Mehrad Ansari,Andrew D White
Mehrad Ansari
Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical...
Chebifier: automating semantic classification in ChEBI to accelerate data-driven discovery [0.03%]
Chebifier：自动化ChEBI的语义分类以加速数据驱动发现
Martin Glauer,Fabian Neuhaus,Simon Flügel et al.
Martin Glauer et al.
Connecting chemical structural representations with meaningful categories and semantic annotations representing existing knowledge enables data-driven digital discovery from chemistry data. Ontologies are semantic annotation resources that ...
Benchmarking machine-readable vectors of chemical reactions on computed activation barriers [0.03%]
基于计算活化能垒的机器可读化学反应向量基准测试
Puck van Gerwen,Ksenia R Briling,Yannick Calvino Alonso et al.
Puck van Gerwen et al.
In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging ...
Predicting small molecules solubility on endpoint devices using deep ensemble neural networks [0.03%]
基于深度集成神经网络在端侧设备预测小分子溶解度
Mayk Caldas Ramos,Andrew D White
Mayk Caldas Ramos
Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively p...
Extended similarity methods for efficient data mining in imaging mass spectrometry [0.03%]
扩展相似性方法在成像质谱数据挖掘中的应用研究
Nicholas R Ellin,Yingchan Guo,Ramón Alain Miranda-Quintana et al.
Nicholas R Ellin et al.
Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at eac...
Seoin Back,Alán Aspuru-Guzik,Michele Ceriotti et al.
Seoin Back et al.
In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to addr...
Albert Sabadell-Rendón,Kamila Kaźmierczak,Santiago Morandi et al.
Albert Sabadell-Rendón et al.
Multiscale techniques integrating detailed atomistic information on materials and reactions to predict the performance of heterogeneous catalytic full-scale reactors have been suggested but lack seamless implementation. The largest challeng...
Deep generative design of porous organic cages via a variational autoencoder [0.03%]
基于变分自动编码器的多孔有机笼材料的深度生成设计
Jiajun Zhou,Austin Mroz,Kim E Jelfs
Jiajun Zhou
Porous organic cages (POCs) are a class of porous molecular materials characterised by their tunable, intrinsic porosity; this functional property makes them candidates for applications including guest storage and separation. Typically form...
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon [0.03%]
大型语言模型黑客松回顾：LLMs如何变革材料科学和化学的14个实例
Kevin Maik Jablonka,Qianxiang Ai,Alexander Al-Feghali et al.
Kevin Maik Jablonka et al.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This...
Markus Orsi,Daniel Probst,Philippe Schwaller et al.
Markus Orsi et al.
Chemical space maps help visualize similarities within molecular sets. However, there are many different molecular similarity measures resulting in a confusing number of possible comparisons. To overcome this limitation, we exploit the fact...