一种用于结合亲和力预测的扩展连接性相互作用特征的简单空间扩展。
A simple spatial extension to the extended connectivity interaction features for binding affinity prediction.
作者信息
Orhobor Oghenejokpeme I, Rehim Abbi Abdel, Lou Hang, Ni Hao, King Ross D
机构信息
Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
Department of Mathematics, University College London, London, UK.
出版信息
R Soc Open Sci. 2022 May 4;9(5):211745. doi: 10.1098/rsos.211745. eCollection 2022 May.
Abstract
The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one such representation. We report that (i) including the discretized distances between protein-ligand atom pairs in the ECIF scheme improves predictive accuracy, and (ii) in an evaluation using gradient boosted trees, we found that the resampling method used in selecting the best hyperparameters has a strong effect on predictive performance, especially for benchmarking purposes.
摘要
用于构建机器学习模型的蛋白质-配体复合物表示形式在结合亲和力预测的准确性方面起着重要作用。扩展连接性相互作用特征（ECIF）就是这样一种表示形式。我们报告：（i）在ECIF方案中纳入蛋白质-配体原子对之间的离散距离可提高预测准确性；（ii）在使用梯度提升树的评估中，我们发现选择最佳超参数时所采用的重采样方法对预测性能有很大影响，尤其是出于基准测试目的时。
相似文献
1
A simple spatial extension to the extended connectivity interaction features for binding affinity prediction.
R Soc Open Sci. 2022 May 4;9(5):211745. doi: 10.1098/rsos.211745. eCollection 2022 May.
2
Extended connectivity interaction features: improving binding affinity prediction through chemical description.
Bioinformatics. 2021 Jun 16;37(10):1376-1382. doi: 10.1093/bioinformatics/btaa982.
3
Multi-shelled ECIF: improved extended connectivity interaction features for accurate binding affinity prediction.
Bioinform Adv. 2023 Oct 20;3(1):vbad155. doi: 10.1093/bioadv/vbad155. eCollection 2023.
4
Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction.
Comput Biol Med. 2023 Sep;164:107250. doi: 10.1016/j.compbiomed.2023.107250. Epub 2023 Jul 17.
5
The impact of cross-docked poses on performance of machine learning classifier for protein-ligand binding pose prediction.
J Cheminform. 2021 Oct 16;13(1):81. doi: 10.1186/s13321-021-00560-w.
6
ET-score: Improving Protein-ligand Binding Affinity Prediction Based on Distance-weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm.
Mol Inform. 2021 Aug;40(8):e2060084. doi: 10.1002/minf.202060084. Epub 2021 May 21.
7
Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules.
J Chem Inf Model. 2022 Sep 26;62(18):4369-4379. doi: 10.1021/acs.jcim.2c00916. Epub 2022 Sep 9.
8
Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment.
J Chem Inf Model. 2018 Jan 22;58(1):119-133. doi: 10.1021/acs.jcim.7b00309. Epub 2017 Dec 20.
9
Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study.
BMC Bioinformatics. 2014 Aug 27;15(1):291. doi: 10.1186/1471-2105-15-291.
10
CScore: a simple yet effective scoring function for protein-ligand binding affinity prediction using modified CMAC learning architecture.
J Bioinform Comput Biol. 2011 Dec;9 Suppl 1:1-14. doi: 10.1142/s021972001100577x.
引用本文的文献
1
ERL-ProLiGraph: Enhanced representation learning on protein-ligand graph structured data for binding affinity prediction.
Mol Inform. 2024 Dec;43(12):e202400044. doi: 10.1002/minf.202400044. Epub 2024 Oct 15.
2
Multi-shelled ECIF: improved extended connectivity interaction features for accurate binding affinity prediction.
Bioinform Adv. 2023 Oct 20;3(1):vbad155. doi: 10.1093/bioadv/vbad155. eCollection 2023.
3
Protein-ligand binding affinity prediction exploiting sequence constituent homology.
Bioinformatics. 2023 Aug 1;39(8). doi: 10.1093/bioinformatics/btad502.
本文引用的文献
1
Extended connectivity interaction features: improving binding affinity prediction through chemical description.
Bioinformatics. 2021 Jun 16;37(10):1376-1382. doi: 10.1093/bioinformatics/btaa982.
2
Learning from the ligand: using ligand-based features to improve binding affinity prediction.
Bioinformatics. 2020 Feb 1;36(3):758-764. doi: 10.1093/bioinformatics/btz665.
3
OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein-Ligand Binding Affinity Prediction.
ACS Omega. 2019 Sep 16;4(14):15956-15965. doi: 10.1021/acsomega.9b01997. eCollection 2019 Oct 1.
4
AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening.
J Chem Inf Model. 2019 Jul 22;59(7):3291-3304. doi: 10.1021/acs.jcim.9b00334. Epub 2019 Jul 1.
5
DG-GL: Differential geometry-based geometric learning of molecular datasets.
Int J Numer Method Biomed Eng. 2019 Mar;35(3):e3179. doi: 10.1002/cnm.3179. Epub 2019 Feb 7.
6
Comparative Assessment of Scoring Functions: The CASF-2016 Update.
J Chem Inf Model. 2019 Feb 25;59(2):895-913. doi: 10.1021/acs.jcim.8b00545. Epub 2018 Dec 11.
7
Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions.
Bioinformatics. 2019 Apr 15;35(8):1334-1341. doi: 10.1093/bioinformatics/bty757.
8
K: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.
J Chem Inf Model. 2018 Feb 26;58(2):287-296. doi: 10.1021/acs.jcim.7b00650. Epub 2018 Jan 29.
9
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.
PLoS Comput Biol. 2018 Jan 8;14(1):e1005929. doi: 10.1371/journal.pcbi.1005929. eCollection 2018 Jan.
10
Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.
Wiley Interdiscip Rev Comput Mol Sci. 2015 Nov-Dec;5(6):405-424. doi: 10.1002/wcms.1225. Epub 2015 Aug 28.
Zotero 插件