Journal: Current Topics in Medicinal Chemistry
Guest editor(s):Dr. Lianxiang Luo
Co-Guest Editor(s):

Introduction

Our approach involves using computational methods to predict the potential therapeutic benefits of natural products by considering factors such as drug structure, targets, and interactions. We also employ multitarget analysis to understand the role of drug targets in disease pathways. We advocate for the use of artificial intelligence in predicting drug targets, assessing activity, and predicting toxicity, as well as in studying drug repositioning. We encourage contributors to utilize the latest artificial intelligence techniques, including machine learning, deep learning, neural networks, and other models, to validate and explain the targets and activities of screened natural products. We welcome contributions on all aspects of computational and artificial intelligence methods in the discovery and design of natural products.

Keywords

Artificial intelligence, Natural Products, Drug discovery, Fragment-based drug design, Machine Learning

Sub-topics

 

Our approach involves using computational methods to predict the potential therapeutic benefits of natural products by considering factors such as drug structure, targets, and interactions. We also employ multitarget analysis to understand the role of drug targets in disease pathways. We advocate for the use of artificial intelligence in predicting drug targets, assessing activity, and predicting toxicity, as well as in studying drug repositioning. We encourage contributors to utilize the latest artificial intelligence techniques, including machine learning, deep learning, neural networks, and other models, to validate and explain the targets and activities of screened natural products. We welcome contributions on all aspects of computational and artificial intelligence methods in the discovery and design of natural products.

 

Original research, reviews, mini-reviews, and opinion pieces on various topics are all welcome, including but not limited to:

• Virtual screening and drug discovery from natural product libraries

• Neural network, deep learning and artificial intelligence methods in natural product discovery

• AI-based natural product target prediction and druggability evaluation.

• Optimize AI algorithms to improve the accuracy of natural product screening and prediction

• Fragment-based drug design

• Microbial gene editing for the synthesis of lead compounds