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Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP.
Chapter 5 - constant electromagnetic fields
LANDAU, L. and LIFSHITZ, E · 1975
Earlier work this paper cites.
A cluster separation measure
Davies, D. L. and Bouldin, D. W · 1979
Earlier work this paper cites.
Concepts and applications of molecular similarity
Johnson, M. A., Maggiora, G. M., et al · 1990
Earlier work this paper cites.
Defining molecular similarity and complementarity for drug design , pp. 1–23
Dean, P. M · 1995
Earlier work this paper cites.
Comparison of conformer distributions in the crystalline state with conformational energies calculated by ab initio techniques
Allen, F. H., Harris, S. E., and Taylor, R · 1996
Earlier work this paper cites.
Chemical similarity searching
Willett, P., Barnard, J. M., and Downs, G. M · 1998
Earlier work this paper cites.
Efficacy and safety of a specific inhibitor of the bcr-abl tyrosine kinase in chronic myeloid leukemia
Druker, B. J., Talpaz, M., Resta, D. J., Peng, B., Buchdunger, E., Ford, J. M., Lydon, N. B., Kantarjian, H., Capdeville, R., Ohno-Jones, S., et al · 2001
Earlier work this paper cites.
Fragment-based drug discovery
Erlanson, D. A., McDowell, R. S., and O’Brien, T · 2004
Earlier work this paper cites.
Advances in the structural biology, design and clinical development of bcr-abl kinase inhibitors for the treatment of chronic myeloid leukaemia
Manley, P. W., Cowan-Jacob, S. W., and Mestan, J · 2005
Earlier work this paper cites.
Small molecule conformational preferences derived from crystal structure data. a medicinal chemistry focused analysis
Brameld, K. A., Kuhn, B., Reuter, D. C., and Stahl, M · 2008
Earlier work this paper cites.
On the art of compiling and using’drug-like’chemical fragment spaces
Degen, J., Wegscheid-Gerlach, C., Zaliani, A., and Rarey, M · 2008
Earlier work this paper cites.
Visualizing data using t-sne
Van der Maaten, L. and Hinton, G · 2008
Earlier work this paper cites.
The rise of fragment-based drug discovery
Murray, C. W. and Rees, D. C · 2009
Earlier work this paper cites.
Nilotinib: a second-generation tyrosine kinase inhibitor for chronic myeloid leukemia
Breccia, M. and Alimena, G · 2010
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.
Openmm 4: a reusable, extensible, hardware independent library for high performance molecular simulation
Eastman, P., Friedrichs, M. S., Chodera, J. D., Radmer, R. J., Bruns, C. M., Ku, J. P., Beauchamp, K. A., Lane, T. J., Wang, L.-P., Shukla, D., et al · 2013
Earlier work this paper cites.
Quantum chemistry: an introduction
Kauzmann, W · 2013
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.
The Differential Calculus of Functions of Several Variables , pp. 427–543
Zorich, V. A · 2015
Earlier work this paper cites.
Twenty years on: the impact of fragments on drug discovery
Erlanson, D. A., Fesik, S. W., Hubbard, R. E., Jahnke, W., and Jhoti, H · 2016
Earlier work this paper cites.
Machine learning of accurate energy-conserving molecular force fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., and Müller, K.-R · 2017
Earlier work this paper cites.
Pubchemqc project: a large-scale first-principles electronic structure database for data-driven chemistry
Nakata, M. and Shimazaki, T · 2017
Earlier work this paper cites.
Towards exact molecular dynamics simulations with machine-learned force fields
Chmiela, S., Sauceda, H. E., Müller, K.-R., and Tkatchenko, A · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 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.
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.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Cited alongside, same era.
Chemberta: large-scale self-supervised pretraining for molecular property prediction
Chithrananda, S., Grand, G., and Ramsundar, B · 2020
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al · 2020
Cited alongside, same era.
Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2020
Cited alongside, same era.
3D infomax improves gnns for molecular property prediction
Stärk, H., Beaini, D., Corso, G., Tossou, P., Dallago, C., Günnemann, S., and Liò, P · 2022
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Pemp: Leveraging physics properties to enhance molecular property prediction
Sun, Y., Chen, Y., Ma, W., Huang, W., Liu, K., Ma, Z., Ma, W.-Y., and Lan, Y · 2022
Later among the works it cites.
Exploring the equivalence of siamese self-supervised learning via a unified gradient framework
Tao, C., Wang, H., Zhu, X., Dong, J., Song, S., Huang, G., and Dai, J · 2022
Later among the works it cites.
Torchmd-net: equivariant transformers for neural network based molecular potentials
Thölke, P. and De Fabritiis, G · 2022
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Mole-bert: Rethinking pre-training graph neural networks for molecules
Xia, J., Zhao, C., Hu, B., Gao, Z., Tan, C., Liu, Y., Li, S., and Li, S. Z · 2022
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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.
What makes for good views for contrastive learning?
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., and Isola, P · 2020
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
Cited alongside, same era.
Exploring simple siamese representation learning
Chen, X. and He, K · 2021
Cited alongside, same era.
Simple gnn regularisation for 3D molecular property prediction and beyond
Godwin, J., Schaarschmidt, M., Gaunt, A., Sanchez-Gonzalez, A., Rubanova, Y., Velivckovi’c, P., Kirkpatrick, J., and Battaglia, P. W · 2021
Cited alongside, same era.
Provable guarantees for self-supervised deep learning with spectral contrastive loss
HaoChen, J. Z., Wei, C., Gaidon, A., and Ma, T · 2021
Cited alongside, same era.
Long-term outcomes with frontline nilotinib versus imatinib in newly diagnosed chronic myeloid leukemia in chronic phase: Enestnd 10-year analysis
Kantarjian, H. M., Hughes, T. P., Larson, R. A., Kim, D.-W., Issaragrisil, S., le Coutre, P., Etienne, G., Boquimpani, C., Pasquini, R., Clark, R. E., et al · 2021
Cited alongside, same era.
scbert as a large-scale pretrained deep language model for cell type annotation of single-cell rna-seq data
Yang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., Lu, H., and Yao, 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
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The hidden uniform cluster prior in self-supervised learning
Assran, M., Balestriero, R., Duval, Q., Bordes, F., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., and Ballas, N · 2023
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A cookbook of self-supervised learning
Balestriero, R., Ibrahim, M., Sobal, V., Morcos, A., Shekhar, S., Goldstein, T., Bordes, F., Bardes, A., Mialon, G., Tian, Y., et al · 2023
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Towards foundational models for molecular learning on large-scale multi-task datasets
Beaini, D., Huang, S., Cunha, J. A., Moisescu-Pareja, G., Dymov, O., Maddrell-Mander, S., McLean, C., Wenkel, F., Müller, L., Mohamud, J. H., et al · 2023
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Accurate global machine learning force fields for molecules with hundreds of atoms
Chmiela, S., Vassilev-Galindo, V., Unke, O. T., Kabylda, A., Sauceda, H. E., Tkatchenko, A., and Müller, K.-R · 2023
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Molecule joint auto-encoding: Trajectory pretraining with 2d and 3d diffusion
Du, W., Chen, J., Zhang, X., Ma, Z., and Liu, S · 2023
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On the duality between contrastive and non-contrastive self-supervised learning
Garrido, Q., Chen, Y., Bardes, A., Najman, L., and Lecun, Y · 2023
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Towards the generalization of contrastive self-supervised learning
Huang, W., Yi, M., Zhao, X., and Jiang, Z · 2023
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Energy-motivated equivariant pretraining for 3d molecular graphs
Jiao, R., Han, J., Huang, W., Rong, Y., and Liu, Y · 2023
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Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollár, P., and Girshick, R · 2023
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Conformations and physicochemical properties of biological ligands in various environments, 2023
Le Questel, J.-Y · 2023
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One transformer can understand both 2D & 3D molecular data
Luo, S., Chen, T., Xu, Y., Zheng, S., Liu, T.-Y., Wang, L., and He, D · 2023
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OpenAI · 2023
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Contrast with reconstruct: Contrastive 3D representation learning guided by generative pretraining
Qi, Z., Dong, R., Fan, G., Ge, Z., Zhang, X., Ma, K., and Yi, L · 2023
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Denoise pretraining on nonequilibrium molecules for accurate and transferable neural potentials
Wang, Y., Xu, C., Li, Z., and Barati Farimani, A · 2023
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Unified molecular modeling via modality blending
Yu, Q., Zhang, Y., Ni, Y., Feng, S., Lan, Y., Zhou, H., and Liu, J · 2023
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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
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Sliced denoising: A physics-informed molecular pre-training method
Ni, Y., Feng, S., Ma, W.-Y., Ma, Z.-M., and Lan, Y · 2024
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