摩洛优化器:用于基于碎片的药物设计的分子优化工具包
Adam Soffer1,2, Samuel Joshua Viswas1,2, Shahar Alon3
1Department of Chemistry, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
Molecules (Basel, Switzerland)
|January 11, 2024
概括
MolOptimizer是一个计算工具包,通过预测小分子结合值来加速药物发现. 它使用用户数据的机器学习来优化具有改进性质的候选药物.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 在药理学中的机器学习.
背景情况:
- 在药物发现中,击中到引入的优化阶段至关重要.
- 准确预测小分子结合亲和力对于识别可行的候选药物至关重要.
- 计算工具可以显著加快药物发现过程.
研究的目的:
- 为了介绍MolOptimizer,一个用户友好的计算工具包.
- 为了简化药物发现中的命中到的优化过程.
- 为了能够准确地预测新型小分子的结合值.
主要方法:
- MolOptimizer从用户提供的标记小分子数据集中提取功能.
- 机器学习模型在这些提取的特征上进行训练.
- 该工具包使用在Azure上托管的基于Web的服务器来实现访问性.
主要成果:
- MolOptimizer准确地预测了具有相似支架的新小分子的结合值.
- 该工具包有助于识别具有增强结合性质的候选药物.
- 计算方法加快了发现和开发时间表.
结论:
- 摩尔优化器是加速击中到领先优化的宝贵资源.
- 该工具包提高了鉴定候选药物的效率,并改善了结合性.
- 它的用户友好的界面和机器学习能力使其成为现代药物发现的重要工具.
相关概念视频
Drug Discovery: Overview
7.9K
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...
7.9K
Drug Biotransformation: Overview
2.4K
Pharmaceutical substances known as xenobiotics are predominantly lipophilic and nonionized. This enables them to permeate lipid bilayers, such as cell membranes, and interact with intracellular target receptors. Lipophilic drugs have an advantage in crossing biological barriers and reaching their intended sites of action. However, lipophilic drugs often have a restricted capacity for renal expulsion or elimination from the body. When these drugs enter the kidneys and undergo glomerular...
2.4K
Structure-Activity Relationships and Drug Design
726
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...
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...
726


