Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design
Artificial intelligence (AI) based drug design has demonstrated great potential to fundamentally change the pharmaceutical industries. Currently, a key issue in AI-based drug design is efficient transferable molecular descriptors or fingerprints. Here, we present hypergraph-based molecular topologic...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/160382 |
| _version_ | 1811695397910872064 |
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| author | Liu, Xiang Wang, Xiangjun Wu, Jie Xia, Kelin |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Liu, Xiang Wang, Xiangjun Wu, Jie Xia, Kelin |
| author_sort | Liu, Xiang |
| collection | NTU |
| description | Artificial intelligence (AI) based drug design has demonstrated great potential to fundamentally change the pharmaceutical industries. Currently, a key issue in AI-based drug design is efficient transferable molecular descriptors or fingerprints. Here, we present hypergraph-based molecular topological representation, hypergraph-based (weighted) persistent cohomology (HPC/HWPC) and HPC/HWPC-based molecular fingerprints for machine learning models in drug design. Molecular structures and their atomic interactions are highly complicated and pose great challenges for efficient mathematical representations. We develop the first hypergraph-based topological framework to characterize detailed molecular structures and interactions at atomic level. Inspired by the elegant path complex model, hypergraph-based embedded homology and persistent homology have been proposed recently. Based on them, we construct HPC/HWPC, and use them to generate molecular descriptors for learning models in protein-ligand binding affinity prediction, one of the key step in drug design. Our models are tested on three most commonly-used databases, including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016, and outperform all existing machine learning models with traditional molecular descriptors. Our HPC/HWPC models have demonstrated great potential in AI-based drug design. |
| first_indexed | 2024-10-01T07:22:50Z |
| format | Journal Article |
| id | ntu-10356/160382 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:22:50Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1603822022-07-20T06:37:20Z Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design Liu, Xiang Wang, Xiangjun Wu, Jie Xia, Kelin School of Physical and Mathematical Sciences Science::Mathematics Molecular Descriptor Machine Learning Artificial intelligence (AI) based drug design has demonstrated great potential to fundamentally change the pharmaceutical industries. Currently, a key issue in AI-based drug design is efficient transferable molecular descriptors or fingerprints. Here, we present hypergraph-based molecular topological representation, hypergraph-based (weighted) persistent cohomology (HPC/HWPC) and HPC/HWPC-based molecular fingerprints for machine learning models in drug design. Molecular structures and their atomic interactions are highly complicated and pose great challenges for efficient mathematical representations. We develop the first hypergraph-based topological framework to characterize detailed molecular structures and interactions at atomic level. Inspired by the elegant path complex model, hypergraph-based embedded homology and persistent homology have been proposed recently. Based on them, we construct HPC/HWPC, and use them to generate molecular descriptors for learning models in protein-ligand binding affinity prediction, one of the key step in drug design. Our models are tested on three most commonly-used databases, including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016, and outperform all existing machine learning models with traditional molecular descriptors. Our HPC/HWPC models have demonstrated great potential in AI-based drug design. Ministry of Education (MOE) Nanyang Technological University This work was supported in part by Nanyang Technological University Startup Grant M4081842 and Singapore Ministry of Education Academic Research fund Tier 1 RG31/18 and RG109/19, Tier 2 MOE2018-T2-1-033. The second author was supported by Natural Science Foundation of China (NSFC grant no. 11871284). The third author was supported by Natural Science Foundation of China (NSFC grant no. 11971144) and High-level Scientific Research Foundation of Hebei Province. 2022-07-20T06:37:20Z 2022-07-20T06:37:20Z 2021 Journal Article Liu, X., Wang, X., Wu, J. & Xia, K. (2021). Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design. Briefings in Bioinformatics, 22(5), bbaa411-. https://dx.doi.org/10.1093/bib/bbaa411 1467-5463 https://hdl.handle.net/10356/160382 10.1093/bib/bbaa411 33480394 2-s2.0-85106113251 5 22 bbaa411 en M4081842 RG31/18 RG109/19 MOE2018-T2-1-033 Briefings in Bioinformatics © 2021 The Author(s). Published by Oxford University Press. All rights reserved. |
| spellingShingle | Science::Mathematics Molecular Descriptor Machine Learning Liu, Xiang Wang, Xiangjun Wu, Jie Xia, Kelin Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design |
| title | Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design |
| title_full | Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design |
| title_fullStr | Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design |
| title_full_unstemmed | Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design |
| title_short | Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design |
| title_sort | hypergraph based persistent cohomology hpc for molecular representations in drug design |
| topic | Science::Mathematics Molecular Descriptor Machine Learning |
| url | https://hdl.handle.net/10356/160382 |
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