热门出版物 - 学术资源搜索

热门出版物

我的图书馆

引用“”的文章MoleculeNet: a benchmark for molecular machine learning.

标题/作者	年份
Automatic chemical design using a data-driven continuous representation of molecules R Gómez-Bombarelli, JN Wei, D Duvenaud, JM Hernández-Lobato, ... ACS central science 4 (2), 268-276	2018
Opportunities and obstacles for deep learning in biology and medicine T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ... Journal of The Royal Society Interface 15 (141), 20170387	2018
Open graph benchmark: Datasets for machine learning on graphs W Hu, M Fey, M Zitnik, Y Dong, H Ren, B Liu, M Catasta, J Leskovec Advances in neural information processing systems 33, 22118-22133	2020
Applications of machine learning in drug discovery and development J Vamathevan, D Clark, P Czodrowski, I Dunham, E Ferran, G Lee, B Li, ... Nature reviews Drug discovery 18 (6), 463-477	2019
The rise of deep learning in drug discovery H Chen, O Engkvist, Y Wang, M Olivecrona, T Blaschke Drug discovery today 23 (6), 1241-1250	2018
A deep learning approach to antibiotic discovery JM Stokes, K Yang, K Swanson, W Jin, A Cubillos-Ruiz, NM Donghia, ... Cell 180 (4), 688-702. e13	2020
Inverse molecular design using machine learning: Generative models for matter engineering B Sanchez-Lengeling, A Aspuru-Guzik Science 361 (6400), 360-365	2018
Weisfeiler and leman go neural: Higher-order graph neural networks C Morris, M Ritzert, M Fey, WL Hamilton, JE Lenssen, G Rattan, M Grohe Proceedings of the AAAI conference on artificial intelligence 33 (01), 4602-4609	2019
Analyzing learned molecular representations for property prediction K Yang, K Swanson, W Jin, C Coley, P Eiden, H Gao, A Guzman-Perez, ... Journal of chemical information and modeling 59 (8), 3370-3388	2019
Wilds: A benchmark of in-the-wild distribution shifts PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ... International Conference on Machine Learning, 5637-5664	2021
Deep graph library: A graph-centric, highly-performant package for graph neural networks M Wang, D Zheng, Z Ye, Q Gan, M Li, X Song, J Zhou, C Ma, L Yu, Y Gai, ... arXiv preprint arXiv:1909.01315	2019
Adaptive graph convolutional neural networks R Li, S Wang, F Zhu, J Huang Proceedings of the AAAI conference on artificial intelligence 32 (1)	2018
A transdisciplinary review of deep learning research and its relevance for water resources scientists C Shen Water Resources Research 54 (11), 8558-8593	2018
Directional message passing for molecular graphs J Gasteiger, J Groß, S Günnemann arXiv preprint arXiv:2003.03123	2020
Concepts of artificial intelligence for computer-assisted drug discovery X Yang, Y Wang, R Byrne, G Schneider, S Yang Chemical reviews 119 (18), 10520-10594	2019
Deep learning for molecular design—a review of the state of the art DC Elton, Z Boukouvalas, MD Fuge, PW Chung Molecular Systems Design & Engineering 4 (4), 828-849	2019
Machine learning for molecular simulation F Noé, A Tkatchenko, KR Müller, C Clementi Annual review of physical chemistry 71, 361-390	2020
Machine learning in computer-aided synthesis planning CW Coley, WH Green, KF Jensen Accounts of chemical research 51 (5), 1281-1289	2018
Matminer: An open source toolkit for materials data mining L Ward, A Dunn, A Faghaninia, NER Zimmermann, S Bajaj, Q Wang, ... Computational Materials Science 152, 60-69	2018
Mol2vec: unsupervised machine learning approach with chemical intuition S Jaeger, S Fulle, S Turk Journal of chemical information and modeling 58 (1), 27-35	2018

1 - 20

计算机程序会自动估算并确定日期和引用次数。

共建清朗的网络空间,如遇有害信息,请举报。
本站数据皆整合自互联网公开资源索引,方便科研学术方面查询,并不存储相关数据资源;如对此有异议,请联系我们解决.
© 2023 学术资源搜索 @联系我们 | 申请短期会员 | 数据源提交