The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Deep Generative Models for 3D Linker Design
Open accessAbstract
Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of three-dimensional (3D) structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural knowledge. Our method ("DeLinker") takes two fragments or partial structures and designs a molecule incorporating both. The generation process is protein-context-dependent, utilizing the relative distance and orientation between the partial structures. This 3D information is vital to successful compound design, and we demonstrate its impact on the generation process and the limitations of omitting such information. In a large-scale evaluation, DeLinker designed 60% more molecules with high 3D similarity to the original molecule than a database baseline. When considering the more relevant problem of longer linkers with at least five atoms, the outperformance increased to 200%. We demonstrate the effectiveness and applicability of this approach on a diverse range of design problems: fragment linking, scaffold hopping, and proteolysis targeting chimera (PROTAC) design. As far as we are aware, this is the first molecular generative model to incorporate 3D structural information directly in the design process. The code is available at https://github.com/oxpig/DeLinker.
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Cites
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
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
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
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895
Automated design of ligands to polypharmacological profiles
Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al. (2012) · Nature · doi:10.1038/nature11691 · Cited by 832
Deep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683
Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets
Hussain, J., & Rea, C. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900450m · Cited by 420
Cited by
PROTACs: past, present and future
Li, K., & Crews, C. M. (2022) · Chemical Society Reviews · doi:10.1039/d2cs00193d · Cited by 869
Current strategies for the design of PROTAC linkers: a critical review
Troup, R. I., Fallan, C., & Baud, M. G. J. (2020) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2020.00018 · Cited by 424
“[148], investigated whether their model could design alternate linkers to the PEG chain in 96 that could retain the same interactions and conformation.”
BackgroundPRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes
Zaidman, D., Prilusky, J., & London, N. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00589 · Cited by 224
DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs
Li, F., Hu, Q., Zhang, X., Sun, R., Liu, Z., Wu, S., Tian, S., Ma, X., Dai, Z., Yang, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34807-3 · Cited by 159
Rationalizing PROTAC-Mediated Ternary Complex Formation Using Rosetta
Bai, N., Miller, S. A., Andrianov, G. V., Yates, M., Kirubakaran, P., & Karanicolas, J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01451 · Cited by 157
Metadata
- Authors
- Imrie, F., Bradley, A. R., van der Schaar, M., & Deane, C. M.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-03-20
- Volume
- 60
- Issue
- 4
- Pages
- 1983-1995
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W3011847211
- Cited by
- 258
- References
- 7
- FWCI
- 15.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
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