Fetching the paper…
Reading the bibliography…
The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever and D. Amodei, Advances in Neural Information Processing Systems, 2020, pp. 1877–1901
1901
Earlier work this paper cites.
A. Doucet, N. de Freitas and N. Gordon, in An Introduction to Sequential Monte Carlo Methods , Springer New York, New York, NY, 2001, pp. 3–14
2001
Earlier work this paper cites.
A. C. Anderson, Chemistry & Biology , 2003, 10
2003
Earlier work this paper cites.
P. Del Moral, A. Doucet and A. Jasra, Journal of the Royal Statistical Society Series B: Statistical Methodology , 2006, 68
2006
Earlier work this paper cites.
P. C. D. Hawkins and A. Nicholls, Journal of Chemical Information and Modeling , 2012, 52
2012
Earlier work this paper cites.
R. D. Taylor, M. MacCoss and A. D. G. Lawson, Journal of Medicinal Chemistry , 2014, 57
2014
Earlier work this paper cites.
T. Sterling and J. J. Irwin, Journal of Chemical Information and Modeling , 2015, 55
2015
Earlier work this paper cites.
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams and A. Aspuru-Guzik, ACS Central Science , 2018, 4
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Batool, B. Ahmad and S. Choi, International Journal of Molecular Sciences , 2019, 20
2019
Earlier work this paper cites.
R. Winter, F. Montanari, A. Steffen, H. Briem, F. Noé and D.-A. Clevert, Chem. Sci. , 2019, 10
2019
Earlier work this paper cites.
R. Winter, F. Montanari, F. Noé and D.-A. Clevert, Chem. Sci. , 2019, 10
2019
Earlier work this paper cites.
J. Jampilek, Molecules , 2019, 24
2019
Earlier work this paper cites.
P. G. Francoeur, T. Masuda, J. Sunseri, A. Jia, R. B. Iovanisci, I. Snyder and D. R. Koes, Journal of Chemical Information and Modeling , 2020, 60
2020
Earlier work this paper cites.
G. K. Kanev, C. de Graaf, B. A. Westerman, I. J. P. de Esch and A. J. Kooistra, Nucleic Acids Research , 2020, 49
2020
Cited alongside, same era.
H. Green, D. R. Koes and J. D. Durrant, Chem. Sci. , 2021, 12
2021
Cited alongside, same era.
S. Luo, J. Guan, J. Ma and J. Peng, Advances in Neural Information Processing Systems, 2021, pp. 6229–6239
2021
Cited alongside, same era.
P. Dhariwal and A. Q. Nichol, Advances in Neural Information Processing Systems, 2021
2021
Cited alongside, same era.
O. T. Unke, S. Chmiela, H. E. Sauceda, M. Gastegger, I. Poltavsky, K. T. Schütt, A. Tkatchenko and K.-R. Müller, Chem. Rev. , 2021, 121
2021
Cited alongside, same era.
M. Ragoza, T. Masuda and D. R. Koes, Chem. Sci. , 2022, 13
S. Liu, H. Guo and J. Tang, The Eleventh International Conference on Learning Representations, 2023
2023
Later among the works it cites.
S. Zaidi, M. Schaarschmidt, J. Martens, H. Kim, Y. W. Teh, A. Sanchez-Gonzalez, P. Battaglia, R. Pascanu and J. Godwin, The Eleventh International Conference on Learning Representations, 2023
2023
Later among the works it cites.
C. Harris, K. Didi, A. R. Jamasb, C. K. Joshi, S. V. Mathis, P. Lio and T. Blundell, Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models? , 2023
2023
Later among the works it cites.
D. Schaller, C. D. Christ, J. D. Chodera and A. Volkamer, Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery , 2023, https://www.biorxiv.org/content/early/2023/09/14/2023.09.11.557138
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
M. Liu, Y. Luo, K. Uchino, K. Maruhashi and S. Ji, Proceedings of the 39th International Conference on Machine Learning, 2022, pp. 13912–13924
2022
Cited alongside, same era.
2022
Cited alongside, same era.
X. Peng, S. Luo, J. Guan, Q. Xie, J. Peng and J. Ma, Proceedings of the 39th International Conference on Machine Learning, 2022, pp. 17644–17655
2022
Cited alongside, same era.
X.-C. Yu, C.-C. Zhang, L.-T. Wang, J.-Z. Li, T. Li and W.-T. Wei, Organic Chemistry Frontiers , 2022, 9
2022
Cited alongside, same era.
A. S. Powers, H. H. Yu, P. Suriana, R. V. Koodli, T. Lu, J. M. Paggi and R. O. Dror, ACS Central Science , 2023, 9
2023
Cited alongside, same era.
J. Guan, W. W. Qian, X. Peng, Y. Su, J. Peng and J. Ma, The Eleventh International Conference on Learning Representations, 2023
2023
Cited alongside, same era.
C. Vignac, N. Osman, L. Toni and P. Frossard, Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part II, 2023, pp. 560–576
2023
Later among the works it cites.
B. L. Trippe, J. Yim, D. Tischer, D. Baker, T. Broderick, R. Barzilay and T. S. Jaakkola, The Eleventh International Conference on Learning Representations, 2023
2023
Later among the works it cites.
L. Wu, B. L. Trippe, C. A. Naesseth, J. P. Cunningham and D. Blei, Thirty-seventh Conference on Neural Information Processing Systems, 2023
2023
Later among the works it cites.
A. Rusu, I.-M. Moga, L. Uncu and G. Hancu, Pharmaceutics , 2023, 15
2023
Later among the works it cites.
J. Zhu, Z. Gu, J. Pei and L. Lai, Chem. Sci. , 2024, –
2024
Closest in time.
Y. Xia, K. Wu, P. Deng, R. Liu, Y. Zhang, H. Guo, Y. Cui, Q. Pei, L. Wu, S. Xie, S. Chen, X. Lu, S. Hu, J. Wu, C.-K. Chan, S. Chen, L. Zhou, N. Yu, H. Liu, J. Guo, T. Qin and T.-Y. Liu, Target-aware Molecule Generation for Drug Design Using a Chemical Language Model , 2024, https://www.biorxiv.org/content/early/2024/01/08/2024.01.08.574635
2024
Closest in time.
T. Le, J. Cremer, F. Noé, D.-A. Clevert and K. Schütt, The Twelfth International Conference on Learning Representations, 2024
2024
Closest in time.
M. Buttenschoen, G. M. Morris and C. M. Deane, PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences , 2024, http://dx.doi.org/10.1039/D3SC04185A
2024
Closest in time.
M. Backenköhler, J. Groß, V. Wolf and A. Volkamer, Guided docking as a data generation approach facilitates structure-based machine learning on kinases , 2024
2024
Closest in time.
G. A. Landrum and S. Riniker, Journal of Chemical Information and Modeling , 2024, 64
2024
Closest in time.