Tango*: constrained synthesis planning using chemically informed value functions [0.03%]
基于化学信息的价值函数的约束合成规划:TANGO*算法
Daniel Armstrong,Zlatko Jončev,Jeff Guo et al.
Daniel Armstrong et al.
Computer-aided synthesis planning (CASP) has made significant strides in generating retrosynthetic pathways for simple molecules in a non-constrained fashion. Recent work has introduced specialized bidirectional search algorithms to find sy...
Ask Hjorth Larsen,Mikael J Kuisma,Tara M Boland et al.
Ask Hjorth Larsen et al.
We introduce Taskblaster, a generic and lightweight Python framework for composing, executing, and managing computational workflows with automated error handling. Taskblaster supports dynamic workflows including flow control using branches ...
Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups [0.03%]
一种用于子群多目标优化来识别异常材料的描述规则集合的方法
Lucas Foppa,Matthias Scheffler
Lucas Foppa
Useful materials are often statistically exceptional and they might be overlooked by artificial intelligence (AI) models that attempt to describe all materials simultaneously. These global models perform well for the majority of materials, ...
An automated photo-isomerisation and kinetics characterisation system for molecular photoswitches [0.03%]
分子光开关的自动化异构化和动力学表征系统
Jacob Lynge Elholm,Paulius Baronas,Paul A Gueben et al.
Jacob Lynge Elholm et al.
Physical chemistry parameters such as absorbance, photoconversion quantum yield, and thermal half-lives are crucial for the characterisation of new molecular photoswitch systems. In a traditional workflow, these parameters are challenging a...
ACES-GNN: can graph neural network learn to explain activity cliffs? [0.03%]
ACES-GNN：图神经网络能学习解释活性悬崖吗？
Xu Chen,Dazhou Yu,Liang Zhao et al.
Xu Chen et al.
Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activi...
Da Teng,Vanessa J Meraz,Akashnathan Aranganathan et al.
Da Teng et al.
We introduce , an open-source Python package that implements an improved and automated version of our previous AlphaFold2-RAVE protocol. AlphaFold2-RAVE integrates machine learning-based structure prediction with physics-driven sampling to ...
Alex M Ganose,Hrushikesh Sahasrabuddhe,Mark Asta et al.
Alex M Ganose et al.
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of "universal" machine learning models. Wh...
Chen Zhou,Marlen Neubert,Yuri Koide et al.
Chen Zhou et al.
Constructing datasets representative of the target domain is essential for training effective machine learning models. Active learning (AL) is a promising method that iteratively extends training data to enhance model performance while mini...
Marvin Alberts,Federico Zipoli,Teodoro Laino
Marvin Alberts
Automated structure elucidation from infrared (IR) spectra represents a significant breakthrough in analytical chemistry, having recently gained momentum through the application of Transformer-based language models. In this work, we improve...
MOFChecker: a package for validating and correcting metal-organic framework (MOF) structures [0.03%]
MOFChecker：一个用于验证和修正金属有机框架（MOF）结构的包
Xin Jin,Kevin Maik Jablonka,Elias Moubarak et al.
Xin Jin et al.
Metal-organic frameworks are promising porous materials for applications like gas adsorption, separation, transportation, and photocatalysis, but their large-scale computational screening requires high-quality, computation-ready structural ...