化学信息学
产量(工程)
计算机科学
任务(项目管理)
机器学习
生化工程
可转让性
领域(数学)
人工智能
化学
工程类
数学
系统工程
材料科学
计算化学
冶金
纯数学
罗伊特
作者
Varvara Voinarovska,Mikhail A. Kabeshov,Dmytro Dudenko,Samuel Genheden,Igor V. Tetko
标识
DOI:10.1021/acs.jcim.3c01524
摘要
Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.
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