RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design
Abstract
Deep learning (DL)-based de novo molecular design has recently gained considerable traction. Many DL-based generative models have been successfully developed to design novel molecules, but most of them are ligand-centric and the role of the 3D geometries of target binding pockets in molecular generation has not been well-exploited. Here, we proposed a new 3D-based generative model called RELATION. In the RELATION model, the BiTL algorithm was specifically designed to extract and transfer the desired geometric features of the protein-ligand complexes to a latent space for generation. The pharmacophore conditioning and docking-based Bayesian sampling were applied to efficiently navigate the vast chemical space for the design of molecules with desired geometric properties and pharmacophore features. As a proof of concept, the RELATION model was used to design inhibitors for two targets, AKT1 and CDK2. The calculation results demonstrated that the RELATION model could efficiently generate novel molecules with favorable binding affinity and pharmacophore features.
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Generative models have been used to compile focused and diversity-oriented chemical libraries, design inhibitors de novo, and perform hit-to-lead optimization, typically in a multiobjective mode.
“Generative models have been used to compile both focused and diversity-oriented chemical libraries,149,150 to design inhibitors de novo,151 and to perform hit-to-lead optimization,152 typically in a multiobjective mode where e.g. selectivity, synthetic accessibility, and drug-like properties are improved simultaneously.153,154”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [151]
Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412 · see citing works
AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings
Eberhardt, J., Santos-Martins, D., Tillack, A., & Forli, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00203 · Cited by 7,111 · see citing works
Generative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477 · see citing works
ZINC 15 – Ligand Discovery for Everyone
Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224 · see citing works
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106 · see citing works
PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints
Yap, C. W. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21707 · Cited by 2,979 · see citing works
ZINC: A Free Tool to Discover Chemistry for Biology
Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S. D., & Coleman, R. G. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3001277 · Cited by 2,533 · see citing works
The Properties of Known Drugs. 1. Molecular Frameworks
Bemis, G. W., & Murcko, M. A. (1996) · Journal of Medicinal Chemistry · doi:10.1021/jm9602928 · Cited by 2,501 · see citing works
Wolber, G., & Langer, T. (2004) · Journal of Chemical Information and Modeling · doi:10.1021/ci049885e · Cited by 1,989 · see citing works
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · see citing works
Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L., & Chong, J. P. C. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1072 · Cited by 1,632 · see citing works
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537 · see citing works
Transfer Learning for Reinforcement Learning Domains: A Survey
Taylor, M. E., & Stone, P. (2009) · doi:10.5555/1577069.1755839 · Cited by 1,521 · see citing works
Prediction of Physicochemical Parameters by Atomic Contributions
Wildman, S. A., & Crippen, G. M. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci990307l · Cited by 1,491 · see citing works
Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models
Hershey, J. R., & Olsen, P. A. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366913 · Cited by 1,219 · see citing works
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., & Erhan, D. (2016) · arXiv · doi:10.48550/arxiv.1608.06019 · Cited by 1,208 · see citing works
Rethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894 · see citing works
ChEMBL web services: streamlining access to drug discovery data and utilities
Davies, M., Nowotka, M., Papadatos, G., Dedman, N., Gaulton, A., Atkinson, F. L., Bellis, L. J., & Overington, J. P. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv352 · Cited by 765 · see citing works
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction
Stepniewska-Dziubinska, M. M., Zielenkiewicz, P., & Siedlecki, P. S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty374 · Cited by 665 · see citing works
Jiang, D., Wu, Z., Hsieh, C.-Y., Chen, G., Liao, B., Wang, Z., Shen, C., Cao, D., Wu, J., & Hou, T. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-020-00479-8 · Cited by 664 · see citing works
Self-citationEstimation of the size of drug-like chemical space based on GDB-17 data
Polishchuk, P. G., Madzhidov, T. I., & Varnek, A. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9672-4 · Cited by 619 · see citing works
Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00346 · Cited by 587 · see citing works
REINVENT 2.0: An AI Tool for De Novo Drug Design
Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00915 · Cited by 496 · see citing works
Generative Recurrent Networks for De Novo Drug Design
Gupta, A., Müller, A. T., Huisman, B. J. H., Fuchs, J. A., Schneider, P., & Schneider, G. (2017) · Molecular Informatics · doi:10.1002/minf.201700111 · Cited by 474 · see citing works
LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads
B�hm, H.-J. (1992) · Journal of Computer-Aided Molecular Design · doi:10.1007/bf00126217 · Cited by 459 · see citing works
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Lim, J., Ryu, S., Kim, J. W., & Kim, W. Y. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0286-7 · Cited by 421 · see citing works
An Overview of Drug Discovery and Development
Berdigaliyev, N., & Aljofan, M. (2020) · Future Medicinal Chemistry · doi:10.4155/fmc-2019-0307 · Cited by 409 · see citing works
Constrained Bayesian optimization for automatic chemical design using variational autoencoders
Griffiths, R., & Hernández-Lobato, J. M. (2019) · Chemical Science · doi:10.1039/c9sc04026a · Cited by 391 · see citing works
Classification of scaffold-hopping approaches
Sun, H., Tawa, G. J., & Wallqvist, A. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.10.024 · Cited by 383 · see citing works
Multi-objective de novo drug design with conditional graph generative model
Li, Y., Zhang, L., & Liu, Z. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0287-6 · Cited by 377 · see citing works
<i>De Novo</i> Design of Bioactive Small Molecules by Artificial Intelligence
Merk, D., Friedrich, L., Grisoni, F., & Schneider, G. (2018) · Molecular Informatics · doi:10.1002/minf.201700153 · Cited by 373 · see citing works
Transfer Learning for Drug Discovery
Cai, C., Wang, S., Xu, Y., Zhang, W., Tang, K., Ouyang, Q., Lai, L., & Pei, J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02147 · Cited by 364 · see citing works
Deep learning in drug discovery: opportunities, challenges and future prospects
Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353 · see citing works
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S., & Klambauer, G. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00234 · Cited by 345 · see citing works
Efficient overlay of small organic molecules using 3D pharmacophores
Wolber, G., Dornhofer, A. A., & Langer, T. (2006) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9078-7 · Cited by 317 · see citing works
Bai, Q., Tan, S., Xu, T., Liu, H., Huang, J., & Yao, X. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa161 · Cited by 291 · see citing works
Self-citationJiang, D., Hsieh, C., Wu, Z., Kang, Y., Wang, J., Wang, E., Liao, B., Shen, C., Xu, L., Wu, J. F., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01830 · Cited by 260 · see citing works
Self-citationDe novo design of molecular architectures by evolutionary assembly of drug-derived building blocks
Schneider, G., Lee, M., Ståhl, M., & Schneider, P. (2000) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008184403558 · Cited by 252 · see citing works
DOGS: Reaction-Driven de novo Design of Bioactive Compounds
Hartenfeller, M., Zettl, H., Walter, M., Rupp, M., Reisen, F., Proschak, E., Weggen, S., Stark, H., & Schneider, G. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002380 · Cited by 251 · see citing works
The next level in chemical space navigation: going far beyond enumerable compound libraries
Hoffmann, T., & Gastreich, M. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.02.013 · Cited by 245 · see citing works
From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
Shen, C., Ding, J., Wang, Z., Cao, D., Ding, X., & Hou, T. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1429 · Cited by 245 · see citing works
Self-citationTakebe, T., Imai, R., & Ono, S. (2018) · Clinical and Translational Science · doi:10.1111/cts.12577 · Cited by 239 · see citing works
Addie, M., Ballard, P. G., Buttar, D., Crafter, C., Currie, G. S., Davies, B. R., Debreczeni, J. E., Dry, H., Dudley, P., Greenwood, R. D. R., et al. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm301762v · Cited by 236 · see citing works
Kotsias, P.-C., Arús‐Pous, J., Chen, H., Engkvist, O., Tyrchan, C., & Bjerrum, E. J. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0174-5 · Cited by 235 · see citing works
Shape-Based Generative Modeling for de Novo Drug Design
Skalic, M., Jiménez, J., Sabbadin, D., & De Fabritiis, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00706 · Cited by 229 · see citing works
Bioisosteric Replacement and Scaffold Hopping in Lead Generation and Optimization
Langdon, S. R., Ertl, P., & Brown, N. (2010) · Molecular Informatics · doi:10.1002/minf.201000019 · Cited by 227 · see citing works
SYNOPSIS: SYNthesize and OPtimize System in Silico
Vinkers, H. M., de Jonge, M. R., Daeyaert, F. F. D., Heeres, J., Koymans, L. M. H., van Lenthe, J. H., Lewi, P. J., Timmerman, H., Aken, K. V., & Janssen, P. A. J. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm030809x · Cited by 225 · see citing works
Grechishnikova, D. (2021) · Scientific Reports · doi:10.1038/s41598-020-79682-4 · Cited by 224 · see citing works
Generative molecular design in low data regimes
Moret, M., Friedrich, L., Grisoni, F., Merk, D., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0160-y · Cited by 219 · see citing works
Conditional Molecular Design with Deep Generative Models
Kang, S., & Cho, K. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00263 · Cited by 199 · see citing works
Generating 3D molecules conditional on receptor binding sites with deep generative models
Ragoza, M., Masuda, T., & Koes, D. R. (2022) · Chemical Science · doi:10.1039/d1sc05976a · Cited by 195 · see citing works
Inverse design of 3d molecular structures with conditional generative neural networks
Gebauer, N. W. A., Gastegger, M., Hessmann, S. S. P., Müller, K., & Schütt, K. T. (2022) · Nature Communications · doi:10.1038/s41467-022-28526-y · Cited by 187 · see citing works
SPROUT: Recent developments in the de novo design of molecules
Gillet, V. J., Newell, W. R., Mata, P., Myatt, G. J., Sike, S., Zsoldos, Z., & Johnson, A. P. (1994) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00017a027 · Cited by 179 · see citing works
Structure-based de novo drug design using 3D deep generative models
Li, Y., Pei, J., & Lai, L. (2021) · Chemical Science · doi:10.1039/d1sc04444c · Cited by 176 · see citing works
Martin, M. P., Olesen, S. H., Georg, G. I., & Schönbrunn, E. (2013) · ACS Chemical Biology · doi:10.1021/cb4003283 · Cited by 160 · see citing works
Deep learning approaches for de novo drug design: An overview
Wang, M., Wang, Z., Sun, H., Wang, J., Shen, C., Weng, G., Chai, X., Li, H., Cao, D., & Hou, T. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.10.001 · Cited by 153 · see citing works
Self-citationClark, D. E., Frenkel, D., Levy, S. A., et al. (1995) · Journal of Computer-Aided Molecular Design · doi:10.1007/bf00117275 · Cited by 135 · see citing works
Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction
Wang, F., Yang, J., Wang, M., Jia, C., Shi, X.-X., Hao, G., & Yang, G. (2020) · 中国科学通报：英文版 · doi:10.1016/j.scib.2020.04.006 · Cited by 133 · see citing works
Self-citationFrom Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design
Škalič, M., Sabbadin, D., Sattarov, B., Sciabola, S., & Fabritiis, G. D. (2019) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.9b00634 · Cited by 130 · see citing works
Deep generative design with 3D pharmacophoric constraints
Imrie, F., Hadfield, T. E., Bradley, A. R., & Deane, C. M. (2021) · Chemical Science · doi:10.1039/d1sc02436a · Cited by 113 · see citing works
CONCERTS: Dynamic Connection of Fragments as an Approach to de Novo Ligand Design
Pearlman, D. A., & Murcko, M. A. (1996) · Journal of Medicinal Chemistry · doi:10.1021/jm950792l · Cited by 111 · see citing works
Mining Toxicity Information from Large Amounts of Toxicity Data
Wu, Z., Jiang, D., Wang, J., Hsieh, C., Cao, D., & Hou, T. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c00421 · Cited by 109 · see citing works
Self-citationDe novodesign: balancing novelty and confined chemical space
Kutchukian, P. S., & Shakhnovich, E. I. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2010.497534 · Cited by 92 · see citing works
Xu, M., Ran, T., & Chen, H. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01494 · Cited by 85 · see citing works
How big can the commutator of two matrices be and how big is it typically?
Böttcher, A., & Wenzel, D. (2005) · Linear Algebra and its Applications · doi:10.1016/j.laa.2005.02.012 · Cited by 76 · see citing works
Xiong, G., Shen, C., Yang, Z., Jiang, D., Liu, S., Lü, A., Chen, X., Hou, T., & Cao, D. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1567 · Cited by 47 · see citing works
Self-citationJoshi, R. P., Gebauer, N. W. A., Bontha, M., Khazaieli, M., James, R. M., Brown, J., & Kumar, N. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c06437 · Cited by 46 · see citing works
Identification of active molecules againstMycobacterium tuberculosisthrough machine learning
Ye, Q., Chai, X., Jiang, D., Liu, Y., Shen, C., Zhang, X., Li, D., Cao, D., & Hou, T. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab068 · Cited by 45 · see citing works
Self-citationWang, J., Cao, D., Tang, C., Xu, L., He, Q., Yang, B., Chen, X., Sun, H., & Hou, T. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa183 · Cited by 41 · see citing works
Self-citationNew thiazole carboxamides as potent inhibitors of Akt kinases
Chang, S., Zhang, Z., Zhuang, X., Luo, J., Cao, X., Li, H., Tu, Z., Lu, X., Ren, X., & Ding, K. (2011) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2011.11.080 · Cited by 37 · see citing works
Self-citationQSAR study of Akt/protein kinase B (PKB) inhibitors using support vector machine
Dong, X., Jiang, C., Hu, H., et al. (2009) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2009.04.050 · Cited by 24 · see citing works
Xue, S., & Yan, X. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.05.005 · Cited by 17 · see citing works
Parthasarathy, S., Henry, K. J., Pei, H., et al. (2018) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2018.03.092 · Cited by 15 · see citing works
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Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50 · see citing works
- Authors
- Wang, M., Hsieh, C.-Y., Wang, J., Wang, D., Weng, G., Shen, C., Yao, X., Bing, Z., Li, H., Cao, D., & Hou, T.
- Affiliations
- Zhejiang University (CN)
- Tencent (China) (CN)
- Macau University of Science and Technology (MO)
- Chinese Academy of Sciences (CN)
- East China University of Science and Technology (CN)
- Central South University (CN)
- Corresponding author
- Li, Honglin
- Cao, Dongsheng
- Hou, Tingjun
- ORCID
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-06-17
- Volume
- 65
- Issue
- 13
- Pages
- 9478-9492
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W4283075725
- Cited by
- 99
- References
- 75
- Citations by year
- 2026: 11 · 2025: 26 · 2024: 31 · 2023: 23 · 2022: 8
- FWCI
- 11.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Medicinal Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Science and Technology of the People's Republic of China (2021YFF1201400)
- Central University Basic Research Fund of China (2020QNA7003)
- Natural Science Foundation of Zhejiang Province (LZ19H300001)
- Key Research and Development Program of Zhejiang Province (2020C03010)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- de novo drug design, relations, AKT1 inhibitors, CDK2 inhibitors, generative models
- MeSH terms
- Bayes Theorem, Ligands, Drug Design
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