Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction
With the great advancements in experimental data, computational power and learning algorithms, artificial intelligence (AI) based drug design has begun to gain momentum recently. AI-based drug design has great promise to revolutionize pharmaceutical industries by significantly reducing the time and...
Main Authors: | Liu, Xiang, Feng, Huitao, Wu, Jie, Xia, Kelin |
---|---|
Other Authors: | School of Physical and Mathematical Sciences |
Format: | Journal Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/161049 |
Similar Items
-
Hom-complex-based machine learning (HCML) for the prediction of protein–protein binding affinity changes upon mutation
by: Liu, Xiang, et al.
Published: (2022) -
Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction
by: Liu, Xiang, et al.
Published: (2022) -
Ligand binding induces agonistic-like conformational adaptations in helix 12 of progesterone receptor ligand binding domain
by: Zheng, Liangzhen, et al.
Published: (2019) -
Synthesis, characterization, and bioactivities of dithiocarbazate-Schiff base ligands and their metal complexes
by: Low, May Lee
Published: (2014) -
Ollivier persistent Ricci curvature-based machine learning for the protein-ligand binding affinity prediction
by: Wee, Junjie, et al.
Published: (2022)