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We build upon these advances by designing a novel deep learning architecture consisting of a 3-dimensional convolutional neural network utilizing channel-wise attention and two graph convolutional networks utilizing attention-based aggregation of node features. HAC-Net (Hybrid Attention-Based Convolutional Neural Network) obtains state-of-the-art results on the PDBbind v.2016 core set, the most widely recognized benchmark in the field. We extensively assess the generalizability of our model using multiple train-test splits, each of which maximizes differences between either protein structures, protein sequences, or ligand extended-connectivity fingerprints of complexes in the training and test sets. Furthermore, we perform 10-fold cross-validation with a similarity cutoff between SMILES strings of ligands in the training and test sets and also evaluate the performance of HAC-Net on lower-quality data. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction. All of our software is available as an open-source repository at https:\/\/github.com\/gregory-kyro\/HAC-Net\/, and the HACNet Python package is available through PyPI.<\/jats:p>","DOI":"10.1021\/acs.jcim.3c00251","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T15:44:04Z","timestamp":1680104644000},"page":"1947-1960","source":"Crossref","is-referenced-by-count":75,"title":["HAC-Net: A Hybrid\nAttention-Based Convolutional Neural\nNetwork for Highly Accurate Protein\u2013Ligand Binding Affinity\nPrediction"],"prefix":"10.1021","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0095-8548","authenticated-orcid":true,"given":"Gregory\nW.","family":"Kyro","sequence":"first","affiliation":[{"name":"Yale University , , ,","place":["New Haven, Connecticut, United States, 06511-8499"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3233-7914","authenticated-orcid":true,"given":"Rafael I.","family":"Brent","sequence":"additional","affiliation":[{"name":"Yale University , , ,","place":["New Haven, Connecticut, United States, 06511-8499"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3262-1237","authenticated-orcid":true,"given":"Victor S.","family":"Batista","sequence":"additional","affiliation":[{"name":"Yale University , , ,","place":["New Haven, Connecticut, United States, 06511-8499"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"2026081804441490600_cit1","doi-asserted-by":"publisher","volume-title":"Protein\u2013Ligand\nInteractions: General Description","author":"Dunn","DOI":"10.1002\/9780470015902.a0001340.pub2"},{"key":"2026081804441490600_cit2","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1186\/s13059-019-1689-0","article-title":"Machine learning\nand complex biological data","volume":"20","author":"Xu","year":"2019","journal-title":"Genome Biol."},{"key":"2026081804441490600_cit3","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A guide\nto machine\nlearning for biologists","volume":"23","author":"Greener","year":"2022","journal-title":"Nat. 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