De Novo Molecule Design Through the Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites
Open accessPublished version of a preprint
Abstract
molecule design through the molecular generative model has gained increasing attention in recent years. Here, a novel generative model was proposed by integrating the three-dimensional (3D) structural information of the protein binding pocket into the conditional RNN (cRNN) model to control the generation of drug-like molecules. In this model, the composition of the protein binding pocket is effectively characterized through a coarse-grain strategy and the 3D information of the pocket can be represented by the sorted eigenvalues of the Coulomb matrix (EGCM) of the coarse-grained atoms composing the binding pocket. In current work, we used our EGCM method and a previously reported binding pocket descriptor, DeeplyTough, to train cRNN models and evaluated their performance. It has been shown that the model trained with the constraint of protein environment information has a clear tendency on generating compounds with higher similarity to the original X-ray-bound ligand than the normal RNN model and also better docking scores. Our results demonstrate the potential application of the controlled generative model for the targeted molecule generation and guided exploration on the drug-like chemical space.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
These algorithms can be interfaced with docking calculations to design small molecules with high docking scores.
“Of note, these algorithms can be interfaced with docking calculations in order to design small molecules with high docking scores.155,156”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [155]
Cites
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412 · see citing works
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826 · see citing works
Harris, C., Millman, K., van der Walt, S., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N., et al. (2020) · RePEc: Research Papers in Economics · Cited by 17,844 · see citing works
Generative Adversarial Networks: An Overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540 · 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
Innovation in the pharmaceutical industry: New estimates of R&D costs
DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016) · Journal of Health Economics · doi:10.1016/j.jhealeco.2016.01.012 · Cited by 3,359 · see citing works
Drug Discovery: A Historical Perspective
Drews, J. (2000) · Science · doi:10.1126/science.287.5460.1960 · Cited by 2,768 · see citing works
Goodford, P. J. (1985) · Journal of Medicinal Chemistry · doi:10.1021/jm00145a002 · Cited by 2,642 · see citing works
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312 · see citing works
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Zhang, L., Han, J., Wang, H., Car, R., & Weinan, E. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.143001 · Cited by 2,267 · see citing works
(2018) · De Gruyter eBooks · doi:10.1515/9783110542400 · Cited by 1,929 · see citing works
Junction Tree Variational Autoencoder for Molecular Graph GenerationPreprint
Jin, W.-G., Barzilay, R., & Jaakkola, T. (2018) · International Conference on Machine Learning · doi:10.48550/arxiv.1802.04364 · Cited by 1,783 · 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
Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018
Wouters, O. J., McKee, M., & Luyten, J. (2020) · JAMA · doi:10.1001/jama.2020.1166 · Cited by 1,531 · see citing works
Tutorial on Variational Autoencoders
Doersch, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05908 · Cited by 1,470 · see citing works
Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,358 · see citing works
Self-citationQ-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites
Laurie, A. T. R., & Jackson, R. M. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti315 · Cited by 1,049 · see citing works
Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b00831 · Cited by 815 · see citing works
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
Simonovsky, M., & Komodakis, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01418-6_41 · Cited by 812 · see citing works
Butina, D. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci9803381 · Cited by 620 · see citing works
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., von Lilienfeld, O. A., Tkatchenko, A., & Müller, K. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400195d · Cited by 615 · see citing works
Grossberg, S. (2013) · Scholarpedia · doi:10.4249/scholarpedia.1888 · Cited by 602 · see citing works
Computer-Aided Drug Design Methods
Yu, W., & MacKerell, A. D. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6634-9_5 · Cited by 599 · see citing works
Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes
An, J., Totrov, M. M., & Abagyan, R. A. (2005) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400159-mcp200 · Cited by 408 · see citing works
Randomized SMILES strings improve the quality of molecular generative models
Arús‐Pous, J., Johansson, S., Prykhodko, O., Bjerrum, E. J., Tyrchan, C., Reymond, J., Chen, H., & Engkvist, O. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0393-0 · Cited by 384 · see citing works
Self-citationMulti-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
Levitt, D. G., & Banaszak, L. J. (1992) · Journal of Molecular Graphics · doi:10.1016/0263-7855(92)80074-n · Cited by 344 · see citing works
Acharya, C., Coop, A., Polli, J. E., & MacKerell, A. D. (2010) · Current Computer - Aided Drug Design · doi:10.2174/157340911793743547 · Cited by 343 · see citing works
sc-PDB: a 3D-database of ligandable binding sites—10 years on
Desaphy, J., Bret, G., Rognan, D., & Kellenberger, E. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku928 · Cited by 301 · see citing works
MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities
Li, S., Wan, F., Shu, H., Jiang, T., Zhao, D. Y., & Zeng, J. (2020) · Cell Systems · doi:10.1016/j.cels.2020.03.002 · Cited by 245 · 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
Self-citation86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
Lu, D., Wang, H., Chen, M., Lin, L., Car, R., Weinan, E., Jia, W., & Zhang, L. (2020) · Computer Physics Communications · doi:10.1016/j.cpc.2020.107624 · Cited by 230 · see citing works
Morris, R. J., Najmanovich, R., Kahraman, A., et al. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti337 · Cited by 190 · see citing works
AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics
Casalino, L., Dommer, A. C., Gaieb, Z., de Barros, E. P., Sztain, T., Ahn, S.-H., Trifan, A., Brace, A., Bogetti, A. T., Clyde, A., et al. (2021) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420211006452 · Cited by 187 · see citing works
Representing molecule-surface interactions with symmetry-adapted neural networks
Behler, J., Lorenz, S., & Reuter, K. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2746232 · Cited by 156 · see citing works
From 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
PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties
Borrel, A., Regad, L., Xhaard, H., Petitjean, M., & Camproux, A. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci5006004 · Cited by 118 · see citing works
Harrirou, H. H., Zhang, C., & Lemmin, T. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00075 · Cited by 116 · see citing works
Representing molecule-surface interactions with symmetry-adapted neural networks
Behler, J., Lorenz, S., & Reuter, K. (2007) · Max Planck Institute for Plasma Physics · Cited by 100 · see citing works
DeeplyTough: Learning Structural Comparison of Protein Binding Sites
Simonovsky, M., & Meyers, J. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00554 · Cited by 98 · see citing works
Bitencourt‐Ferreira, G., & de Azevedo, W. F. (2018) · Biophysical Chemistry · doi:10.1016/j.bpc.2018.05.010 · Cited by 93 · see citing works
Protein Pockets: Inventory, Shape, and Comparison
Coleman, R. G., & Sharp, K. A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900397t · Cited by 88 · see citing works
Ehrt, C., Brinkjost, T., & Koch, O. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006483 · Cited by 68 · see citing works
LigVoxel: inpainting binding pockets using 3D-convolutional neural networks
Škalič, M., Varela‐Rial, A., Jiménez-Luna, J., Martínez-Rosell, G., & Fabritiis, G. D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty583 · Cited by 63 · see citing works
Binding Affinity Prediction by Pairwise Function Based on Neural Network
Zhu, F., Zhang, X., Allen, J., et al. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00026 · Cited by 61 · see citing works
Bennett, W. F., He, S., Bilodeau, C., Jones, D., Sun, D., Kim, H., Allen, J., Lightstone, F. C., & Ingólfsson, H. I. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00318 · Cited by 56 · see citing works
Linear Algebra and Its Applications, 4th Edition
Strang, G. (1976) · Cited by 46 · see citing works
Graph Neural Network Architecture Search for Molecular Property Prediction
Jiang, S., & Balaprakash, P. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378060 · Cited by 45 · see citing works
Latent Molecular Optimization for Targeted Therapeutic DesignPreprint
Aumentado‐Armstrong, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.02032 · Cited by 40 · see citing works
NeVAE: A Deep Generative Model for Molecular Graphs
Samanta, B., De, A., Jana, G., Chattaraj, P. K., Ganguly, N., & Gomez-Rodriguez, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011110 · Cited by 39 · see citing works
A novel protein descriptor for the prediction of drug binding sites
Jiang, M., Li, Z., Bian, Y., & Wei, Z. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3058-0 · Cited by 31 · see citing works
Editorial: Chemoinformatics Approaches to Structure- and Ligand-Based Drug Design
Ferreira, L. L. G., & Andricopulo, A. D. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01416 · Cited by 21 · see citing works
Naderi, M., Govindaraj, R. G., & Bryliński, M. (2018) · GigaScience · doi:10.1093/gigascience/giy091 · Cited by 18 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bai, Q., Liu, S., Tian, Y., Xu, T., Banegas‐Luna, A. J., Pérez‐Sánchez, H., Huang, J., Liu, H., & Yao, X. (2022) · WIREs Computational Molecular Science · doi:10.1002/wcms.1581 · Cited by 156 · see citing works
AlphaFold2-aware protein–DNA binding site prediction using graph transformer
Yuan, Q., Chen, S., Rao, J., Zheng, S., Zhao, H., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab564 · Cited by 137 · see citing works
RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design
Wang, M., Hsieh, C.-Y., Wang, J., Wang, D., Weng, G., Shen, C., Yao, X., Bing, Z., Li, H., Cao, D., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00732 · Cited by 99 · see citing works
Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
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
- Xu, M., Ran, T., & Chen, H.
- Affiliations
- Guangzhou Regenerative Medicine and Health Guangdong Laboratory (CN)
- Corresponding author
- Chen, Hongming
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-07-01
- Volume
- 61
- Issue
- 7
- Pages
- 3240-3254
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W3121082297
- Cited by
- 85
- References
- 53
- Citations by year
- 2026: 11 · 2025: 21 · 2024: 19 · 2023: 13 · 2022: 15 · 2021: 6
- FWCI
- 6.98
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Royal College of Surgeons in Ireland (submittedVersion, cc-by-nc-nd)
- Funders
- Xtal Pi Inc.
- 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 molecule design, conditional generative models, protein binding sites, protein pockets, Coulomb matrix, coarse-grained representation, drug-like molecules, chemical space exploration
- MeSH terms
- Binding Sites, Models, Molecular, Protein Binding, Proteins, Quantitative Structure-Activity Relationship
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
