The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion Model
Open accessPreprint — published as 10.1021/acs.jcim.3c01466 · Paper page
Abstract
In molecular optimization, one popular way is R-groups decoration on molecular scaffolds, and many efforts have been put to generate R-groups based on deep generative models. However, these methods mostly use information of known binding ligands, without fully utilizing target structure information. In this study, we proposed a new method, DiffDec, to involve three-dimensional pocket constraints by a modified diffusion technique for optimizing molecules through molecular scaffold decoration. For an end-to-end generation of R-groups with different sizes, we designed a novel fake atom mechanism. DiffDec was shown able to generate structure-aware R-groups, and simultaneously generate multiple R-groups for one scaffold on different growth anchors. The growth anchors could be provided by users or automatically determined by our model. DiffDec achieved R-group recovery rates of 69.67% and 45.34% in the single and multiple R-group decoration tasks, respectively, and these values were significantly higher than competing methods (37.33% and 26.85%). According to the molecular docking study, our decorated molecules obtained better average binding affinity than baseline methods. The docking pose analysis revealed that DiffDec could decorate scaffolds with R-groups that exhibited improved binding affinities and more favourable interactions with the pocket. These results demonstrated the potential and applicability of DiffDec in real-world scaffold decoration for molecular optimization.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Flagged first, then by citations
Research works printed but not linked: 44
- 10.1007/s12272-015-0640-5 · DOI · Paper page
- Yu, W. ; MacKerell, A. D. Computer-aided drug design methods. Antibiotics: methods and protocols 2017, 85–106. · DOI
- 2010, Pharmacophore-based virtual screening: a review of recent applications, Expert opinion on drug discovery, 5, 205, 10.1517/17460441003592072 · DOI
- 2020, Applications of quantitative structure-Activity relationships (QSAR) based virtual screening in drug design: a review, Mini Reviews in Medicinal Chemistry, 20, 1375, 10.2174/1389557520666200429102334 · DOI
- 2011, Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach, Current computer-aided drug design, 7, 10, 10.2174/157340911793743547 · DOI
- 2018, Application of generative autoencoder in de novo molecular design, Molecular informatics, 37, 1700123, 10.1002/minf.201700123 · DOI · Paper page
- 2019, Advances and challenges in deep generative models for de novo molecule generation, Wiley Interdisciplinary Reviews: Computational Molecular Science, 9, e1395
- 2018, Generating focused molecule libraries for drug discovery with recurrent neural networks, ACS central science, 4, 120, 10.1021/acscentsci.7b00512 · DOI · Paper page
- 2020, Scaffold-constrained molecular generation, Journal of Chemical Information and Modeling, 60, 5637, 10.1021/acs.jcim.0c01015 · DOI
- 2021, Meta learning for low-resource molecular optimization, Journal of Chemical Information and Modeling, 61, 1627, 10.1021/acs.jcim.0c01416 · DOI
- 2022, Accelerated rational PROTAC design via deep learning and molecular simulations, Nature Machine Intelligence, 4, 739, 10.1038/s42256-022-00527-y · DOI
- 2022, DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design, Journal of Chemical Information and Modeling, 62, 5907, 10.1021/acs.jcim.2c00982 · DOI
- 10.1162/neco.1997.9.8.1735 · DOI · Paper page
- Vaswani, A. ; Shazeer, N. ; Parmar, N. ; Uszkoreit, J. ; Jones, L. ; Gomez, A. N. ; Kaiser, Ł. ; Polosukhin, I. Attention is all you need. Advances in neural information processing systems 2017, 30 .
- 2019, DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning, Journal of chemical information and modeling, 60, 77
- 2020, Scaffold-based molecular design with a graph generative model, Chemical science, 11, 1153, 10.1039/C9SC04503A · DOI
- 2021, A deep generative model for molecule optimization via one fragment modification, Nature machine intelligence, 3, 1040, 10.1038/s42256-021-00410-2 · DOI
- 2021, E (n) Equivariant Normalizing Flows, Advances in Neural Information Processing Systems, 34, 4181
- 10.1111/j.1476-5381.2010.01127.x · DOI · Paper page
- 2020, SyntaLinker: automatic fragment linking with deep conditional transformer neural networks, Chemical science, 11, 8312, 10.1039/D0SC03126G · DOI
- 2021, LibINVENT: reaction-based generative scaffold decoration for in silico library design, Journal of Chemical Information and Modeling, 62, 2046
- 10.1021/ci00057a005 · DOI · Paper page
- 10.3390/molecules200713384 · DOI
- Rudrapal, M. ; Khairnar, S. J. ; Jadhav, A. G. Drug repurposing (DR): an emerging approach in drug discovery. Drug repurposing-hypothesis, molecular aspects and therapeutic applications 2020, 10 . · DOI
- Drenth, J. Principles of protein X-ray crystallography; Springer Science & Business Media, 2007.
- 10.1016/j.tibs.2014.10.005 · DOI
- 10.1126/science.ade2574 · DOI · Paper page
- 10.1016/j.neuron.2018.08.011 · DOI
- Morris, G. M. ; Lim-Wilby, M. Molecular docking. Molecular modeling of proteins 2008, 365–382. · DOI
- Peng, X. ; Luo, S. ; Guan, J. ; Xie, Q. ; Peng, J. ; Ma, J. Pocket2mol: Efficient molecular sampling based on 3d protein pockets. International Conference on Machine Learning. 2022; pp 17644–17655.
- Zhang, Z. ; Min, Y. ; Zheng, S. ; Liu, Q. Molecule generation for target protein binding with structural motifs. The Eleventh International Conference on Learning Representations. 2023.
- Aksan, E. ; Kaufmann, M. ; Cao, P. ; Hilliges, O. A spatio-temporal transformer for 3d human motion prediction. 2021 International Conference on 3D Vision (3DV). 2021; pp 565–574. · DOI
- Sohl-Dickstein, J. ; Weiss, E. ; Maheswaranathan, N. ; Ganguli, S. Deep unsupervised learning using nonequilibrium thermodynamics. International Conference on Machine Learning. 2015; pp 2256–2265.
- 10.1021/acs.jcim.0c00411 · DOI
- 10.1038/nbt.3988 · DOI · Paper page
- 10.48550/ARXIV.2006.11239 · DOI · Paper page
- 10.48550/ARXIV.2107.00630 · DOI · Paper page
- Hoogeboom, E. ; Satorras, V. G. ; Vignac, C. ; Welling, M. Equivariant diffusion for molecule generation in 3d. International Conference on Machine Learning. 2022; pp 8867–8887.
- Satorras, V. G. ; Hoogeboom, E. ; Welling, M. E (n) equivariant graph neural networks. International conference on machine learning. 2021; pp 9323–9332.
- 2016, Rdkit: Open-source cheminformatics software, 149, 650
- 2015, Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?, Journal of cheminformatics, 7, 1
- 10.1093/bioinformatics/btv082 · DOI
- 2015, Structure of the ABL2/ARG kinase in complex with dasatinib, Acta Crystallographica Section F: Structural Biology Communications, 71, 443
- 2011, Open Babel: An open chemical toolbox, Journal of cheminformatics, 3, 1
Metadata
- Authors
- Xie, J., Chen, S., Lei, J., & Yang, Y.
- Affiliations
- Sun Yat-sen University (CN)
- Corresponding author
- Lei, Jinping
- Yang, Yuedong
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- openRxiv
- Published
- 2023-10-10
- Volume
- 64
- Issue
- 7
- Pages
- 2554-2564
- ISSN
- 1549-9596 (ISSN-L)
- OpenAlex
- W4391187294
- Cited by
- 31
- References
- 1
- Citations by year
- 2026: 13 · 2025: 11 · 2024: 7
- FWCI
- 6.56
- Type
- journal article
- Language
- en
- Open access
- green
- dx.doi.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Griffith Research Online (submittedVersion, other-oa)
- Funders
- National Natural Science Foundation of China (2020YFB0204803, 82003651)
- National Key Research and Development Program of China (2023YFF1204900)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- diffusion models, binding affinity, molecular docking, molecular optimization
- MeSH terms
- Quantitative Structure-Activity Relationship, Molecular Docking Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
