<?xml version='1.0' encoding='UTF-8'?><codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5"><docDscr><citation><titlStmt><titl>Replication Data for: Hypergraph based persistent cohomology (HPC) for molecular representations in drug design</titl><IDNo agency="DOI">doi:10.21979/N9/16CFBW</IDNo></titlStmt><distStmt><distrbtr source="archive">DR-NTU (Data)</distrbtr><distDate>2021-04-02</distDate></distStmt><verStmt source="archive"><version date="2021-04-06" type="RELEASED">2</version></verStmt><biblCit>Xia, Kelin, 2021, "Replication Data for: Hypergraph based persistent cohomology (HPC) for molecular representations in drug design", https://doi.org/10.21979/N9/16CFBW, DR-NTU (Data), V2</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>Replication Data for: Hypergraph based persistent cohomology (HPC) for molecular representations in drug design</titl><IDNo agency="DOI">doi:10.21979/N9/16CFBW</IDNo></titlStmt><rspStmt><AuthEnty affiliation="Nanyang Technological University">Xia, Kelin</AuthEnty></rspStmt><prodStmt><software>Python</software><grantNo agency="Nanyang Technological University">Startup Grant M4081842</grantNo><grantNo agency="Ministry of Education (MOE)">Academic Research fund Tier 1 RG31/18</grantNo><grantNo agency="Ministry of Education (MOE)">RG109/19</grantNo><grantNo agency="Ministry of Education (MOE)">Tier 2 MOE2018-T2-1-033</grantNo><grantNo agency="Natural Science Foundation of China (NSFC)">11871284</grantNo><grantNo agency="Natural Science Foundation of China (NSFC)">11971144</grantNo></prodStmt><distStmt><distrbtr source="archive">DR-NTU (Data)</distrbtr><contact affiliation="Nanyang Technological University">Xia, Kelin</contact><depositr>Xia, Kelin</depositr><depDate>2021-04-02</depDate></distStmt><holdings URI="https://doi.org/10.21979/N9/16CFBW"/></citation><stdyInfo><subject><keyword xml:lang="en">Computer and Information Science</keyword><keyword xml:lang="en">Mathematical Sciences</keyword><keyword xml:lang="en">Medicine, Health and Life Sciences</keyword><keyword>Computer and Information Science</keyword><keyword>Mathematical Sciences</keyword><keyword>Medicine, Health and Life Sciences</keyword><keyword>molecular descriptor, machine learning, hypergraph-based persistent cohomology, drug design</keyword></subject><abstract date="2021-4-2">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.</abstract><sumDscr><dataKind>Code</dataKind></sumDscr></stdyInfo><method><dataColl><sources/></dataColl><anlyInfo/></method><dataAccs><setAvail/><useStmt/></dataAccs><othrStdyMat><relStdy>The PDBbind databases were obtained from &lt;a href="http://pdbbind.org.cn"> http://pdbbind.org.cn&lt;/a>.The codes implemented for the hypergraph persistent cohomology and HPC-GBT models can be found in &lt;a href="http://github.com/LiuXiangMath/Hypergraph-based-Persistent-Cohomology"> http://github.com/LiuXiangMath/Hypergraph-based-Persistent-Cohomology&lt;/a>.</relStdy><relPubl><citation><titlStmt><IDNo agency="doi">10.1093/bib/bbaa411</IDNo></titlStmt><biblCit>Liu, X., Wang, X., Wu, J., &amp; Xia, K. (2021). Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design. Briefings in Bioinformatics.</biblCit></citation><ExtLink URI="https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbaa411/6105940"/></relPubl></othrStdyMat></stdyDscr><otherMat ID="f62341" URI="https://researchdata.ntu.edu.sg/api/access/datafile/62341" level="datafile"><labl>Hypergraph-based-Persistent-Cohomology-master.zip</labl><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">application/zip</notes></otherMat></codeBook>