Morgan, H. L. (1965). The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. Journal of Chemical Documentation, 5(2), 107-113. Link
\nRogers, D., & Hahn, M. (2010). Extended-connectivity fingerprints. Journal of chemical information and modeling, 50(5), 742-754. Link
\nRiniker, S., & Landrum, G. A. (2013). Open-source platform to benchmark fingerprints for ligand-based virtual screening. Journal of cheminformatics, 5(1), 1-17. Link
\nCereto-Massagué, A., Ojeda, M. J., Valls, C., Mulero, M., Garcia-Vallvé, S., & Pujadas, G. (2015). Molecular fingerprint similarity search in virtual screening. Methods, 71, 58-63. Link
\nGilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017). Neural message passing for quantum chemistry. In International conference on machine learning (pp. 1263-1272). PMLR. Link
\nDuvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015). Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems, 28. Link
\nKipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. Link
\nVeličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903. Link
\nXiong, Z., Wang, D., Liu, X., Zhong, F., Wan, X., Li, X., … & Deng, H. (2019). Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. Journal of medicinal chemistry, 63(16), 8749-8760. Link
\nYang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., … & Barzilay, R. (2019). Analyzing learned molecular representations for property prediction. Journal of chemical information and modeling, 59(8), 3370-3388. Link
\nHu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., & Leskovec, J. (2019). Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265. Link
\nGoh, G. B., Hodas, N. O., & Vishnu, A. (2017). Deep learning for computational chemistry. Journal of computational chemistry, 38(16), 1291-1307. Link
\nSchwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., & Lee, A. A. (2019). Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science, 5(9), 1572-1583. Link
\nWang, S., Guo, Y., Wang, Y., Sun, H., & Huang, J. (2019). SMILES-BERT: Large scale unsupervised pre-training for molecular property prediction. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 429-436). Link
\nChithrananda, S., Grand, G., & Ramsundar, B. (2020). ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885. Link
\nHonda, S., Shi, S., & Ueda, H. R. (2019). SMILES transformer: Pre-trained molecular fingerprint for low data drug discovery. arXiv preprint arXiv:1911.04738. Link
\nFabian, B., Edlich, T., Gaspar, H., Segler, M., Meyers, J., Fiscato, M., & Ahmed, M. (2020). Molecular representation learning with language models and domain-relevant auxiliary tasks. arXiv preprint arXiv:2011.13230. Link
\nHu*, X., Shen, Z., Yang, C., & Tang, J. (2021). Pre-training graph neural networks for molecular property prediction. arXiv preprint arXiv:2111.04718. Link
\nCohen, T. S., Geiger, M., Köhler, J., & Welling, M. (2018). Spherical CNNs. arXiv preprint arXiv:1801.10130. Link
\nBoomsma, W., & Frellsen, J. (2017). Spherical convolutions and their application in molecular modelling. Advances in Neural Information Processing Systems, 30. Link
\nWeiler, M., Geiger, M., Welling, M., Boomsma, W., & Cohen, T. (2018). 3D steerable CNNs: Learning rotationally equivariant features in volumetric data. Advances in Neural Information Processing Systems, 31. Link
\nTownshend, R. J., Vögele, M., Suriana, P., Derry, A., Powers, A., Laloudakis, Y., … & Dror, R. O. (2020). ATOM3D: Tasks on molecules in three dimensions. arXiv preprint arXiv:2012.04035. Link
\nSimm, G. N., & Hernández-Lobato, J. M. (2020). A generative model for molecular distance geometry. arXiv preprint arXiv:1909.11459. Link
\nGanea, O. E., Pattanaik, L., Coley, C. W., Barzilay, R., Jensen, K. F., Green, W. H., & Jaakkola, T. S. (2021). GeoMol: Torsional geometric generation of molecular 3D conformer ensembles. arXiv preprint arXiv:2106.07802. Link
\nPlease contribute more!
", "messages": [ { "id": "2737286", "postDate": "04/05/2024 17:16:19", "content": "Morgan, H. L. (1965). The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. Journal of Chemical Documentation, 5(2), 107-113. Link
\nRogers, D., & Hahn, M. (2010). Extended-connectivity fingerprints. Journal of chemical information and modeling, 50(5), 742-754. Link
\nRiniker, S., & Landrum, G. A. (2013). Open-source platform to benchmark fingerprints for ligand-based virtual screening. Journal of cheminformatics, 5(1), 1-17. Link
\nCereto-Massagué, A., Ojeda, M. J., Valls, C., Mulero, M., Garcia-Vallvé, S., & Pujadas, G. (2015). Molecular fingerprint similarity search in virtual screening. Methods, 71, 58-63. Link
\nGilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017). Neural message passing for quantum chemistry. In International conference on machine learning (pp. 1263-1272). PMLR. Link
\nDuvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015). Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems, 28. Link
\nKipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. Link
\nVeličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903. Link
\nXiong, Z., Wang, D., Liu, X., Zhong, F., Wan, X., Li, X., … & Deng, H. (2019). Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. Journal of medicinal chemistry, 63(16), 8749-8760. Link
\nYang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., … & Barzilay, R. (2019). Analyzing learned molecular representations for property prediction. Journal of chemical information and modeling, 59(8), 3370-3388. Link
\nHu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., & Leskovec, J. (2019). Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265. Link
\nGoh, G. B., Hodas, N. O., & Vishnu, A. (2017). Deep learning for computational chemistry. Journal of computational chemistry, 38(16), 1291-1307. Link
\nSchwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., & Lee, A. A. (2019). Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science, 5(9), 1572-1583. Link
\nWang, S., Guo, Y., Wang, Y., Sun, H., & Huang, J. (2019). SMILES-BERT: Large scale unsupervised pre-training for molecular property prediction. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 429-436). Link
\nChithrananda, S., Grand, G., & Ramsundar, B. (2020). ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885. Link
\nHonda, S., Shi, S., & Ueda, H. R. (2019). SMILES transformer: Pre-trained molecular fingerprint for low data drug discovery. arXiv preprint arXiv:1911.04738. Link
\nFabian, B., Edlich, T., Gaspar, H., Segler, M., Meyers, J., Fiscato, M., & Ahmed, M. (2020). Molecular representation learning with language models and domain-relevant auxiliary tasks. arXiv preprint arXiv:2011.13230. Link
\nHu*, X., Shen, Z., Yang, C., & Tang, J. (2021). Pre-training graph neural networks for molecular property prediction. arXiv preprint arXiv:2111.04718. Link
\nCohen, T. S., Geiger, M., Köhler, J., & Welling, M. (2018). Spherical CNNs. arXiv preprint arXiv:1801.10130. Link
\nBoomsma, W., & Frellsen, J. (2017). Spherical convolutions and their application in molecular modelling. Advances in Neural Information Processing Systems, 30. Link
\nWeiler, M., Geiger, M., Welling, M., Boomsma, W., & Cohen, T. (2018). 3D steerable CNNs: Learning rotationally equivariant features in volumetric data. Advances in Neural Information Processing Systems, 31. Link
\nTownshend, R. J., Vögele, M., Suriana, P., Derry, A., Powers, A., Laloudakis, Y., … & Dror, R. O. (2020). ATOM3D: Tasks on molecules in three dimensions. arXiv preprint arXiv:2012.04035. Link
\nSimm, G. N., & Hernández-Lobato, J. M. (2020). A generative model for molecular distance geometry. arXiv preprint arXiv:1909.11459. Link
\nGanea, O. E., Pattanaik, L., Coley, C. W., Barzilay, R., Jensen, K. F., Green, W. H., & Jaakkola, T. S. (2021). GeoMol: Torsional geometric generation of molecular 3D conformer ensembles. arXiv preprint arXiv:2106.07802. Link
\nPlease contribute more!
", "rawMarkdown": "## Fingerprint Representations:\n\nMorgan, H. L. (1965). The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. Journal of Chemical Documentation, 5(2), 107-113. [Link](https://pubs.acs.org/doi/abs/10.1021/c160017a018)\n\nRogers, D., & Hahn, M. (2010). Extended-connectivity fingerprints. Journal of chemical information and modeling, 50(5), 742-754. [Link](https://pubs.acs.org/doi/abs/10.1021/ci100050t)\n\nRiniker, S., & Landrum, G. A. (2013). Open-source platform to benchmark fingerprints for ligand-based virtual screening. Journal of cheminformatics, 5(1), 1-17. [Link](https://link.springer.com/article/10.1186/1758-2946-5-26)\n\nCereto-Massagué, A., Ojeda, M. J., Valls, C., Mulero, M., Garcia-Vallvé, S., & Pujadas, G. (2015). Molecular fingerprint similarity search in virtual screening. Methods, 71, 58-63. [Link](https://www.sciencedirect.com/science/article/pii/S1046202314002734)\n\n## Graph Representations:\n\nGilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017). Neural message passing for quantum chemistry. In International conference on machine learning (pp. 1263-1272). PMLR. [Link](https://arxiv.org/abs/1704.01212)\n\nDuvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015). Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems, 28. [Link](https://arxiv.org/abs/1509.09292)\n\nKipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. [Link](https://arxiv.org/abs/1609.02907)\n\nVeličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903. [Link](https://arxiv.org/abs/1710.10903)\n\nXiong, Z., Wang, D., Liu, X., Zhong, F., Wan, X., Li, X., ... & Deng, H. (2019). Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. Journal of medicinal chemistry, 63(16), 8749-8760. [Link](https://pubs.acs.org/doi/abs/10.1021/acs.jmedchem.9b00959)\n\nYang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., ... & Barzilay, R. (2019). Analyzing learned molecular representations for property prediction. Journal of chemical information and modeling, 59(8), 3370-3388. [Link](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00237)\n\nHu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., & Leskovec, J. (2019). Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265. [Link](https://arxiv.org/abs/1905.12265)\n\n## SMILES/Text Representations:\n\nGoh, G. B., Hodas, N. O., & Vishnu, A. (2017). Deep learning for computational chemistry. Journal of computational chemistry, 38(16), 1291-1307. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.24764)\n\nSchwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., & Lee, A. A. (2019). Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science, 5(9), 1572-1583. [Link](https://pubs.acs.org/doi/abs/10.1021/acscentsci.9b00576)\n\nWang, S., Guo, Y., Wang, Y., Sun, H., & Huang, J. (2019). SMILES-BERT: Large scale unsupervised pre-training for molecular property prediction. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 429-436). [Link](https://dl.acm.org/doi/abs/10.1145/3307339.3342186)\n\nChithrananda, S., Grand, G., & Ramsundar, B. (2020). ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885. [Link](https://arxiv.org/abs/2010.09885)\n\nHonda, S., Shi, S., & Ueda, H. R. (2019). SMILES transformer: Pre-trained molecular fingerprint for low data drug discovery. arXiv preprint arXiv:1911.04738. [Link](https://arxiv.org/abs/1911.04738)\n\nFabian, B., Edlich, T., Gaspar, H., Segler, M., Meyers, J., Fiscato, M., & Ahmed, M. (2020). Molecular representation learning with language models and domain-relevant auxiliary tasks. arXiv preprint arXiv:2011.13230. [Link](https://arxiv.org/abs/2011.13230)\n\nHu*, X., Shen, Z., Yang, C., & Tang, J. (2021). Pre-training graph neural networks for molecular property prediction. arXiv preprint arXiv:2111.04718. [Link](https://arxiv.org/abs/2111.04718)\n\n## 3D/Mesh/Pointcloud Representations:\n\nCohen, T. S., Geiger, M., Köhler, J., & Welling, M. (2018). Spherical CNNs. arXiv preprint arXiv:1801.10130. [Link](https://arxiv.org/abs/1801.10130)\n\nBoomsma, W., & Frellsen, J. (2017). Spherical convolutions and their application in molecular modelling. Advances in Neural Information Processing Systems, 30. [Link](https://papers.nips.cc/paper/2017/hash/6c524f9d3b7ed8a5230e7afe1c9fd96e-Abstract.html)\n\nWeiler, M., Geiger, M., Welling, M., Boomsma, W., & Cohen, T. (2018). 3D steerable CNNs: Learning rotationally equivariant features in volumetric data. Advances in Neural Information Processing Systems, 31. [Link](https://papers.nips.cc/paper/2018/hash/491174581af7ba2dd51e35e5fad83d34-Abstract.html)\n\nTownshend, R. J., Vögele, M., Suriana, P., Derry, A., Powers, A., Laloudakis, Y., ... & Dror, R. O. (2020). ATOM3D: Tasks on molecules in three dimensions. arXiv preprint arXiv:2012.04035. [Link](https://arxiv.org/abs/2012.04035)\n\nSimm, G. N., & Hernández-Lobato, J. M. (2020). A generative model for molecular distance geometry. arXiv preprint arXiv:1909.11459. [Link](https://arxiv.org/abs/1909.11459)\n\nGanea, O. E., Pattanaik, L., Coley, C. W., Barzilay, R., Jensen, K. F., Green, W. H., & Jaakkola, T. S. (2021). GeoMol: Torsional geometric generation of molecular 3D conformer ensembles. arXiv preprint arXiv:2106.07802. [Link](https://arxiv.org/abs/2106.07802)\n\nPlease contribute more!", "votes": null }, { "id": "2737361", "postDate": "04/05/2024 18:15:05", "content": "@andrewdblevins Thanks for sharing the publications, could you share any recent publications from 2023 and 2024 too.
", "rawMarkdown": "andrewdblevins Thanks for sharing the publications, could you share any recent publications from 2023 and 2024 too.", "votes": null }, { "id": "2769762", "postDate": "04/23/2024 14:35:40", "content": "Thank you, it will be very interesting to study all of this.
", "rawMarkdown": "Thank you, it will be very interesting to study all of this.", "votes": null }, { "id": "2865230", "postDate": "06/10/2024 15:42:15", "content": "This is really helpful!
", "rawMarkdown": "This is really helpful!", "votes": null } ], "comments": [ { "id": 2737361, "author_name": "seshurajup", "author_url": "", "post_date": "04/05/2024 18:15:05", "content": "@andrewdblevins Thanks for sharing the publications, could you share any recent publications from 2023 and 2024 too.
", "votes": null, "replies": [] }, { "id": 2769762, "author_name": "eugenedurymanov", "author_url": "", "post_date": "04/23/2024 14:35:40", "content": "Thank you, it will be very interesting to study all of this.
", "votes": null, "replies": [] }, { "id": 2865230, "author_name": "kasidechaewsrisakul", "author_url": "", "post_date": "06/10/2024 15:42:15", "content": "This is really helpful!
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2737286": "## Fingerprint Representations:\n\nMorgan, H. L. (1965). The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. Journal of Chemical Documentation, 5(2), 107-113. [Link](https://pubs.acs.org/doi/abs/10.1021/c160017a018)\n\nRogers, D., & Hahn, M. (2010). Extended-connectivity fingerprints. Journal of chemical information and modeling, 50(5), 742-754. [Link](https://pubs.acs.org/doi/abs/10.1021/ci100050t)\n\nRiniker, S., & Landrum, G. A. (2013). Open-source platform to benchmark fingerprints for ligand-based virtual screening. Journal of cheminformatics, 5(1), 1-17. [Link](https://link.springer.com/article/10.1186/1758-2946-5-26)\n\nCereto-Massagué, A., Ojeda, M. J., Valls, C., Mulero, M., Garcia-Vallvé, S., & Pujadas, G. (2015). Molecular fingerprint similarity search in virtual screening. Methods, 71, 58-63. [Link](https://www.sciencedirect.com/science/article/pii/S1046202314002734)\n\n## Graph Representations:\n\nGilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017). Neural message passing for quantum chemistry. In International conference on machine learning (pp. 1263-1272). PMLR. [Link](https://arxiv.org/abs/1704.01212)\n\nDuvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015). Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems, 28. [Link](https://arxiv.org/abs/1509.09292)\n\nKipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. [Link](https://arxiv.org/abs/1609.02907)\n\nVeličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903. [Link](https://arxiv.org/abs/1710.10903)\n\nXiong, Z., Wang, D., Liu, X., Zhong, F., Wan, X., Li, X., ... & Deng, H. (2019). Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. Journal of medicinal chemistry, 63(16), 8749-8760. [Link](https://pubs.acs.org/doi/abs/10.1021/acs.jmedchem.9b00959)\n\nYang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., ... & Barzilay, R. (2019). Analyzing learned molecular representations for property prediction. Journal of chemical information and modeling, 59(8), 3370-3388. [Link](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00237)\n\nHu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., & Leskovec, J. (2019). Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265. [Link](https://arxiv.org/abs/1905.12265)\n\n## SMILES/Text Representations:\n\nGoh, G. B., Hodas, N. O., & Vishnu, A. (2017). Deep learning for computational chemistry. Journal of computational chemistry, 38(16), 1291-1307. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.24764)\n\nSchwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., & Lee, A. A. (2019). Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science, 5(9), 1572-1583. [Link](https://pubs.acs.org/doi/abs/10.1021/acscentsci.9b00576)\n\nWang, S., Guo, Y., Wang, Y., Sun, H., & Huang, J. (2019). SMILES-BERT: Large scale unsupervised pre-training for molecular property prediction. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 429-436). [Link](https://dl.acm.org/doi/abs/10.1145/3307339.3342186)\n\nChithrananda, S., Grand, G., & Ramsundar, B. (2020). ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885. [Link](https://arxiv.org/abs/2010.09885)\n\nHonda, S., Shi, S., & Ueda, H. R. (2019). SMILES transformer: Pre-trained molecular fingerprint for low data drug discovery. arXiv preprint arXiv:1911.04738. [Link](https://arxiv.org/abs/1911.04738)\n\nFabian, B., Edlich, T., Gaspar, H., Segler, M., Meyers, J., Fiscato, M., & Ahmed, M. (2020). Molecular representation learning with language models and domain-relevant auxiliary tasks. arXiv preprint arXiv:2011.13230. [Link](https://arxiv.org/abs/2011.13230)\n\nHu*, X., Shen, Z., Yang, C., & Tang, J. (2021). Pre-training graph neural networks for molecular property prediction. arXiv preprint arXiv:2111.04718. [Link](https://arxiv.org/abs/2111.04718)\n\n## 3D/Mesh/Pointcloud Representations:\n\nCohen, T. S., Geiger, M., Köhler, J., & Welling, M. (2018). Spherical CNNs. arXiv preprint arXiv:1801.10130. [Link](https://arxiv.org/abs/1801.10130)\n\nBoomsma, W., & Frellsen, J. (2017). Spherical convolutions and their application in molecular modelling. Advances in Neural Information Processing Systems, 30. [Link](https://papers.nips.cc/paper/2017/hash/6c524f9d3b7ed8a5230e7afe1c9fd96e-Abstract.html)\n\nWeiler, M., Geiger, M., Welling, M., Boomsma, W., & Cohen, T. (2018). 3D steerable CNNs: Learning rotationally equivariant features in volumetric data. Advances in Neural Information Processing Systems, 31. [Link](https://papers.nips.cc/paper/2018/hash/491174581af7ba2dd51e35e5fad83d34-Abstract.html)\n\nTownshend, R. J., Vögele, M., Suriana, P., Derry, A., Powers, A., Laloudakis, Y., ... & Dror, R. O. (2020). ATOM3D: Tasks on molecules in three dimensions. arXiv preprint arXiv:2012.04035. [Link](https://arxiv.org/abs/2012.04035)\n\nSimm, G. N., & Hernández-Lobato, J. M. (2020). A generative model for molecular distance geometry. arXiv preprint arXiv:1909.11459. [Link](https://arxiv.org/abs/1909.11459)\n\nGanea, O. E., Pattanaik, L., Coley, C. W., Barzilay, R., Jensen, K. F., Green, W. H., & Jaakkola, T. S. (2021). GeoMol: Torsional geometric generation of molecular 3D conformer ensembles. arXiv preprint arXiv:2106.07802. [Link](https://arxiv.org/abs/2106.07802)\n\nPlease contribute more!", "2737361": "andrewdblevins Thanks for sharing the publications, could you share any recent publications from 2023 and 2024 too.", "2769762": "Thank you, it will be very interesting to study all of this.", "2865230": "This is really helpful!" }, "source": "meta" }