相关实验视频

Updated: May 28, 2025

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

计算方法来预测生物活性天然产品的毒性:对方法的小型审查

Kwanyong Choi¹, Ji Yeon Kim¹

¹Department of Food Science and Biotechnology, Seoul National University of Science and Technology, 232, Gongneung-Ro, Nowon-Gu, Seoul, 01811 Republic of Korea.

Food science and biotechnology

|February 13, 2025

概括

机器学习 (ML) 可以通过评估内在毒性来改善生物活性天然产品的食品安全预测. 本审查审查了对更安全的功能性食品的ML模型选择和验证.

科学领域:

食品科学食品科学食品科学
毒理学毒理学毒理学
计算化学计算化学

背景情况:

在全球范围内,功能性食品的需求量很高.
生物活性天然产品存在复杂的组成挑战.
潜在的生理干扰和不良影响需要进行安全评估.

研究的目的:

审查基于机器学习 (ML) 的食品安全预测策略.
专注于生物活性天然产品的内在毒性.
评估当前的模型选择和验证技术.

主要方法:

对食品安全中的ML现有文献的审查.
对模型选择和验证方法的分析.
探索in silico毒性建模方法的探索.

主要成果:

目前用于食品安全的ML方法具有优点和局限性.
模型选择和验证技术的有效性各不相同.
需要先进的in silico毒性建模.

结论:

基于ML的食品安全预测对于功能性食品至关重要.
进一步的研究应侧重于化合物组合和先进的建模.
开发强大的in silico毒性模型对于生物活性天然产品至关重要.

关键词:

这是自下而上的.在状的中.自然产品是天然产品.从上到下,从上到下.有毒性有毒性

更多相关视频

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput

A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput

Published on: September 22, 2019

相关概念视频

Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...