Replication Data for: Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction (doi:10.21979/N9/JHXLSG)

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Document Description

Citation

Title:

Replication Data for: Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction

Identification Number:

doi:10.21979/N9/JHXLSG

Distributor:

DR-NTU (Data)

Date of Distribution:

2021-04-02

Version:

1

Bibliographic Citation:

Xia, Kelin, 2021, "Replication Data for: Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction", https://doi.org/10.21979/N9/JHXLSG, DR-NTU (Data), V1

Study Description

Citation

Title:

Replication Data for: Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction

Identification Number:

doi:10.21979/N9/JHXLSG

Authoring Entity:

Xia, Kelin (Nanyang Technological University)

Software used in Production:

Pyhton

Grant Number:

Startup Grant M4081842.110

Grant Number:

Academic Research fund Tier 1 RG109/19

Grant Number:

Tier 2 MOE2018-T2-1-033

Distributor:

DR-NTU (Data)

Access Authority:

Xia, Kelin

Depositor:

Xia, Kelin

Date of Deposit:

2021-04-02

Holdings Information:

https://doi.org/10.21979/N9/JHXLSG

Study Scope

Keywords:

Computer and Information Science, Mathematical Sciences, Medicine, Health and Life Sciences, Computer and Information Science, Mathematical Sciences, Medicine, Health and Life Sciences, Ricci curvature, machine learning, drug design, molecular descriptor

Abstract:

Efficient molecular featurization is one of the major issues for machine learning models in drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC (OPRC), for the molecular featurization and feature engineering, for the first time. The filtration process proposed in the persistent homology is employed to generate a series of nested molecular graphs. Persistence and variation of Ollivier Ricci curvatures on these nested graphs are defined as OPRC. Moreover, persistent attributes, which are statistical and combinatorial properties of OPRCs during the filtration process, are used as molecular descriptors and further combined with machine learning models, in particular, gradient boosting tree (GBT). Our OPRC-GBT model is used in the prediction of the protein–ligand binding affinity, which is one of the key steps in drug design. Based on three of the most commonly used data sets from the well-established protein–ligand binding databank, that is, PDBbind, we intensively test our model and compare with existing models. It has been found that our model can achieve the state-of-the-art results and has advantages over traditional molecular descriptors.

Kind of Data:

Code

Methodology and Processing

Sources Statement

Data Access

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Related Studies

The computation of ORC for a graph network is obtained using the GraphRicciCurvature library from <a href="https://github.com/saibalmars/GraphRicciCurvature"> https://github.com/saibalmars/GraphRicciCurvature</a>. The PDBbind databases were obtained from <a href="http://pdbbind.org.cn"> http://pdbbind.org.cn</a>. The codes implemented for the HBNs and OPRC models can be found in <a href="https://github.com/ExpectozJJ/Ollivier-Persistent-Ricci-Curvature"> https://github.com/ExpectozJJ/Ollivier-Persistent-Ricci-Curvature</a>. Additional data or code would be available upon reasonable request.

Related Publications

Citation

Identification Number:

10.1021/acs.jcim.0c01415

Bibliographic Citation:

Wee, J., & Xia, K. (2021). Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction. Journal of Chemical Information and Modeling.

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