Geometric Graph Learning with Extended Atom-Types Features for
Protein-Ligand Binding Affinity Prediction
- URL: http://arxiv.org/abs/2301.06194v1
- Date: Sun, 15 Jan 2023 21:30:21 GMT
- Title: Geometric Graph Learning with Extended Atom-Types Features for
Protein-Ligand Binding Affinity Prediction
- Authors: Md Masud Rana and Duc Duy Nguyen
- Abstract summary: We upgrade the graph-based learners for the study of protein-ligand interactions by integrating extensive atom types such as SYBYL.
Our approach results in two different methods, namely $textsybyltextGGL$-Score and $texteciftextGGL$-Score.
While both of our models achieve state-of-the-art results, the SYBYL atom-type model $textsybyltextGGL$-Score outperforms other methods by a wide margin in all benchmarks.
- Score: 0.17132914341329847
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding and accurately predicting protein-ligand binding affinity are
essential in the drug design and discovery process. At present, machine
learning-based methodologies are gaining popularity as a means of predicting
binding affinity due to their efficiency and accuracy, as well as the
increasing availability of structural and binding affinity data for
protein-ligand complexes. In biomolecular studies, graph theory has been widely
applied since graphs can be used to model molecules or molecular complexes in a
natural manner. In the present work, we upgrade the graph-based learners for
the study of protein-ligand interactions by integrating extensive atom types
such as SYBYL and extended connectivity interactive features (ECIF) into
multiscale weighted colored graphs (MWCG). By pairing with the gradient
boosting decision tree (GBDT) machine learning algorithm, our approach results
in two different methods, namely $^\text{sybyl}\text{GGL}$-Score and
$^\text{ecif}\text{GGL}$-Score. Both of our models are extensively validated in
their scoring power using three commonly used benchmark datasets in the drug
design area, namely CASF-2007, CASF-2013, and CASF-2016. The performance of our
best model $^\text{sybyl}\text{GGL}$-Score is compared with other
state-of-the-art models in the binding affinity prediction for each benchmark.
While both of our models achieve state-of-the-art results, the SYBYL atom-type
model $^\text{sybyl}\text{GGL}$-Score outperforms other methods by a wide
margin in all benchmarks.
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