Molecule Generation for Drug Design: a Graph Learning Perspective

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Yang, Nianzu, Wu, Huaijin, Zeng, Kaipeng, Li, Yang, Yan, Junchi
Format: Preprint
Veröffentlicht: 2022
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866916084560429056
author Yang, Nianzu
Wu, Huaijin
Zeng, Kaipeng
Li, Yang
Yan, Junchi
author_facet Yang, Nianzu
Wu, Huaijin
Zeng, Kaipeng
Li, Yang
Yan, Junchi
contents Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields. One such promising application is in the realm of molecule design and discovery, notably within the pharmaceutical industry. Our survey offers a comprehensive overview of state-of-the-art methods in molecule design, particularly focusing on \emph{de novo} drug design, which incorporates (deep) graph learning techniques. We categorize these methods into three distinct groups: \emph{i)} \emph{all-at-once}, \emph{ii)} \emph{fragment-based}, and \emph{iii)} \emph{node-by-node}. Additionally, we introduce some key public datasets and outline the commonly used evaluation metrics for both the generation and optimization of molecules. In the end, we discuss the existing challenges in this field and suggest potential directions for future research.
format Preprint
id arxiv_https___arxiv_org_abs_2202_09212
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Molecule Generation for Drug Design: a Graph Learning Perspective
Yang, Nianzu
Wu, Huaijin
Zeng, Kaipeng
Li, Yang
Yan, Junchi
Machine Learning
Artificial Intelligence
Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields. One such promising application is in the realm of molecule design and discovery, notably within the pharmaceutical industry. Our survey offers a comprehensive overview of state-of-the-art methods in molecule design, particularly focusing on \emph{de novo} drug design, which incorporates (deep) graph learning techniques. We categorize these methods into three distinct groups: \emph{i)} \emph{all-at-once}, \emph{ii)} \emph{fragment-based}, and \emph{iii)} \emph{node-by-node}. Additionally, we introduce some key public datasets and outline the commonly used evaluation metrics for both the generation and optimization of molecules. In the end, we discuss the existing challenges in this field and suggest potential directions for future research.
title Molecule Generation for Drug Design: a Graph Learning Perspective
topic Machine Learning
Artificial Intelligence
url https://arxiv.org/abs/2202.09212