Comprehensive prediction of drug-protein interactions and side effects for the human proteome
Open accessAbstract
Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITE comb, whose average precision is ~30% and recall ~27%, is employed. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds.
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There are 26,996 consensus human exome sequences between AF2 and a previously predicted set using TASSER-VMT.
“There are 26,996 consensus human exome sequences between AF2 and a previously predicted set using TASSER-VMT.44”
From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [44]
AF2 generates significantly better structure predictions than existing methods on average.
“While AF2 generates significantly better structure predictions than existing methods on average, for the human exome, it is worthwhile to examine how many additional sequences have confident models compared to the prior art.44”
From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [44]
TASSER-VMT predicts that at least one domain has a TM-score greater than 0.4 for 89% of sequences.
“TASSER-VMT predicts that at least one domain has a TM-score greater than 0.4 (indicating global fold similarity to the native structure) for 89% of sequences.”
From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [44]
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
The protein data bank: A computer-based archival file for macromolecular structures
Bernstein, F. C., Koetzle, T. F., Williams, G. J. B., Meyer, E. F., Brice, M. D., Rodgers, J. R., Kennard, O., Shimanouchi, T., & Tasumi, M. (1977) · Journal of Molecular Biology · doi:10.1016/s0022-2836(77)80200-3 · Cited by 8,582 · see citing works
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877 · see citing works
Amino acid substitution matrices from protein blocks.
Henikoff, S., & Henikoff, J. G. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.22.10915 · Cited by 6,218 · see citing works
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382 · see citing works
DrugBank: a comprehensive resource for in silico drug discovery and exploration
Wishart, D. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj067 · Cited by 3,892 · see citing works
Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.
Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300687e · Cited by 2,412 · see citing works
Scoring function for automated assessment of protein structure template quality
Zhang, Y., & Skolnick, J. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20264 · Cited by 2,392 · see citing works
Self-citationPredicting new molecular targets for known drugs
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. (2009) · Nature · doi:10.1038/nature08506 · Cited by 1,667 · see citing works
Benchmarking sets for molecular docking.
Huang, N., Shoichet, B. K., & Irwin, J. J. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0608356 · Cited by 1,318 · see citing works
Drug target identification using side-effect similarity
Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L. J., & Bork, P. (2008) · Science · doi:10.1126/science.1158140 · Cited by 1,248 · see citing works
Prediction of drug–target interaction networks from the integration of chemical and genomic spaces
Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., & Kanehisa, M. I. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn162 · Cited by 1,148 · see citing works
How significant is a protein structure similarity with TM-score = 0.5?
Xu, J., & Zhang, Y. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq066 · Cited by 1,142 · see citing works
Ballester, P. J., & Mitchell, J. B. O. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq112 · Cited by 988 · see citing works
Gaussian interaction profile kernels for predicting drug–target interaction
van Laarhoven, T., Nabuurs, S. B., & Marchiori, E. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr500 · Cited by 935 · see citing works
A side effect resource to capture phenotypic effects of drugs
Kuhn, M., Campillos, M., Letunic, I., Jensen, L. J., & Bork, P. (2010) · Molecular Systems Biology · doi:10.1038/msb.2009.98 · Cited by 899 · see citing works
PREDICT: a method for inferring novel drug indications with application to personalized medicine
Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.26 · Cited by 892 · see citing works
Large-scale prediction and testing of drug activity on side-effect targets
Lounkine, E., Keiser, M. J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J. L., Lavan, P., Weber, E., Doak, A. K., Côté, S., et al. (2012) · Nature · doi:10.1038/nature11159 · Cited by 879 · see citing works
A Computer Program for Classifying Plants
Rogers, D. J., & Tanimoto, T. T. (1960) · Science · doi:10.1126/science.132.3434.1115 · Cited by 874 · see citing works
Similarity-based virtual screening using 2D fingerprints
Willett, P. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.10.005 · Cited by 849 · see citing works
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
Cheng, F., Liu, C., Jiang, J., Lü, W., Li, W., Liu, G., Zhou, W., Huang, J. Y., & Tang, Y. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002503 · Cited by 820 · see citing works
Global mapping of pharmacological space
Paolini, G. V., Shapland, R. H. B., van Hoorn, W. P., Mason, J. S., & Hopkins, A. L. (2006) · Nature Biotechnology · doi:10.1038/nbt1228 · Cited by 812 · see citing works
Molecular Similarity in Medicinal Chemistry
Maggiora, G. M., Vogt, M., Stumpfe, D., & Bajorath, J. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm401411z · Cited by 669 · see citing works
Supervised prediction of drug–target interactions using bipartite local models
Kevin Bleakley, A., & Yamanishi, Y. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp433 · Cited by 631 · see citing works
Neighborhood Behavior: A Useful Concept for Validation of “Molecular Diversity” Descriptors
Patterson, D. E., CRAMER, R. D. I., Ferguson, A. M., Clark, R. D., & Weinberger, L. E. (1996) · Journal of Medicinal Chemistry · doi:10.1021/jm960290n · Cited by 509 · see citing works
STITCH 4: integration of protein–chemical interactions with user data
Kuhn, M., Szklarczyk, D., Pletscher-Frankild, S., Blicher, T. H., von Mering, C., Jensen, L. J., & Peer Bork, B. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1207 · Cited by 487 · see citing works
Yamanishi, Y., Kotera, M., Kanehisa, M. I., & Goto, S. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq176 · Cited by 472 · see citing works
SPICKER: A clustering approach to identify near‐native protein folds
Zhang, Y., & Skolnick, J. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20011 · Cited by 393 · see citing works
Self-citationDrug–target interaction prediction by learning from local information and neighbors
Mei, J.-P., Kwoh, C.-K., Yang, P., Li, X., & Zheng, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts670 · Cited by 384 · see citing works
Computational Drug Repositioning: From Data to Therapeutics
Hurle, M. R., Yang, L., Xie, Q. S., Rajpal, D. K., Sanséau, P., & Agarwal, P. (2013) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2013.1 · Cited by 375 · see citing works
Automated structure prediction of weakly homologous proteins on a genomic scale
Zhang, Y., & Skolnick, J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0305695101 · Cited by 344 · see citing works
Self-citationA threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation
Brylinski, M., & Skolnick, J. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707684105 · Cited by 333 · see citing works
Self-citationKinnings, S., Liu, N., Buchmeier, N. A., Tonge, P. J., Xie, L., & Bourne, P. E. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000423 · Cited by 318 · see citing works
Drug repositioning for personalized medicine
Li, Y. Y., & Jones, S. J. M. (2012) · Genome Medicine · doi:10.1186/gm326 · Cited by 250 · see citing works
Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile
van Laarhoven, T., & Marchiori, E. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066952 · Cited by 246 · see citing works
PROMISCUOUS: a database for network-based drug-repositioning
von Eichborn, J., Murgueitio, M. S., Dunkel, M., Koerner, S., Bourne, P. E., & Preißner, R. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1037 · Cited by 225 · see citing works
Luo, H., Chen, J., Shi, L., Mikailov, M., Zhu, H., Wang, K., He, L., & Yang, L. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr299 · Cited by 206 · see citing works
Predicting Drug–Target Interactions Using Probabilistic Matrix Factorization
Çobanoğlu, M. C., Liu, C., Hu, F., Oltvai, Z. N., & Bahar, İ. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400219z · Cited by 187 · see citing works
Repurposing Strategies for Therapeutics
Sleigh, S. H., & Barton, C. L. (2010) · Pharmaceutical Medicine · doi:10.1007/bf03256811 · Cited by 167 · see citing works
Assessment of programs for ligand binding affinity prediction
Kim, R., & Skolnick, J. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.20893 · Cited by 149 · see citing works
Self-citationDrug Side-Effect Prediction Based on the Integration of Chemical and Biological Spaces
Yamanishi, Y., Pauwels, E., & Kotera, M. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci2005548 · Cited by 144 · see citing works
A Comprehensive Survey of Small-Molecule Binding Pockets in Proteins
Gao, M., & Skolnick, J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003302 · Cited by 142 · see citing works
Self-citationFr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score
Pandit, S. B., & Skolnick, J. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-531 · Cited by 138 · see citing works
Self-citationAPoc: large-scale identification of similar protein pockets
Gao, M., & Skolnick, J. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt024 · Cited by 125 · see citing works
Self-citationSystematic identification of proteins that elicit drug side effects
Kuhn, M., Banchaabouchi, M. A., Campillos, M., Jensen, L. J., Gross, C. T., Gavin, A., & Peer Bork, B. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.10 · Cited by 117 · see citing works
Gao, M., & Skolnick, J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117768109 · Cited by 99 · see citing works
Self-citationFINDSITELHM: A Threading-Based Approach to Ligand Homology Modeling
Bryliński, M., & Skolnick, J. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000405 · Cited by 78 · see citing works
Self-citationXu, K., & Coté, T. R. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr006 · Cited by 71 · see citing works
FINDSITE comb : A Threading/Structure-Based, Proteomic-Scale Virtual Ligand Screening Approach
Zhou, H., & Skolnick, J. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300510n · Cited by 63 · see citing works
Self-citationInterplay of physics and evolution in the likely origin of protein biochemical function
Skolnick, J., & Gao, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1300011110 · Cited by 62 · see citing works
Self-citationCheminformatic/bioinformatic analysis of large corporate databases: Application to drug repurposing
Loging, W., Rodriguez‐Esteban, R., Hill, J., Freeman, T. C., & Miglietta, J. J. (2011) · Drug Discovery Today Therapeutic Strategies · doi:10.1016/j.ddstr.2011.06.004 · Cited by 50 · see citing works
Zhou, H., & Skolnick, J. (2012) · Molecular Pharmaceutics · doi:10.1021/mp3000716 · Cited by 36 · see citing works
Self-citationInferring protein domains associated with drug side effects based on drug-target interaction network
Iwata, H., Mizutani, S., Tabei, Y., Kotera, M., Goto, S., & Yamanishi, Y. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-s6-s18 · Cited by 29 · see citing works
Villoutreix, B. O., Dahlb�ck, B., Borgel, D., Gandrille, S., & Muller, Y. A. (2001) · Proteins Structure Function and Bioinformatics · doi:10.1002/1097-0134(20010501)43:2 · Cited by 26 · see citing works
Srinivasan, B., Zhou, H., Kubanek, J., & Skolnick, J. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-16 · Cited by 26 · see citing works
Self-citationProject ranks billions of drug interactions
Reardon, S. (2013) · Nature · doi:10.1038/503449a · Cited by 20 · see citing works
Contrast agents and mechanisms
Dastrù, W., Longo, D. L., & Aime, S. (2011) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2011.11.013 · Cited by 16 · see citing works
Polynucleotide secondary structures: an historical perspective
Arnott, S. (1999) · doi:10.1093/oso/9780198500384.003.0001 · Cited by 13 · see citing works
Flagged first, then by citations
1 of the 59 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Arnott S. in Oxford Handbook of Nucleic Acid Structure (ed Neidle S.) 1– 38 (Oxford University Press, 1999).
Cited by
Sorted by citations
STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data
Szklarczyk, D., Santos, A., von Mering, C., Jensen, L. J., Peer Bork, B., & Kuhn, M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1277 · Cited by 1,702 · see citing works
Machine Learning in Drug Discovery: A Review
Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., & Ahsan, M. J. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10058-4 · Cited by 843 · see citing works
Drug repurposing from the perspective of pharmaceutical companies
Cha, Y., Erez, T., Reynolds, I. J., Kumar, D., Ross, J., Koytiger, G., Kusko, R. L., Zeskind, B. J., Risso, S., Kagan, E., et al. (2017) · British Journal of Pharmacology · doi:10.1111/bph.13798 · Cited by 434 · see citing works
In silico methods for drug repurposing and pharmacology
Hodos, R. A., Kidd, B. A., Shameer, K., Readhead, B., & Dudley, J. T. (2016) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1337 · Cited by 333 · see citing works
Utilizing graph machine learning within drug discovery and development
Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R., Roberts, C., Tang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab159 · Cited by 313 · see citing works
Skolnick, J., Gao, M., Zhou, H., & Singh, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01114 · Cited by 251 · see citing works
Self-citationArtificial Intelligence for COVID-19 Drug Discovery and Vaccine Development
Arshadi, A. K., Webb, J., Salem, M., Cruz, E., Calad-Thomson, S., Ghadirian, N., Collins, J., Diez‐Cecilia, E., Kelly, B., Goodarzi, H., et al. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00065 · Cited by 245 · see citing works
Changing Trends in Computational Drug Repositioning
Yella, J., Yaddanapudi, S., Wang, Y., & Jegga, A. G. (2018) · Pharmaceuticals · doi:10.3390/ph11020057 · Cited by 166 · see citing works
Link prediction in drug-target interactions network using similarity indices
Lu, Y., Guo, Y., & Korhonen, A. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1460-z · Cited by 116 · see citing works
Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects
Nguyen, P. A., Born, D. A., Deaton, A. M., Nioi, P., & Ward, L. D. (2019) · Nature Communications · doi:10.1038/s41467-019-09407-3 · Cited by 104 · see citing works
Harnessing machine learning for development of microbiome therapeutics
McCoubrey, L. E., Elbadawi, M., Orlu, M., Gaisford, S., & Basit, A. W. (2021) · Gut Microbes · doi:10.1080/19490976.2021.1872323 · Cited by 94 · see citing works
Validation strategies for target prediction methods
Mathai, N., Chen, Y., & Kirchmair, J. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz026 · Cited by 86 · see citing works
An up-to-date overview of computational polypharmacology in modern drug discovery
Chaudhari, R. R., Fong, L. W. R., Tan, Z., Huang, B., & Zhang, S. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2020.1767063 · Cited by 83 · see citing works
A brief review of protein–ligand interaction prediction
Zhao, L., Zhu, Y. L., Wang, J., Wen, N., Wang, C., & Cheng, L. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.06.004 · Cited by 61 · see citing works
Structure-based prediction of ligand–protein interactions on a genome-wide scale
Hwang, H., Dey, F., Petrey, D. S., & Honig, B. H. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705381114 · Cited by 60 · see citing works
Structural coverage of the proteome for pharmaceutical applications
Somody, J. C., MacKinnon, S. S., & Windemuth, A. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.08.004 · Cited by 59 · see citing works
Web-based drug repurposing tools: a survey
Sam, E., & Athri, P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx125 · Cited by 58 · see citing works
A knowledge-based approach for predicting gene–disease associations
Zhou, H., & Skolnick, J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw358 · Cited by 55 · see citing works
Self-citationScreening drug-target interactions with positive-unlabeled learning
Peng, L., Zhu, W. Q., Liao, B., Yu, D., Chen, M., Chen, Y., & Yang, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-08079-7 · Cited by 55 · see citing works
Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation
Tan, Y., Hu, Y. Q., Liu, X., Yin, Z., Chen, X.-W., & Liu, M. (2016) · Methods · doi:10.1016/j.ymeth.2016.07.023 · Cited by 50 · see citing works
Survey of Similarity-Based Prediction of Drug-Protein Interactions
Wang, C., & Kurgan, L. (2019) · Current Medicinal Chemistry · doi:10.2174/0929867326666190808154841 · Cited by 49 · see citing works
Repurposing FDA-approved drugs for anti-aging therapies
Snell, T. W., Johnston, R. K., Srinivasan, B., Zhou, H., Gao, M., & Skolnick, J. (2016) · Biogerontology · doi:10.1007/s10522-016-9660-x · Cited by 47 · see citing works
Self-citationA comprehensive review of computational techniques for the prediction of drug side effects
Sachdev, K., & Gupta, M. K. (2020) · Drug Development Research · doi:10.1002/ddr.21669 · Cited by 45 · see citing works
The Promise and Peril of Chemical Probe Negative Controls
Lee, J.-Y., & Schapira, M. (2021) · ACS Chemical Biology · doi:10.1021/acschembio.1c00036 · Cited by 44 · see citing works
DeepSide: A Deep Learning Approach for Drug Side Effect Prediction
Uner, O. C., Kuru, H. İ., Cinbiş, R. G., Taştan, Ö., & Çiçek, A. E. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3141103 · Cited by 43 · see citing works
Machine learning prediction of side effects for drugs in clinical trials
Galeano, D., & Paccanaro, A. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100358 · Cited by 43 · see citing works
A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases
Vicidomini, C., Fontanella, F., D’Alessandro, T., & Roviello, G. N. (2024) · Biomolecules · doi:10.3390/biom14101330 · Cited by 43 · see citing works
Sterling, J., Baker, J. R., McCluskey, A., & Munoz, L. (2023) · Nature Communications · doi:10.1038/s41467-023-38952-1 · Cited by 40 · see citing works
Detecting similar binding pockets to enable systems polypharmacology
Duran‐Frigola, M., Siragusa, L., Ruppin, E., Barril, X., Cruciani, G., & Aloy, P. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005522 · Cited by 40 · see citing works
Zhou, H., Cao, H., & Skolnick, J. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00309 · Cited by 39 · see citing works
Self-citationZhou, H., Cao, H., Matyunina, L., Shelby, M., Cassels, L., McDonald, J. F., & Skolnick, J. (2020) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.9b01248 · Cited by 39 · see citing works
Self-citationSun, J., Zhong, H., Wang, K., Li, N., & Chen, L. (2021) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2021.02.023 · Cited by 36 · see citing works
Özdemir, E. S., Halakou, F., Nussinov, R., Gürsoy, A., & Keskin, Ö. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8955-3_1 · Cited by 34 · see citing works
Targeted in vivo delivery of EGFR siRNA inhibits ovarian cancer growth and enhances drug sensitivity
Satpathy, M., Mezencev, R., Wang, L., & McDonald, J. F. (2016) · Scientific Reports · doi:10.1038/srep36518 · Cited by 32 · see citing works
Are protein-protein interfaces special regions on a protein’s surface?
Tonddast-Navaei, S., & Skolnick, J. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4937428 · Cited by 31 · see citing works
Self-citationBerishvili, V. P., Voronkov, A. E., Radchenko, E. V., & Palyulin, V. A. (2018) · Molecular Informatics · doi:10.1002/minf.201800030 · Cited by 31 · see citing works
Liu, J., Zhu, Y., He, Y., Zhu, H., Gao, Y. Q., Li, Z., Zhu, J., Sun, X., Fang, F., Wen, H., et al. (2019) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2019.1590241 · Cited by 30 · see citing works
DeepSide: a deep learning approach for drug side effect prediction
Üner, O. C., Kuru, H. İ., Cinbiş, R. G., Taştan, Ö., & Cicek, E. (2023) · Sabanci University · Cited by 28 · see citing works
Senac, C., Desgranges, S., Contino-Pepin, C., Urbach, W., Fuchs, P., & Taulier, N. (2018) · ACS Omega · doi:10.1021/acsomega.7b01212 · Cited by 28 · see citing works
Identifying Potential Ageing-Modulating Drugs In Silico
Dönertaş, H. M., Fuentealba, M., Partridge, L., & Thornton, J. M. (2018) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2018.11.005 · Cited by 28 · see citing works
Using Drug Similarities for Discovery of Possible Adverse Reactions
Muñoz, E., Nováček, V., & Vandenbussche, P.-Y. (2016) · PubMed Central · doi:10.13025/21076 · Cited by 26 · see citing works
DeepSide: A Deep Learning Framework for Drug Side Effect PredictionPreprint
Üner, O. C., Cinbiş, R. G., Taştan, Ö., & Çiçek, A. E. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/843029 · Cited by 26 · see citing works
Sohraby, F., & Aryapour, H. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.04.007 · Cited by 25 · see citing works
cando.py: Open Source Software for Predictive Bioanalytics of Large Scale Drug–Protein–Disease Data
Mangione, W., Falls, Z., Chopra, G., & Samudrala, R. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00110 · Cited by 25 · see citing works
Snell, T. W., Johnston, R. K., Matthews, A. B., Zhou, H., Gao, M., & Skolnick, J. (2018) · Biogerontology · doi:10.1007/s10522-018-9745-9 · Cited by 23 · see citing works
Self-citationMultiscale modelling of drug mechanism and safety
Zhang, J. D., Sach-Peltason, L., Krämer, C., Wang, K., & Ebeling, M. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.12.009 · Cited by 22 · see citing works
Mitchel, J., Bajaj, P., Patil, K., Gunnarson, A., Pourchet, E., Kim, Y. N., Skolnick, J., & Pai, S. B. (2021) · Cancer Informatics · doi:10.1177/11769351211065979 · Cited by 21 · see citing works
Self-citationWilson, J. L., Racz, R., Liu, T., Adeniyi, O., Sun, J., Ramamoorthy, A., Pacanowski, M. A., & Altman, R. B. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006614 · Cited by 19 · see citing works
CoDe-DTI: Collaborative Deep Learning-based Drug-Target Interaction Prediction
Yasuo, N., Nakashima, Y., & Sekijima, M. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621368 · Cited by 18 · see citing works
Choi, Y. H., Han, C. Y., Kim, K. S., & Kim, S. G. (2019) · Toxicological Research · doi:10.5487/tr.2019.35.4.319 · Cited by 16 · see citing works
Drug‐Target Interaction Prediction Based on Adversarial Bayesian Personalized Ranking
Ye, Y., Wen, Y., Zhang, Z., He, S., & Bo, X. (2021) · BioMed Research International · doi:10.1155/2021/6690154 · Cited by 16 · see citing works
Thermal Reversibility and Structural Stability in Lysozyme Induced by Epirubicin Hydrochloride
Khamari, L., Pramanik, U., Shekhar, S., Mohanakumar, S., & Mukherjee, S. (2021) · Langmuir · doi:10.1021/acs.langmuir.1c00179 · Cited by 16 · see citing works
Recycled Translation: Repurposing Drugs for Stroke
Spellicy, S. E., & Hess, D. C. (2022) · Translational Stroke Research · doi:10.1007/s12975-022-01000-z · Cited by 15 · see citing works
Yin, Z., Chen, Y., Hao, Y., Pandiyan, S., Shao, J., & Wang, L. (2023) · iScience · doi:10.1016/j.isci.2023.108756 · Cited by 16 · see citing works
Rowaiye, A. B., Ogugua, A. J., Ibeanu, G. C., Bur, D., Asala, T., Ogbeide, O. B., Abraham, E. O., & Usman, H. B. (2022) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0009799 · Cited by 15 · see citing works
Proteome‐Scale Drug‐Target Interaction Predictions: Approaches and Applications
MacKinnon, S. S., Tonekaboni, S. A. M., & Windemuth, A. (2021) · Current Protocols · doi:10.1002/cpz1.302 · Cited by 13 · see citing works
Why Is There a Glass Ceiling for Threading Based Protein Structure Prediction Methods?
Skolnick, J., & Zhou, H. (2016) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b09517 · Cited by 14 · see citing works
Self-citationIn Silico Databases and Tools for Drug Repurposing
Serçinoğlu, O., & Özbek, P. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816125-8.00024-9 · Cited by 14 · see citing works
Park, S., Lee, S., Pak, M., & Kim, S. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3350083 · Cited by 13 · see citing works
High Impact: The Role of Promiscuous Binding Sites in Polypharmacology
Cerisier, N., Petitjean, M., Regad, L., Bayard, Q., Réau, M. F., Badel, A., & Camproux, A. (2019) · Molecules · doi:10.3390/molecules24142529 · Cited by 13 · see citing works
Redka, D. S., MacKinnon, S. S., Landon, M. R., Windemuth, A., Kurji, N., & Shahani, V. M. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12071271.v1 · Cited by 13 · see citing works
On the importance of composite protein multiple ligand interactions in protein pockets
Tonddast‐Navaei, S., Srinivasan, B., & Skolnick, J. (2016) · Journal of Computational Chemistry · doi:10.1002/jcc.24523 · Cited by 12 · see citing works
Self-citationAmano, Y., Honda, H., Sawada, R., Nukada, Y., Yamane, M., Ikeda, N., Morita, O., & Yamanishi, Y. (2020) · The Journal of Toxicological Sciences · doi:10.2131/jts.45.137 · Cited by 12 · see citing works
Evaluating molecular fingerprint-based models of drug side effects against a statistical control
Alpay, B. A., Gosink, M. M., & Aguiar, D. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.103364 · Cited by 12 · see citing works
Utilising Graph Machine Learning within Drug Discovery and DevelopmentPreprint
Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R. J., Roberts, C., Tang, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.05716 · Cited by 11 · see citing works
How special is the biochemical function of native proteins?
Skolnick, J., Gao, M., & Zhou, H. (2016) · F1000Research · doi:10.12688/f1000research.7374.1 · Cited by 10 · see citing works
Self-citationBegum, T., Ghosh, T. C., & Basak, S. (2017) · Genome Biology and Evolution · doi:10.1093/gbe/evw301 · Cited by 10 · see citing works
Bansal, A., Srivastava, P. A., & Singh, T. R. (2018) · Scientific Reports · doi:10.1038/s41598-018-28577-6 · Cited by 10 · see citing works
Khadempar, S., Lotfi, M., Haghiralsadat, F., Saidijam, M., Ghasemi, N., & Afshar, S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107518 · Cited by 9 · see citing works
Bioinformatics Perspective of Drug Repurposing
Patel, B., Gelat, B., Soni, M. N., Rathaur, P., & Johar, K. (2023) · Current Bioinformatics · doi:10.2174/0115748936264692230921071504 · Cited by 9 · see citing works
Fisher, J. L., Clark, A. D., Jones, E. F., & Lasseigne, B. N. (2024) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-023-00727-1 · Cited by 9 · see citing works
Machine learning for predicting drug–drug interactions: Graph neural networks and beyond
Petschner, P., Nguyen, A. D., Nguyen, C. H., & Mamitsuka, H. (2025) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2025.100551 · Cited by 9 · see citing works
How has artificial intelligence impacted COVID-19 drug repurposing and what lessons have we learned?
Özdemir, E. S., Ranganathan, S. V., & Nussinov, R. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2128333 · Cited by 9 · see citing works
Joshi, S., & Singh, V. (2020) · Chemical Papers · doi:10.1007/s11696-020-01231-0 · Cited by 9 · see citing works
Aloi, E., Rizzuti, B., Guzzi, R., & Bartucci, R. (2020) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2020.108599 · Cited by 9 · see citing works
Drug‐target‐ADR Network and Possible Implications of Structural Variants in Adverse Events
Dafniet, B., Cerisier, N., Audouze, K., & Taboureau, O. (2020) · Molecular Informatics · doi:10.1002/minf.202000116 · Cited by 8 · see citing works
Preclinical side effect prediction through pathway engineering of protein interaction network models
Alidoost, M., & Wilson, J. L. (2024) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.13150 · Cited by 7 · see citing works
In Silico Tools to Score and Predict Cholesterol–Protein Interactions
Nguyen, A., & Ondrus, A. E. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01885 · Cited by 8 · see citing works
Xuan, P., Cheng, T., Cui, H., Gu, J., Jin, Q., & Zhang, T. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00136 · Cited by 8 · see citing works
Hossen, M. A., Jahan, U. M. S., Hossain, M. A., Asif, K. H., Rahman, A., Ahmed, S., Uddin, M. M., Amin, M. F., Barik, M. A., Islam, M. J., et al. (2025) · PLOS One · doi:10.1371/journal.pone.0320550 · Cited by 8 · see citing works
Seraj, S., Cho, Y., Lee, J., & Ahn, H. J. (2019) · Biochemical Pharmacology · doi:10.1016/j.bcp.2019.113735 · Cited by 8 · see citing works
Xu, C., Zou, H., Yu, X., Xie, Y., Cai, J., Shang, Q., Ouyang, N., Wang, Y., Xu, P., He, Z., et al. (2020) · ChemistryOpen · doi:10.1002/open.202000235 · Cited by 7 · see citing works
Aldehoff, A. S., Karkossa, I., Broghammer, H., Krupka, S., Weiner, J., Goerdeler, C., Nuwayhid, R., Langer, S., Wabitsch, M., Rolle-Kampczyk, U. E., et al. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c01206 · Cited by 7 · see citing works
Vigil-Vásquez, C., & Schüller, A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23179666 · Cited by 7 · see citing works
Using Machine Learning To Inform Decisions in Drug Discovery: An Industry Perspective
Green, D. V. S. (2019) · ACS symposium series · doi:10.1021/bk-2019-1326.ch005 · Cited by 7 · see citing works
Lee, J., Konc, J., Janežič, D., & Brooks, B. R. (2017) · Scientific Reports · doi:10.1038/s41598-017-10412-z · Cited by 7 · see citing works
Aksam, V. K. M., Chandrasekaran, V. M., & Pandurangan, S. (2017) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.08.006 · Cited by 6 · see citing works
Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors
Naveed, H., Reglin, C., Schubert, T. E. O., Gao, X., Arold, S. T., & Maitland, M. L. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.05.006 · Cited by 6 · see citing works
Systematic in silico Evaluation of Leishmania spp. Proteomes for Drug Discovery
Vasconcelos, C. R. D. S., & Rezende, A. M. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2021.607139 · Cited by 6 · see citing works
Astore, C., Zhou, H., Jacob, J., & Skolnick, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-00368-6 · Cited by 6 · see citing works
Self-citationRao, G., Nkepang, G. N., Xu, J., Yari, H., Houson, H. A., Teng, C., & Awasthi, V. (2018) · Frontiers in Chemistry · doi:10.3389/fchem.2018.00392 · Cited by 6 · see citing works
Network pharmacology to elucidate the role of phytotherapy in neurocognitive disorders
Barth, S., & Efferth, T. (2025) · Phytomedicine · doi:10.1016/j.phymed.2025.157031 · Cited by 6 · see citing works
Drug Dosage Balancing Using Large Scale Multi-omics Datasets
Jha, A., Mehdi, M., Khan, Y., Mehmood, Q., Rebholz‐Schuhmann, D., & Sahay, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57741-8_6 · Cited by 5 · see citing works
Spectroscopic and thermodynamic interaction studies of anti-diabetic drug with globular proteins
(2019) · Biointerface Research in Applied Chemistry · doi:10.33263/briac94.172176 · Cited by 5 · see citing works
Identification of cluster of proteins in the network of MAPK pathways as cancer drug targets
Aksam, V. K. M., Chandrasekaran, V. M., & Pandurangan, S. (2017) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.07.001 · Cited by 5 · see citing works
Abuse liability assessment for biologic drugs – All molecules are not created equal
de Zafra, C. L. Z., Markgraf, C. G., Compton, D. M., & Hudzik, T. J. (2017) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2017.11.019 · Cited by 5 · see citing works
Networks and Pathways in Systems Pharmacology
Bezerianos, A., Dragomir, A., & Balomenos, P. (2017) · SpringerBriefs in computer science · doi:10.1007/978-3-319-53868-6_2 · Cited by 4 · see citing works
Synthesis of imatinib, a tyrosine kinase inhibitor, labeled with carbon‐14
Kang, J., Lee, J. Y., Park, J. H., & Chang, D. (2020) · Journal of Labelled Compounds and Radiopharmaceuticals · doi:10.1002/jlcr.3830 · Cited by 4 · see citing works
Karakoltzidis, A., Karakitsios, S., & Sarigiannis, D. A. (2025) · NAM journal. · doi:10.1016/j.namjnl.2025.100012 · Cited by 3 · see citing works
Banach, M., Konieczny, L., & Roterman, I. (2019) · Elsevier eBooks · doi:10.1016/b978-0-08-102981-7.00019-1 · Cited by 3 · see citing works
- Authors
- Zhou, H., Gao, M., & Skolnick, J.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Skolnick, Jeffrey
- Venue
- Scientific Reports
- Published
- 2015-06-09
- Volume
- 5
- Issue
- 1
- Pages
- 11090-11090
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4603786
- OpenAlex
- W2416913394
- Cited by
- 138
- References
- 59
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 24 · 2023: 8 · 2022: 9 · 2021: 13 · 2020: 16 · 2019: 17 · 2018: 14 · 2017: 16 · 2016: 9 · 2015: 2
- FWCI
- 14.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- U.S. Bureau of Land Management
- National Institute of General Medical Sciences (GM 37408, R01GM037408, GM-48835, R01GM048835)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- drug-protein interaction prediction, drug side effect prediction, drug repurposing, human proteome, drug promiscuity, drug withdrawal, disease association
- MeSH terms
- Pharmaceutical Preparations, Humans, Protein Binding, Proteins, Proteome, Drug-Related Side Effects and Adverse Reactions
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