Results 241 to 250 of about 6,104,936 (275)

Graph-Aware AURALSTM: An Attentive Unified Representation Architecture with BiLSTM for Enhanced Molecular Property Prediction. [PDF]

Mol Divers
Pala MA.
europepmc   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Chain-aware graph neural networks for molecular property prediction. [PDF]

Bioinformatics
Wang H, Zhang A, Zhong Y, Tang J, Zhang K, Li P.   +5 more
europepmc   +1 more source

Prototype-based contrastive substructure identification for molecular property prediction. [PDF]

Brief Bioinform
He G, Liu S, Liu Z, Wang C, Zhang K, Li H.   +5 more
europepmc   +1 more source

Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design. [PDF]

J Cheminform
Masood MA, Kaski S, Cui T.
europepmc   +1 more source

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Molecular pretraining models towards molecular property prediction

Science China Information Sciences
Balachandran Manavalan, Leyi Wei, Junru Jin   +2 more
exaly   +3 more sources

Molecular sharing and molecular-specific representations for multimodal molecular property prediction

Applied Soft Computing Journal
Xuecong Tian
exaly   +2 more sources

TransFoxMol: predicting molecular property with focused attention

Briefings in Bioinformatics, 2023
Abstract Predicting the biological properties of molecules is crucial in computer-aided drug development, yet it’s often impeded by data scarcity and imbalance in many practical applications. Existing approaches are based on self-supervised learning or 3D data and using an increasing number of parameters to improve performance.
Jian Gao, Zheyuan Shen, Yufeng Xie, Jialiang Lu, Yang Lu, Sikang Chen, Qingyu Bian, Yue Guo 0008, Liteng Shen, Jian Wu 0001, Binbin Zhou 0005, Tingjun Hou, Qiaojun He, Jinxin Che, Xiaowu Dong   +14 more
openaire   +2 more sources

Prediction of properties of chiral compounds by molecular topology

Journal of Molecular Graphics and Modelling, 1998
A common assumption in chemistry is that chiral behavior is associated with 3-D geometry. However, chiral information is related to symmetry, which allows the topological handling of chiral atoms by weighted graphs and the calculation of new descriptors that give a weight to the corresponding entry in the main diagonal of the topological matrix.
J V, de Julián-Ortiz, C, de Gregorio Alapont, I, Ríos-Santamarina, R, García-Doménech, J, Gálvez   +4 more
openaire   +2 more sources

A multiscale molecular structural neural network for molecular property prediction

Molecular Diversity
Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time and computing power.
Zhiwei, Shi, Miao, Ma, Hanyang, Ning, Bo, Yang, Jingshuang, Dang   +4 more
openaire   +2 more sources