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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

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化学死亡 (ChemMORT):一个使用深度学习和多目标粒子群集优化的自动ADMET优化平台.

Jia-Cai Yi^1,2, Zi-Yi Yang², Wen-Tao Zhao¹

¹School of Computer Science, National University of Defense Technology, Changsha 410073, Hunan, PR China.

Briefings in bioinformatics

|February 22, 2024

概括

此摘要是机器生成的。

这项研究介绍了ChemMORT,这是一个优化药物吸收,分发,新陈代谢,消除和毒性 (ADMET) 特性的平台. 通过改善ADMET配置文件,而不会牺牲疗效,ChemMORT提高了药物发现.

关键词:

在ADMET的评估中,ADMET的评价.深度学习是一种深度学习.这是一个反向的QSAR.领先优化优化领先优化粒子群集优化粒子群集优化可逆分子表示的可逆分子表示.基层结构的修改变更

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科学领域:

计算化学是一种计算化学.
药物的发现和开发.
药品化学药品化学是一个

背景情况:

药物发现是昂贵和耗时的,ADMET性能差导致高达50%的失败.
优化多个ADMET参数是具有挑战性的,因为巨大的化学空间和有限的专家知识.

研究的目的:

开发一个计算平台,ChemMORT,用于同时优化多个ADMET端点.
为了确保在ADMET属性优化期间保持药物的效力.

主要方法:

化学MORT集成了三个模块:SMILES编码器,描述解码器和分子优化器.
在SMILES编码器生成512维的分子向量.
描述器解码器重建分子结构,分子优化器使用粒子群优化和反向QSAR原理来改进ADMET属性.

主要成果:

化学MORT有效地优化了ADMET的特性,同时保持了生物活性.
该平台在涉及多 (ADP-ribose) 聚合酶-1 抑制剂的案例研究中展示了实用性.

结论:

ChemMORT为改善药物候选人的ADMET配置文件提供了一个有价值的工具.
这个平台可以通过解决关键的发展挑战来加速药物发现.