阅读Retro web:一个用户友好的平台,用于预测植物天然产品生物合成
Yejin Kwak1, Taein Kim2, Sang-Gyu Kim2
1Medical Research Institute, Pusan National University, Yangsan, Republic of Korea.
Molecules and cells
|June 4, 2025
概括
研究人员现在可以使用READRetro Web轻松设计自然产品 (NP) 反合成途径. 这个用户友好的平台利用机器学习来更快地发现NP,克服计算障碍,实现更广泛的可访问性.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 自然产品 (NPs) 是药物研究中至关重要的生物活性分子.
- 大规模的NP生产面临着可用性和可扩展性的挑战.
- 深度学习模型加速了NP发现,但通常需要显著的计算专业知识.
研究的目的:
- 开发一个用户友好的网络平台,用于回复合成预测.
- 为研究人员提供先进的机器学习模型,用于NP发现.
- 为了促进自然产品回合成途径的设计.
主要方法:
- 将READRetro机器学习模型集成到一个Web平台中.
- 利用现代网络技术,提供快速响应的用户体验.
- 开发一个可访问的接口,用于回复合成预测.
主要成果:
- READRetro Web提供了高预测准确度和计算效率.
- 该平台为设计NP回合成途径提供了精心组织的结果.
- 该网络工具简化了研究人员复杂的计算工作流程.
结论:
- READRetro Web使访问先进的机器学习驱动的回复合成变得民主化.
- 该平台弥合了计算工具和实践研究之间的差距.
- 它赋予了更广泛的研究人员在天然产品药物发现方面的权力.
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