(1) Keiser, M. J.; Setola, V.; Irwin, J. J.; Laggner, C.; Abbas, A. I.;
Hufeisen, S. J.; Jensen, N. H.; Kuijer, M. B.; Matos, R. C.; Tran, T. B.;
et al. Predicting new molecular targets for known drugs. Nature 2009,
462, 175−181.
(2) Macarron, R.; Banks, M. N.; Bojanic, D.; Burns, D. J.; Cirovic, D.
A.; Garyantes, T.; Green, D. V.; Hertzberg, R. P.; Janzen, W. P.;
Paslay, J. W.; et al. Impact of high-throughput screening in biomedical
research. Nat. Rev. Drug Discovery 2011, 10, 188−195.
(3) Trott, O.; Olson, A. J. AutoDock Vina: improving the speed and
accuracy of docking with a new scoring function, efficient
optimization, and multithreading. J. Comput. Chem. 2009, 31, 455−
461.
(4) Meng, X. Y.; Zhang, H. X.; Mezei, M.; Cui, M. Molecular
docking: a powerful approach for structure-based drug discovery.
Curr. Comput.-Aided Drug Des. 2011, 7, 146−157.
(5) Meiler, J.; Baker, D. ROSETTALIGAND: Protein−small
molecule docking with full side-chain flexibility. Proteins: Struct.,
Funct., Bioinf. 2006, 65, 538−548.
(6) Wishart, D. S.; Feunang, Y. D.; Guo, A. C.; Lo, E. J.; Marcu, A.;
Grant, J. R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; et al.
DrugBank 5.0: a major update to the DrugBank database for 2018.
Nucleic Acids Res. 2018, 46, D1074−D1082.
(7) Wang, R.; Fang, X.; Lu, Y.; Wang, S. The PDBbind database:
Collection of binding affinities for protein− ligand complexes with
known three-dimensional structures. J. Med. Chem. 2004, 47, 2977−
2980.
(8) Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.;
Shoemaker, B. A.; Thiessen, P. A.; Yu, B.; et al. PubChem 2019
update: improved access to chemical data. Nucleic Acids Res. 2019, 47,
D1102−D1109.
4558
https://doi.org/10.1021/acs.jcim.3c00269
J. Chem. Inf. Model. 2023, 63, 4552−4559
Journal of Chemical Information and Modeling
pubs.acs.org/jcim
(29) Davis, M. I.; Hunt, J. P.; Herrgard, S.; Ciceri, P.; Wodicka, L.
M.; Pallares, G.; Hocker, M.; Treiber, D. K.; Zarrinkar, P. P.
Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol.
2011, 29, 1046−1051.
(30) Boeckmann, B.; Bairoch, A.; Apweiler, R.; Blatter, M.-C.;
Estreicher, A.; Gasteiger, E.; Martin, M. J.; Michoud, K.; O’Donovan,
C.; Phan, I. The SWISS-PROT protein knowledgebase and its
supplement TrEMBL in 2003. Nucleic Acids Res. 2003, 31, 365−370.
(31) Ö ztürk, H.; Ö zgür, A.; Ozkirimli, E. DeepDTA: deep drug−
target binding affinity prediction. Bioinformatics 2018, 34, i821−i829.
(32) Kojima, R.; Ishida, S.; Ohta, M.; Iwata, H.; Honma, T.; Okuno,
Y. kGCN: a graph-based deep learning framework for chemical
structures. J. Cheminf. 2020, 12, 32.
(33) Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J.
Chem. Inf. Model. 2010, 50, 742−754.
(34) Ö zdemir, Ö .; Sönmez, E. B. Weighted cross-entropy for
unbalanced data with application on covid x-ray images. 2020
Innovations in Intelligent Systems and Applications Conference (ASYU);
IEEE, 2020; pp 1−6.
(35) Drummond, C.; Holte, R. C. C4. 5, class imbalance, and cost
sensitivity: why under-sampling beats over-sampling. Workshop on
Learning from Imbalanced Datasets II; ICML, 2003; Vol. 11, pp 1−8.
(36) Bai, X.; Yin, Y. Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinasecentric drug development. J. Cheminf. 2021, 13, 95.
(37) Lihong, P.; Wang, C.; Tian, X.; Zhou, L.; Li, K. Finding lncrnaprotein interactions based on deep learning with dual-net neural
architecture. IEEE/ACM Transactions on Computational Biology and
Bioinformatics; IEEE, 2022; Vol. 19, pp 3456−3468.
(38) Saito, T.; Rehmsmeier, M. The precision-recall plot is more
informative than the ROC plot when evaluating binary classifiers on
imbalanced datasets. PLoS One 2015, 10, No. e0118432.
(39) Xu, K.; Hu, W.; Leskovec, J.; Jegelka, S. How powerful are
graph neural networks? 2018, arXiv:1810.00826. arXiv preprint.
(40) McInnes, L.; Healy, J.; Melville, J. Umap: Uniform manifold
approximation and projection for dimension reduction. 2018,
arXiv:1802.03426. arXiv preprint.
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