Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
Open accessPublished version of a preprint
Abstract
Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.
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
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.
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?
The development of ultra-large chemical libraries and generative chemistry methods, particularly those based on Generative Adversarial Networks (GANs), have significantly expanded the possibilities for hit generation and drug discovery.
“The development of ultra-large chemical libraries (94) and generative chemistry methods, particularly those based on Generative Adversarial Networks (GANs), have significantly expanded the possibilities for hit generation and drug discovery.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [93]
The Results and discussion section (page unavailable) says machine-learning models can search large chemical libraries for potential candidates and promote discovery. It does not establish that developing ultra-large libraries expanded hit generation, and the passages found do not address GAN-based generative chemistry. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
Neural Message Passing for Quantum ChemistryPreprint
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,414 · see citing works
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322 · see citing works
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
Svetnik, V., Liaw, A. I., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034160g · Cited by 3,587 · see citing works
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107 · see citing works
Self-citationPaDEL‐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
Analyzing Learned Molecular Representations for Property Prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00237 · Cited by 1,923 · 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
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631 · see citing works
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural NetworksPreprint
Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X. H., Zhou, J., Ma, C., Yu, L., Gai, Y. L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01315 · Cited by 1,252 · see citing works
Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159 · see citing works
ESOL: Estimating Aqueous Solubility Directly from Molecular Structure
Delaney, J. S. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034243x · Cited by 1,137 · see citing works
Xiong, Z., Wang, D., Liu, X., Zhong, F., Wan, X., Li, X., Li, Z. S., Luo, X., Chen, K., Jiang, H., et al. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b00959 · Cited by 1,083 · see citing works
DeepTox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081 · see citing works
Adaptive Graph Convolutional Neural Networks
Li, R., Wang, S., Zhu, F., & Huang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11691 · Cited by 842 · see citing works
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms
Bergstra, J., Yamins, D., & Cox, D. (2013) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-8b375195-003 · Cited by 784 · see citing works
From machine learning to deep learning: progress in machine intelligence for rational drug discovery
Zhang, L., Tan, J., Han, D., & Zhu, H. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.08.010 · Cited by 760 · see citing works
Deep Learning in Drug Discovery
Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723 · see citing works
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584 · see citing works
Byvatov, E., Fechner, U., Sadowski, J., & Schneider, G. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0341161 · Cited by 569 · see citing works
Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships
Sheridan, R. P., Wang, W. M., Liaw, A., Ma, J., & Gifford, E. M. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00591 · Cited by 544 · see citing works
Rodríguez-Pérez, R., & Bajorath, J. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b01101 · Cited by 517 · see citing works
Machine learning methods in chemoinformatics
Mitchell, J. B. O. (2014) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1183 · Cited by 470 · see citing works
Graph convolutional networks for computational drug development and discovery
Sun, M., Zhao, S., Gilvary, C. M., Elemento, O., Zhou, J., & Wang, F. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz042 · Cited by 414 · see citing works
Deep Learning for Drug-Induced Liver Injury
Xu, Y., Dai, Z., Chen, F., Gao, S., Pei, J., & Lai, L. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00238 · Cited by 385 · see citing works
Artificial Intelligence for Drug Toxicity and Safety
Basile, A. O., Yahi, A., & Tatonetti, N. P. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.07.005 · Cited by 385 · see citing works
Classification of Kinase Inhibitors Using a Bayesian Model
Xia, X., Maliski, E. G., Gallant, P., & Rogers, D. O. W. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0303195 · Cited by 352 · see citing works
Lenselink, E. B., ten Dijke, N., Bongers, B., Papadatos, G., van Vlijmen, H. W. T., Kowalczyk, W., IJzerman, A. P., & van Westen, G. J. P. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0232-0 · Cited by 334 · see citing works
Koutsoukas, A., Monaghan, K. J., Li, X., & Huan, J. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0226-y · Cited by 315 · see citing works
Zhang, J., Mucs, D., Norinder, U., & Svensson, F. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00633 · Cited by 276 · see citing works
In silico Prediction of Chemical Ames Mutagenicity
Xu, C., Cheng, F., Chen, L., Du, Z., Li, W., Liu, G., Lee, P. W., & Tang, Y. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300400a · Cited by 267 · see citing works
Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future
Polishchuk, P. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00274 · Cited by 236 · see citing works
Withnall, M., Lindelöf, E., Engkvist, O., & Chen, H. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-019-0407-y · Cited by 232 · see citing works
Wenzel, J., Matter, H., & Schmidt, F. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00785 · Cited by 227 · see citing works
Learning Molecular Representations for Medicinal Chemistry
Chuang, K. V., Gunsalus, L. M., & Keiser, M. J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c00385 · Cited by 227 · see citing works
Multi-task Neural Networks for QSAR PredictionsPreprint
Dahl, G. E., Jaitly, N., & Salakhutdinov, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.1231 · Cited by 217 · see citing works
Zernov, V. V., Balakin, K. V., Ivaschenko, A. A., Savchuk, N., & Плетнев, И. В. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0340916 · Cited by 211 · see citing works
Chen, L., Li, Y., Zhao, Q., et al. (2011) · Molecular Pharmaceutics · doi:10.1021/mp100465q · Cited by 183 · see citing works
Understanding the Roles of the “Two QSARs”
Fujita, T., & Winkler, D. A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00229 · Cited by 166 · see citing works
Tang, B., Kramer, S. T., Fang, M., Qiu, Y., Wu, Z., & Xu, D. H. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0414-z · Cited by 163 · see citing works
Self-citationKorolev, V., Mitrofanov, A., Korotcov, A., & Tkachenko, V. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00587 · Cited by 143 · see citing works
Li, D., Chen, L., Li, Y., et al. (2014) · Molecular Pharmaceutics · doi:10.1021/mp400450m · Cited by 142 · see citing works
Wu, Z., Lei, T., Shen, C., Wang, Z., Cao, D., & Hou, T. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00801 · Cited by 142 · see citing works
Self-citationHou, T., Wang, J., & Li, Y. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci7002076 · Cited by 139 · see citing works
Self-citationPrediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network
Li, X., Xu, Y., Lai, L., & Pei, J. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00110 · Cited by 130 · see citing works
Tian, S., Wang, J., Li, Y., Xu, X., & Hou, T. (2012) · Molecular Pharmaceutics · doi:10.1021/mp300198d · Cited by 118 · see citing works
Self-citationOpportunities and challenges using artificial intelligence in ADME/Tox
Bhhatarai, B., Walters, W. P., Hop, C. E. C. A., Lanza, G., & Ekins, S. (2019) · Nature Materials · doi:10.1038/s41563-019-0332-5 · Cited by 117 · see citing works
Lombardo, F., Obach, R. S., DiCapua, F. M., Bakken, G. A., Lu, J., Potter, D. M., Gao, F., Miller, M. D., & Zhang, Y. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm050200r · Cited by 107 · see citing works
Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space
Sosnin, S., Karlov, D., Tetko, I. V., & Fedorov, M. V. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00685 · Cited by 102 · see citing works
Random Forest Prediction of Mutagenicity from Empirical Physicochemical Descriptors
Zhang, Q.-Y., & Aires‐de‐Sousa, J. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050520j · Cited by 97 · see citing works
Adaptive Graph Convolutional Neural NetworksPreprint
Li, R., Wang, S., Zhu, F., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.03226 · Cited by 94 · see citing works
Jiang, D., Lei, T., Wang, Z., Shen, C., Cao, D., & Hou, T. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00421-y · Cited by 92 · see citing works
Self-citationTrade-Off Between Accuracy and Interpretability for PredictiveIn SilicoModeling
Johansson, U., Sönströd, C., Norinder, U., & Boström, H. (2011) · Future Medicinal Chemistry · doi:10.4155/fmc.11.23 · Cited by 89 · see citing works
Lei, T., Sun, H., Kang, Y., Zhu, F., Liu, H., Zhou, W., Wang, Z., Li, D., Li, Y., & Hou, T. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00631 · Cited by 87 · see citing works
Self-citationRecent Developments of In Silico Predictions of Intestinal Absorption and Oral Bioavailability
Hou, T., Li, Y., Zhang, W., et al. (2009) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620709788489082 · Cited by 86 · see citing works
Self-citationStructural Analysis and Identification of Colloidal Aggregators in Drug Discovery
Yang, Z., Yang, Z., Dong, J. N., Wang, L., Zhang, L., Ding, J., Ding, X., Lü, A., Hou, T., & Cao, D. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00541 · Cited by 68 · see citing works
Self-citationLi, Y., Cong, Y., Feng, G., Zhong, S., Zhang, J. Z. H., Sun, H., & Duan, L. (2018) · Structural Dynamics · doi:10.1063/1.5058172 · Cited by 56 · see citing works
Hop, P., Allgood, B., & Yu, J. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b01144 · Cited by 53 · see citing works
DeepTox: Toxicity prediction using deep learning
Klambauer, G., Unterthiner, T., Mayr, A., et al. (2017) · Toxicology Letters · doi:10.1016/j.toxlet.2017.07.175 · Cited by 47 · see citing works
Large-scale ligand-based predictive modelling using support vector machines
Alvarsson, J., Lampa, S., Schaal, W., Andersson, C. R., Wikberg, J. E. S., & Spjuth, O. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0151-5 · Cited by 46 · see citing works
Ren, Y., Zhou, L., Li, Y., Liu, P. Y., Zhao, B., & Liu, H. (2016) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2016.1229691 · Cited by 37 · see citing works
Virtual Screening of DrugBank Reveals Two Drugs as New BCRP Inhibitors
Montanari, F., Cseke, A., Wlcek, K., et al. (2016) · SLAS DISCOVERY · doi:10.1177/1087057116657513 · Cited by 34 · see citing works
Path-Augmented Graph Transformer NetworkPreprint
Chen, B., Barzilay, R., & Jaakkola, T. (2019) · ChemRxiv · doi:10.26434/chemrxiv.8214422 · Cited by 32 · see citing works
Path-Augmented Graph Transformer NetworkPreprint
Chen, B., Barzilay, R., & Jaakkola, T. (2019) · ChemRxiv · doi:10.26434/chemrxiv.8214422.v1 · Cited by 26 · see citing works
Muegge, I., & Podlogar, B. L. (2001) · Quantitative Structure-Activity Relationships · doi:10.1002/1521-3838(200110)20:3 · Cited by 19 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [26]Flam-Shepherd D, Wu T, Friederich P et al. Neural Message Passing on High Order Paths, arXiv preprint arXiv:2002.10413 · arXiv
- [28]Chen P, Liu W, Hsieh C-Y et al. Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization, arXiv preprint arXiv:.05488 2019
- [30]Shang C, Liu Q, Chen K-S et al. Edge attention-based multi-relational graph convolutional networks, arXiv preprint arXiv:.04944 2018
- [31]Li J, Cai D, He X. Learning graph-level representation for drug discovery, arXiv preprint arXiv:.03741 2017
- [56]Veličković P, Cucurull G, Casanova A et al. Graph attention networks, arXiv preprint arXiv:1710.10903. 2017. · arXiv
Cited by
Sorted by citations
Hopfield Networks is All You Need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., et al. (2020) · arXiv · doi:10.48550/arxiv.2008.02217 · Cited by 236 · see citing works
“Predictive performance (ROCAUC) on test set as reported by Jiang et al. (2020) for 50 random splits”
MethodArtificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works
“and multiple property predictions (92).”
BackgroundModel agnostic generation of counterfactual explanations for molecules
Wellawatte, G. P., Seshadri, A., & White, A. D. (2022) · Chemical Science · doi:10.1039/d1sc05259d · Cited by 143 · see citing works
EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124 · 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
Self-citationRecent advances in AI-based toxicity prediction for drug discovery
Lee, H., Kim, J., Kim, J.-W., & Lee, Y. (2025) · Frontiers in Chemistry · doi:10.3389/fchem.2025.1632046 · Cited by 76 · see citing works
Wozniak, S., Janson, G., & Feig, M. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01682 · Cited by 14 · see citing works
- Authors
- Jiang, D., Wu, Z., Hsieh, C.-Y., Chen, G., Liao, B., Wang, Z., Shen, C., Cao, D., Wu, J., & Hou, T.
- Affiliations
- Zhejiang University (CN)
- Tencent (China) (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- Central South University (CN)
- Corresponding author
- Cao, Dongsheng
- Wu, Jian
- Hou, Tingjun
- ORCID
- Venue
- Journal of Cheminformatics
- Published
- 2021-02-17
- Volume
- 13
- Issue
- 1
- Pages
- 12-12
- ISSN
- 1758-2946 (ISSN-L)
- PMC
- PMC7888189
- OpenAlex
- W3116202926
- Cited by
- 664
- References
- 70
- Citations by year
- 2026: 108 · 2025: 141 · 2024: 163 · 2023: 117 · 2022: 94 · 2021: 40 · 2020: 1
- FWCI
- 54.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Cheminformatics (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Key R&D Program of Zhejiang Province (2020C03010)
- National Natural Science Foundation of China (21575128, 81773632)
- Natural Science Foundation of Zhejiang Province (LZ19H300001)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
- Keywords
- graph neural networks, molecular descriptors, molecular property prediction, drug discovery, virtual screening, HIV, SHAP, computational efficiency, GCN, GAT, SVM, XGBoost, random forest
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
