Fetching the paper…
Reading the bibliography…
Molecular pretrained representations (MPR) has emerged as a powerful approach for addressing the challenge of limited supervised data in applications such as drug discovery and material design.
Local density functional theory of atoms and molecules
Parr, R. G., Gadre, S. R., and Bartolotti, L. J · 1979
Earlier work this paper cites.
The quantum theory of fields , volume 2
Weinberg, S · 1995
Earlier work this paper cites.
Random features for large-scale kernel machines
Rahimi, A. and Recht, B · 2007
Earlier work this paper cites.
Quantum field theory in a nutshell , volume 7
Zee, A · 2010
Earlier work this paper cites.
Molecular quantum mechanics
Atkins, P. W. and Friedman, R. S · 2011
Earlier work this paper cites.
Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
Ruddigkeit, L., Van Deursen, R., Blum, L. C., and Reymond, J.-L · 2012
Earlier work this paper cites.
Modern quantum chemistry: introduction to advanced electronic structure theory
Szabo, A. and Ostlund, N. S · 2012
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and Von Lilienfeld, O. A · 2014
Earlier work this paper cites.
Inchi, the iupac international chemical identifier
Heller, S. R., McNaught, A., Pletnev, I., Stein, S., and Tchekhovskoi, D · 2015
Earlier work this paper cites.
Electronic spectra from tddft and machine learning in chemical space
Ramakrishnan, R., Hartmann, M., Tapavicza, E., and Von Lilienfeld, O. A · 2015
Earlier work this paper cites.
Zinc 15–ligand discovery for everyone
Sterling, T. and Irwin, J. J · 2015
Earlier work this paper cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Earlier work this paper cites.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A · 2017
Earlier work this paper cites.
Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery
Xu, Z., Wang, S., Zhu, F., and Huang, J · 2017
Earlier work this paper cites.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
Earlier work this paper cites.
Moleculenet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2018
Earlier work this paper cites.
Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2019
Earlier work this paper cites.
Smiles-bert: large scale unsupervised pre-training for molecular property prediction
Wang, S., Guo, Y., Wang, Y., Sun, H., and Huang, J · 2019
Cited alongside, same era.
Directional message passing for molecular graphs
Gasteiger, J., Groß, J., and Günnemann, S · 2020
Cited alongside, same era.
Self-supervised graph transformer on large-scale molecular data
Rong, Y., Bian, Y., Xu, T., Xie, W., Wei, Y., Huang, W., and Huang, J · 2020
Cited alongside, same era.
Gemnet: Universal directional graph neural networks for molecules
Gasteiger, J., Becker, F., and Günnemann, S · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Geodiff: A geometric diffusion model for molecular conformation generation
Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J · 2022
Later among the works it cites.
Pre-training via denoising for molecular property prediction
Zaidi, S., Schaarschmidt, M., Martens, J., Kim, H., Teh, Y. W., Sanchez-Gonzalez, A., Battaglia, P., Pascanu, R., and Godwin, J · 2022
Later among the works it cites.
Vision transformers need registers
Darcet, T., Oquab, M., Mairal, J., and Bojanowski, P · 2023
Later among the works it cites.
Prospective validation of machine learning algorithms for absorption, distribution, metabolism, and excretion prediction: An industrial perspective
Fang, C., Wang, Y., Grater, R., Kapadnis, S., Black, C., Trapa, P., and Sciabola, S · 2023
Later among the works it cites.
Fractional denoising for 3d molecular pre-training
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
An effective self-supervised framework for learning expressive molecular global representations to drug discovery
Li, P., Wang, J., Qiao, Y., Chen, H., Yu, Y., Yao, X., Gao, P., Xie, G., and Song, S · 2021
Cited alongside, same era.
Pre-training molecular graph representation with 3d geometry
Liu, S., Wang, H., Liu, W., Lasenby, J., Guo, H., and Tang, J · 2021
Cited alongside, same era.
Learning gradient fields for molecular conformation generation
Shi, C., Luo, S., Xu, M., and Tang, J · 2021
Cited alongside, same era.
Learning neural generative dynamics for molecular conformation generation
Xu, M., Luo, S., Bengio, Y., Peng, J., and Tang, J · 2021
Cited alongside, same era.
Multimodal virtual point 3d detection
Yin, T., Zhou, X., and Krähenbühl, P · 2021
Cited alongside, same era.
FlashAttention: Fast and memory-efficient exact attention with IO-awareness
Dao, T., Fu, D. Y., Ermon, S., Rudra, A., and Ré, C · 2022
Cited alongside, same era.
Geometry-enhanced molecular representation learning for property prediction
Fang, X., Liu, L., Lei, J., He, D., Zhang, S., Zhou, J., Wang, F., Wu, H., and Wang, H · 2022
Cited alongside, same era.
Feng, S., Ni, Y., Lan, Y., Ma, Z.-M., and Ma, W.-Y · 2023
Later among the works it cites.
Energy-motivated equivariant pretraining for 3d molecular graphs
Jiao, R., Han, J., Huang, W., Rong, Y., and Liu, Y · 2023
Later among the works it cites.
Dense voxel fusion for 3d object detection
Mahmoud, A., Hu, J. S., and Waslander, S. L · 2023
Later among the works it cites.
Denoise pretraining on nonequilibrium molecules for accurate and transferable neural potentials
Wang, Y., Xu, C., Li, Z., and Barati Farimani, A · 2023
Later among the works it cites.
Virtual sparse convolution for multimodal 3d object detection
Wu, H., Wen, C., Shi, S., Li, X., and Wang, C · 2023
Later among the works it cites.
Uni-mol: A universal 3d molecular representation learning framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., and Ke, G · 2023
Later among the works it cites.
Accurate structure prediction of biomolecular interactions with alphafold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al · 2024
Later among the works it cites.
Geometry-enhanced pretraining on interatomic potentials
Cui, T., Tang, C., Su, M., Zhang, S., Li, Y., Bai, L., Dong, Y., Gong, X., and Ouyang, W · 2024
Later among the works it cites.
Let’s think dot by dot: Hidden computation in transformer language models
Pfau, J., Merrill, W., and Bowman, S. R · 2024
Later among the works it cites.
Roformer: Enhanced transformer with rotary position embedding
Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., and Liu, Y · 2024
Later among the works it cites.
We need better benchmarks for machine learning in drug discovery, 2023
Walters, P · 2024
Later among the works it cites.
Mol-ae: Auto-encoder based molecular representation learning with 3d cloze test objective
Yang, J., Zheng, K., Long, S., Nie, Z., Zhang, M., Dai, X., Ma, W.-Y., and Zhou, H · 2024
Later among the works it cites.
Multimodal molecular pretraining via modality blending
Yu, Q., Zhang, Y., Ni, Y., Feng, S., Lan, Y., Zhou, H., and Liu, J · 2024
Later among the works it cites.