Database utility for cyclovoltammetry knowledge (DUCK): unified platform for electrochemical data [0.03%]
Diego Garay-Ruiz,Sergio Pablo-García,Han Hao et al.
Diego Garay-Ruiz et al.
Cyclic voltammetry (CV) is a valuable tool for electrochemistry, providing qualitative and quantitative information about redox processes occurring in solution. Despite its ubiquity, the lack of standardized reporting and sharing protocols,...
Glen M Hocky,Andrew D White
Glen M Hocky
Four years ago we wrote an article predicting the disruptive effect of large language models in the fields of chemical education and research. Here we review and grade our past predictions, give our perspective on some of the progress that ...
Wei Bin How,Pol Febrer,Sanggyu Chong et al.
Wei Bin How et al.
In the last few years several "universal" interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often compara...
Precision fragment addition: domain-specific DeepFrag2 models for smarter lead optimization [0.03%]
精准片段替换：药物分子领域专用DeepFrag2模型助力更智能的先导化合物优化
César R García-Jacas,Harrison Green,Shayne D Wierbowski et al.
César R García-Jacas et al.
This study introduces a series of machine-learning models based on DeepFrag, our previously published tool designed to guide small-molecule lead optimization through fragment addition. We demonstrate enhanced accuracy by training new DeepFr...
Scientific knowledge graph and ontology generation using open large language models [0.03%]
基于开源大规模语言模型的科学知识图和本体生成
Alexandru Oarga,Matthew Hart,Andres M Bran et al.
Alexandru Oarga et al.
Knowledge graphs (KGs) are powerful tools for structured information modeling, increasingly recognized for their potential to enhance the factuality and reasoning capabilities of Large Language Models (LLMs). However, in scientific domains,...
MC3D: the materials cloud computational database of experimentally known stoichiometric inorganics [0.03%]
材料云：已知化学式的无机物计算数据库
Sebastiaan P Huber,Michail Minotakis,Marnik Bercx et al.
Sebastiaan P Huber et al.
Density-functional theory (DFT) is a widely used method to compute properties of materials, which are often collected in databases and serve as valuable starting points for further studies. In this article, we present the Materials Cloud Th...
A simple compound prioritization method for drug discovery considering multi-target binding [0.03%]
一种用于药物发现的基于多靶点结合的简便化合物优先级确定方法
Alžbeta Kubincová,David L Mobley
Alžbeta Kubincová
Active learning is an emerging paradigm used to help accelerating drug discovery, but most prior applications seek solely to optimize potency, whereas multiple properties influence a compound's utility as a drug candidate. We introduce a me...
Data augmentation in a triple transformer loop retrosynthesis model [0.03%]
基于三重变压器循环的 retrosynthesis 模型中的数据增强方法
Yves Grandjean,David Kreutter,Jean-Louis Reymond
Yves Grandjean
Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits their usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied ret...
Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction [0.03%]
评估量子力学描述符在物理化学和生物属性预测中的性能
Alejandra Hinostroza Caldas,Artem Kokorin,Alexandre Tkatchenko et al.
Alejandra Hinostroza Caldas et al.
Machine learning (ML) approaches have drastically advanced the exploration of structure-property and property-property relationships in computer-aided drug discovery. A central challenge in this field is the identification of molecular desc...
Artem Mishchenko,Anupam Bhattacharya,Xiangwen Wang et al.
Artem Mishchenko et al.
This review explores the impact of deep learning (DL) techniques on understanding and predicting electronic structures in two-dimensional (2D) materials. We highlight unique computational challenges posed by 2D materials and discuss how DL ...