相关实验视频
Updated: Jun 24, 2025
11:41
Rapid Analysis and Exploration of Fluorescence Microscopy Images
Published on: March 19, 2014
12.3K
玻璃盒子和黑盒子机器学习方法利用分子的组合描述器在药物发现中,并帮助药物化学家
Barry Robson1,2, Richard Cooper3,4
1Ingine Inc., 2723 Rocklyn Road, Cleveland, OH-44122, USA.
ChemMedChem
|June 5, 2024
概括
新的概率AI方法为药物发现合成提供了可解释的见解. 这些"玻璃盒子"模型补充了"黑盒子"AI,为开发新药的化学家提供了清晰,可量化的预测.
科学领域:
- 药用化学 医学化学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能工具指导药物发现合成,但通常充当"黑子" (BB),对预测提供有限的洞察力.
- 可解释的AI (XAI) 方法存在,但可能缺乏熟悉的概率测量,并依赖于任意的初始权重.
- 处理不平衡的数据集,其中的无活性化合物比活性化合物多得多,是机器学习用于药物发现的常见挑战.
研究的目的:
- 引入和评估一种概率的"玻璃盒子" (GB) AI方法,作为药物发现合成中现有的BB和XAI方法的补充方法.
- 证明概率方法可以提供可解释的指导方针,用于在随后的合成步骤中选择化学组.
- 使用概率学术术语量化化学结构和活动之间的关系,增强对AI预测的理解.
主要方法:
- 在两种已知类型的BB AI方法和一种新的概率性GB方法之间进行了盲测试比较.
- 焦点是GB框架内最简单的解释模型的预测能力,特别是对组成数据.
- 在真实世界的数据集上评估了性能,解决了高比例非活性化合物的挑战.
主要成果:
- 概率的GB方法显示了与BB方法相比较的预测能力.
- GB方法提供了明确的概率指南,在未来的合成中包括或避免哪些化学组.
- 这种方法即使在高度不平衡的数据集中也被证明是有效的,这在药物发现中很常见.
结论:
- 可能性的"玻璃盒"人工智能方法为药物化学中的"黑盒"人工智能提供了有价值的,可解释的替代品或补充.
- 这些方法通过提供可量化的,概率性的见解来增强对人工智能驱动的药物发现的理解.
- 开发的方法成功地解决了用于药物发现的机器学习的关键挑战,包括数据不平衡和模型可解释性.
相关概念视频
02:00Molecular Models
38.2K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.2K
01:26Drug Discovery: Overview
7.8K
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...
7.8K
01:21Mechanistic Models: Overview of Compartment Models
80
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
80
01:14Model Approaches for Pharmacokinetic Data: Compartment Models
91
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Two primary types of compartment models are recognized: mammillary and catenary. The more...
91
01:28Structure-Activity Relationships and Drug Design
699
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...
699