Feature functional theory–binding predictor (FFT–BP) for the blind prediction of binding free energies-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Feature functional theory–binding predictor (FFT–BP) for the blind prediction of binding free energies

Published:2017-04 Issue:4 Volume:136 Page:
ISSN:1432-881X
Container-title:Theoretical Chemistry Accounts
language:en
Short-container-title:Theor Chem Acc

Author:

Wang Bao,Zhao Zhixiong,Nguyen Duc D.,Wei Guo-Wei

Funder

National Science Foundation

Michigan State University

Publisher

Springer Science and Business Media LLC

Subject

Physical and Theoretical Chemistry

Link

http://link.springer.com/content/pdf/10.1007/s00214-017-2083-1.pdf

Reference98 articles.

1. Agarwal S, Dugar D, Sengupta S (2010) Ranking chemical structure for drug discovery: a new machine learning approach. J Chem Inf Model 50:716–731

2. Ashtawy HM, Mahapatra NR (2012) A comparative assessment of ranking accuracies of conventional and machine-learning-based scoring functions for protein-ligand binding affinity prediction. IEEE/ACM Trans Comput Biol Bioinf 9(5):1301–1313

3. Ballester PJ (2012) Machine learning scoring functions based on random forest and support vector regression. In: Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics, pp 14–25

4. Ballester PJ, Mitchell JBO (2010) A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26(9):1169–1175

5. Ballester PJ, Schreyer A, Tom LB (2014) Does a more precise chemical description of protein–ligand complexes lead to more accurate prediction of binding affinity? J Chem Inf Model 54:944–955

Cited by 27 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model;Briefings in Bioinformatics;2023-05-16

2. Leveraging scaffold information to predict protein–ligand binding affinity with an empirical graph neural network;Briefings in Bioinformatics;2023-01

3. Machine Learning Approaches to Improve Prediction of Target‐Drug Interactions;Drug Design Using Machine Learning;2022-10-07

4. A reinforcement learning approach for protein–ligand binding pose prediction;BMC Bioinformatics;2022-09-08

5. Leveraging Scaffold Information to Predict Protein-ligand Binding Affinity with an Empirical Graph Neural Network;2022-08-21

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3