inverted_index.used | 68, 112 |
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| abstract_inverted_index.data. | 19 |
| abstract_inverted_index.model | 100 |
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| abstract_inverted_index.voxel | 92, 132 |
| abstract_inverted_index.where | 49 |
| abstract_inverted_index.which | 134 |
| abstract_inverted_index.work, | 145 |
| abstract_inverted_index.(CADD) | 13 |
| abstract_inverted_index.Proper | 37 |
| abstract_inverted_index.aspect | 2 |
| abstract_inverted_index.create | 139 |
| abstract_inverted_index.method | 28 |
| abstract_inverted_index.models | 48, 163 |
| abstract_inverted_index.states | 61 |
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| abstract_inverted_index.vital. | 63 |
| abstract_inverted_index.widely | 67 |
| abstract_inverted_index.PDBbind | 84 |
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| abstract_inverted_index.refined | 86 |
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| abstract_inverted_index.version | 81 |
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| abstract_inverted_index.critical | 1 |
| abstract_inverted_index.crucial, | 45 |
| abstract_inverted_index.dataset, | 69 |
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| abstract_inverted_index.hydrogen | 53 |
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| abstract_inverted_index.suitable | 97 |
| abstract_inverted_index.training | 101 |
| abstract_inverted_index.community | 152 |
| abstract_inverted_index.complexes | 42 |
| abstract_inverted_index.depicting | 30 |
| abstract_inverted_index.developed | 128 |
| abstract_inverted_index.discovery | 12 |
| abstract_inverted_index.features. | 142 |
| abstract_inverted_index.functions | 117 |
| abstract_inverted_index.introduce | 123 |
| abstract_inverted_index.modelling | 8 |
| abstract_inverted_index.molecular | 32, 141 |
| abstract_inverted_index.resources | 156 |
| abstract_inverted_index.DockTGrid, | 124 |
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| abstract_inverted_index.facilitate | 158 |
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| abstract_inverted_index.structures | 33, 96 |
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| abstract_inverted_index.straightforward | 27 |
| abstract_inverted_index.representations, | 133 |
| abstract_inverted_index.docking-generated | 107 |
| abstract_inverted_index.protonation/tautomeric | 60 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.61683212 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |