Cocry-pred:一种用于 Cocrystal 预测的动态资源传播方法
Wenxiang Song1, Ren Peng2, Hongbo Yu1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
Journal of chemical information and modeling
|March 12, 2025
概括
一个新的共晶预测模型,Cocry-pred,使用基于网络的推断来有效地识别适合药物开发的共构体. 这种工具加速了共晶选和设计,改善了药物特性.
科学领域:
- 制药科学 制药科学
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 药物共结晶增强物理化学性能,而不会改变化学结构.
- 在药物开发中,确定合适的共造物质是一个重大挑战,需要大量的资源.
- 现有对coformer预测的方法往往是资源密集型和耗时的.
研究的目的:
- 为了开发一种新且高效的共晶预测模型,Cocry-pred.
- 为了简化对药物向分子潜在的共构体的识别.
- 提高协晶选和设计的效率和成功率.
主要方法:
- 利用基于网络的推断 (NBI) 算法进行动态资源传播.
- 从共晶网络和分子亚结构信息中集成的拓数据.
- 评估了13个分子指纹,并为模型性能优化了超参数 (α,β,γ).
主要成果:
- Cocry-pred以0.885的曲线下面积 (AUC) 和0.108.10的R平方 (RS) 实现了高性能.
- 该模型成功地预测了阿帕提尼布的潜在协同构造物.
- 合成了七个阿帕蒂尼布共晶体,其中两个产生单晶结构,验证了模型的可靠性.
结论:
- Cocry-pred提供了一个强大而高效的工具,用于可晶选和设计.
- 该模型显著改善了识别合适的协成体的过程.
- 这一进步为通过共结晶加速药物特性增强提供了宝贵的见解.
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