.png){kind=icon}
返回
Deductive machine learning models for product identification
.png){kind=icon}
OA
AI
DOI：10.1039/d3sc04909d.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deductive solution strategies are required in prediction scenarios that are under determined, when contradictory information is available, or more generally wherever one-to-many non-functional mappings occur. In contrast, most contemporary machine learning (ML) in the chemical sciences is inductive learning from example, with a fixed set of features. Chemical workflows are replete with situations requiring deduction, including many aspects of lab automation and spectral interpretation. Here, a general strategy is described for designing and training machine learning models capable of deduction that consists of combining individual inductive models into a larger deductive network. The training and testing of these models is demonstrated on the task of deducing reaction products from a mixture of spectral sources. The resulting models can distinguish between intended and unintended reaction outcomes and identify starting material based on a mixture of spectral sources. The models also perform well on tasks that they were not directly trained on, like performing structural inference using real rather than simulated spectral inputs, predicting minor products from named organic chemistry reactions, identifying reagents and isomers as plausible impurities, and handling missing or conflicting information. A new dataset of 1 124 043 simulated spectra that were generated to train these models is also distributed with this work. These findings demonstrate that deductive bottlenecks for chemical problems are not fundamentally insuperable for ML models. Machine learning models are developed that emulate the H deductive chemical reasoning processes associated with product identification from analytical spectra.
Keyword:
GENERATION
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.4
论文数:
1.7W
被引数:
9.3W
机构
引用论文
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules微笑，一种化学语言和信息系统。1.介绍方法和编码规则
PREAI
分享收藏Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization-Mass Spectrometry.png)
ANALYTICAL CHEMISTRYOAAI
ANALYTICAL CHEMISTRYIF6.7
OAAI分享收藏A survey of police officers encounters with sober, alcohol- and drug-intoxicated suspects in SwedenOAAI
OAAI分享收藏分享收藏Spectral deep learning for prediction and prospective validation of functional groupsCHEMICAL SCIENCEOAAI
CHEMICAL SCIENCEIF7.4
OAAI分享收藏
查看更多内容