.png){kind=icon}
返回
Machine learning-aided generative molecular design
.png){kind=icon}
PRE
AI
DOI：10.1038/s42256-024-00843-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing machine learning models that can achieve this on the fly to the satisfaction of medicinal chemists remains a challenge owing to the enormous search space. Researchers have addressed de novo design of molecules by decomposing the problem into a series of tasks determined by design criteria. Here we provide a comprehensive overview of the current state of the art in molecular design using machine learning models as well as important design decisions, such as the choice of molecular representations, generative methods and optimization strategies. Subsequently, we present a collection of practical applications in which the reviewed methodologies have been experimentally validated, encompassing both academic and industrial efforts. Finally, we draw attention to the theoretical, computational and empirical challenges in deploying generative machine learning and highlight future opportunities to better align such approaches to achieve realistic drug discovery end points. Data-driven generative methods have the potential to greatly facilitate molecular design tasks for drug design.
Keyword:
DRUG DESIGN
NEURAL-NETWORK
DISCOVERY
TRANSFORMER
EXPLORATION
DERIVATIVES
SOLUBILITY
STRATEGIES
INHIBITORS
ALGORITHM
期刊
IF:
23.9
论文数:
1.3K
被引数:
1.5W
机构
引用论文
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimizationAutoGrow4: 一种用于从头药物设计和线索优化的开源遗传算法
OAAI
OAAI分享收藏分享收藏Sensorless Control of Z Source Inverter fed BLDC Motor Drive by FOC - DTC Hybrid Control Strategy Using Fuzzy Logic Controller采用模糊逻辑控制器的foc-dtc混合控制策略的Z源逆变器馈电BLDC电机驱动的无传感器控制
PREAI
分享收藏The art and practice of structure-based drug design: A molecular modeling perspective基于结构的药物设计的艺术与实践: 分子建模视角
PREAI
分享收藏CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures.png)
NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings在药物发现和开发环境中估算溶解度和渗透性的实验和计算方法
PREAI
分享收藏
查看更多内容