Fetching the paper…
Reading the bibliography…
With the emergence of Transformer architectures and their powerful understanding of textual data, a new horizon has opened up to predict the molecular properties based on text description.
Weininger, D. SMILES, a Chemical Language and Information System. 1. Introduction to Methodology and Enciding Rules. J. Chem. Inf. Comp. Sci. 1988
1988
Earlier work this paper cites.
Webster, J. J.; Kit, C. Tokenization as the initial phase in NLP. COLING 1992 volume 4: The 14th international conference on computational linguistics. 1992
1992
Earlier work this paper cites.
Rogers, D.; Hahn, M. Extended-connectivity fingerprints. Journal of chemical information and modeling 2010
2010
Earlier work this paper cites.
Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. 2010
2010
Earlier work this paper cites.
Bartók, A. P.; Kondor, R.; Csányi, G. On representing chemical environments. Phys. Rev. B 2013
2013
Earlier work this paper cites.
Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. Convolutional Networks on Graphs for Learning Molecular Fingerprints. Advances in Neural Information Processing Systems. 2015
2015
Earlier work this paper cites.
Kim, S.; Thiessen, P. A.; Bolton, E. E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B. A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S. H. PubChem Substance and Compound databases. Nucleic Acids Research 2015
2015
Earlier work this paper cites.
Sterling, T.; Irwin, J. J. ZINC 15 – Ligand Discovery for Everyone. Journal of Chemican Information and Modeling 2015
2015
Earlier work this paper cites.
Zhu, Y.; Kiros, R.; Zemel, R.; Salakhutdinov, R.; Urtasun, R.; Torralba, A.; Fidler, S. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. Proceedings of the IEEE international conference on computer vision. 2015; pp 19–27
2015
Earlier work this paper cites.
Huang, B.; von Lilienfeld, O. A. Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity. The Journal of Chemical Physics 2016
2016
Earlier work this paper cites.
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; Dahl, G. E. Neural Message Passing for Quantum Chemistry. Proceedings of the 34th International Conference on Machine Learning. 2017; pp 1263–1272
2017
Earlier work this paper cites.
Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Research 2017
2017
Earlier work this paper cites.
Vaswani, A.; Parmar, N. S. N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; Polosukhin, I. Attention is All you Need. Advances in Neural Information Processing Systems. 2017
2017
Earlier work this paper cites.
Altae-Tran, H.; Ramsundar, B.; Pappu, A. S.; Pande, V. Low data drug discovery with one-shot learning. ACS central science 2017
2017
Earlier work this paper cites.
Kipf, T. N.; Welling, M. Semi-Supervised Classification with Graph Convolutional Networks. 2017
2017
Earlier work this paper cites.
Schutt, K. T.; Sauceda, H. E.; Kindermans, P.-J.; Tkatchenko, A.; Muller, K.-R. SchNet – A deep learning architecture for molecules and materials. The Journal of Chemical Physics 2018
2018
Earlier work this paper cites.
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. Automatic chemical design using a data-driven continuous representation of molecules. ACS central science 2018
2018
Earlier work this paper cites.
Bjerrum, E. J.; Sattarov, B. Improving Chemical Autoencoder Latent Space and Molecular De novo Generation Diversity with Heteroencoders. 2018
2018
Earlier work this paper cites.
Chmiela, S.; Sauceda, H. E.; Müller, K.-R.; Tkatchenko, A. Towards exact molecular dynamics simulations with machine-learned force fields. Nature communications 2018
2018
Earlier work this paper cites.
Wu, Z.; Ramsundar, B.; Feinberg, E. N.; Gomes, J.; Geniesse, C.; Pappu, A. S.; Leswing, K.; Pande, V. MoleculeNet: a benchmark for molecular machine learning. Chemical Science 2018
2018
Earlier work this paper cites.
Shen, J.; Nicolaou, C. A. Molecular property prediction: recent trends in the era of artificial intelligence. ScienceDirect 2019
2019
Earlier work this paper cites.
Yang, K.; Swanson, K.; Jin, W.; Coley, C.; Eiden, P.; Gao, H.; Guzman-Perez, A.; Hopper, T.; Kelley, B.; Mathea, M.; Palmer, A.; Settels, V.; Jaakkola, T.; Jensen, K.; Barzilay, R. Analyzing Learned Molecular Representations for Property Prediction. Journal of Chemical Information and Modeling 2019
2019
Earlier work this paper cites.
Lu, C.; Liu, Q.; Wang, C.; Huang, Z.; Lin, P.; He, L. Molecular property prediction: A multilevel quantum interactions modeling perspective. Proceedings of the AAAI conference on artificial intelligence. 2019; pp 1052–1060
2019
Earlier work this paper cites.
Sachdev, K.; Gupta, M. K. A comprehensive review of feature based methods for drug target interaction prediction. Journal of Biomedical Informatics 2019
2019
Earlier work this paper cites.
Jacob Devlin, K. L., Ming-Wei Chang; Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. The Journal of Chemical Physics 2019
2019
Cited alongside, same era.
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; Stoyanov, V. RoBERTa: A Robustly Optimized BERT Pretraining Approach. 2019
2019
Cited alongside, same era.
Nathan Brown, M. H. S., Marco Fiscato; Vaucher, A. C. GuacaMol: Benchmarking Models for de Novo Molecular Design. Journal of Chemical Information and Modeling 2019
2019
Cited alongside, same era.
Wang, W.; Gomez-Bombarelli, R. Coarse-graining auto-encoders for molecular dynamics. Computational Materials 2019
2019
Cited alongside, same era.
Zhang, D.; Xia, S.; Zhang, Y. Accurate prediction of aqueous free solvation energies using 3d atomic feature-based graph neural network with transfer learning. Journal of Chemical Information and Modeling 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
Tianyang Lin, X. L., Yuxin Wang; Qiu, X. A survey of transformers. AI Open 2022
2022
Later among the works it cites.
Su, J.; Lu, Y.; Pan, S.; Murtadha, A.; Wen, B.; Liu, Y. ROFORMER: ENHANCED TRANSFORMER WITH ROTARY POSITION EMBEDDING. 2022
2022
Later among the works it cites.
Irwin, R.; Dimitriadis, S.; He, J.; Bjerrum, E. J. Chemformer: a pre-trained transformer for computational chemistry. Machine Learning: Science and Technology 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I. Language Models are Unsupervised Multitask Learners. 2019
2019
Cited alongside, same era.
Xu, K.; Hu, W.; Leskovec, J.; Jegelka, S. How Powerful are Graph Neural Networks? 2019
2019
Cited alongside, same era.
Laurianne David, R. M., Amol Thakkar; Engkvist, O. Molecular representations in AI-driven drug discovery: a review and practical guide. Journal of Cheminformatics 2020
2020
Cited alongside, same era.
Capecchi, A.; Probst, D.; Reymond, J.-L. One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. Journal of cheminformatics 2020
2020
Cited alongside, same era.
Johannes Gasteiger, J. G. . S. G. DIRECTIONAL MESSAGE PASSING FOR MOLECULAR GRAPHS. International Conference on Learning Representations 2020
2020
Cited alongside, same era.
Karamad, M.; Magar, R.; Shi, Y.; Siahrostami, S.; Gates, I. D.; Farimani, A. B. Orbital graph convolutional neural network for material property prediction. Physical Review Materials 2020
2020
Cited alongside, same era.
Katharopoulos, A.; Vyas, A.; Pappas, N.; Fleuret, F. Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention. Proceedings of the 37th International Conference on Machine Learning. 2020; pp 5156–5165
2020
Cited alongside, same era.
2022
Later among the works it cites.
Ahmad, W.; Simon, E.; Chithrananda, S.; Grand, G.; Ramsundar, B. ChemBERTa-2: Towards Chemical Foundation Models. 2022
2022
Later among the works it cites.
Ross, J.; Belgodere, B.; Chenthamarakshan, V.; Padhi, I.; Mroueh, Y.; Das, P. Large-Scale Chemical Language Representations Capture Molecular Structure and Properties. Nature Machine Intelligence 2022
2022
Later among the works it cites.
Wang, Y.; Wang, J.; Cao, Z.; Farimani, A. B. Molecular contrastive learning of representations via graph neural networks. Nature Machine Intelligence 2022
2022
Later among the works it cites.
Trewartha, A.; Walker, N.; Huo, H.; Lee, S.; Cruse, K.; Dagdelen, J.; Dunn, A.; Persson, K. A.; Ceder, G.; Jain, A. Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science. Patterns 2022
2022
Later among the works it cites.
Gupta, T.; Zaki, M.; Krishnan, N. M. A.; Mausam, MatSciBERT: A materials domain language model for text mining and information extraction. Nature Computational Materials 2022
2022
Later among the works it cites.
Magar, R.; Wang, Y.; Barati Farimani, A. Crystal twins: self-supervised learning for crystalline material property prediction. npj Computational Materials 2022
2022
Later among the works it cites.
Ock, J.; Tian, T.; Kitchin, J.; Ulissi, Z. Beyond independent error assumptions in large GNN atomistic models. The Journal of Chemical Physics 2023
2023
Closest in time.
Cheng, A. H.; Cai, A.; Miret, S.; Malkomes, G.; Phielipp, M.; Aspuru-Guzik, A. Group SELFIES: a robust fragment-based molecular string representation. Digital Discovery 2023
2023
Closest in time.
Yüksel, A.; Ulusoy, E.; Ünlü, A.; Doğan, T. SELFormer: molecular representation learning via SELFIES language models. Machine Learning: Science and Technology 2023
2023
Closest in time.
Born, J.; Markert, G.; Janakarajan, N.; Kimber, T. B.; Volkamer, A.; Martínez, M. R.; Manica, M. Chemical representation learning for toxicity prediction. Royal Society of Chemistry 2023
2023
Closest in time.
Cao, Z.; Magar, R.; Wang, Y.; Barati Farimani, A. MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction. Journal of the American Chemical Society 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Xu, C.; Wang, Y.; Barati Farimani, A. TransPolymer: a Transformer-based language model for polymer property predictions. npj Computational Materials 2023
2023
Closest in time.
2023
Closest in time.
Ock, J.; Guntuboina, C.; Farimani, A. B. Catalyst Property Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models. 2023
2023
Closest in time.
OpenAI, GPT-4 Technical Report. 2023
2023
Closest in time.