Results 21 to 30 of about 6,104,936 (275)

TGF-M: Topology-augmented geometric features enhance molecular property prediction. [PDF]

PLoS Computational Biology
Accurate prediction of molecular properties is a key component of Artificial Intelligence-driven Drug Design (AIDD). Despite significant progress in improving these predictive models, balancing accuracy with computational complexity remains a challenge ...
Wei He, Xu Tian, Xue Li, Peifu Han, Shuang Wang, Lin Liu, Tao Song   +6 more
doaj   +2 more sources

Geometry-aware lightweight convolutional network for efficient molecular property prediction [PDF]

Scientific Reports
Molecular representation learning (MRL) has demonstrated significant potential in various fields such as drug discovery, particularly in extracting molecular features under limited supervision.
Huan Zhang, Guifei Zhou, Mingjing Tang, Jin Li, Hang Zhu, Chunyan Li   +5 more
doaj   +2 more sources

A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability [PDF]

Nature Communications
The major challenges in drug development stem from frequent structure-activity cliffs and unknown drug properties, which are expensive and time-consuming to estimate, contributing to a high rate of failures and substantial unavoidable costs in the ...
Jianbo Qiao, Junru Jin, Ding Wang, Saisai Teng, Junyu Zhang, Xuetong Yang, Yuhang Liu, Yu Wang, Lizhen Cui, Quan Zou, Ran Su, Leyi Wei   +11 more
doaj   +2 more sources

Knowledge Distillation for Molecular Property Prediction: A Scalability Analysis [PDF]

Advanced Science
Knowledge distillation (KD) is a powerful model compression technique that transfers knowledge from complex teacher models to compact student models, reducing computational costs while preserving predictive accuracy. This study investigated KD's efficacy
Rahul Sheshanarayana, Fengqi You
doaj   +2 more sources

Information-theoretic multi-scale geometric pre-training for enhanced molecular property prediction. [PDF]

PLoS ONE
Maximizing information transfer across different structural scales is critical for effective molecular representation learning. Current molecular graph neural networks fail to fully capture the multi-scale nature of molecular geometry, leading to ...
Xiaoyu Hu, Xiuyuan Zhao, Jiyuan Wang, Yongbin Yang   +3 more
doaj   +2 more sources

Molecular property prediction based on graph structure learning. [PDF]

Bioinformatics
Abstract Motivation Molecular property prediction (MPP) is a fundamental but challenging task in the computer-aided drug discovery process. More and more recent works employ different graph-based models for MPP, which have achieved considerable progress in improving prediction performance.
Zhao B, Xu W, Guan J, Zhou S.
europepmc   +5 more sources

Assigning confidence to molecular property prediction [PDF]

Expert Opinion on Drug Discovery, 2021
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design. Recently, machine learning techniques have emerged as a powerful and cost-effective strategy to learn from existing datasets and perform predictions on unseen molecules. Accordingly,
AkshatKumar Nigam, Robert Pollice, Matthew F. D. Hurley, Riley J. Hickman, Matteo Aldeghi, Naruki Yoshikawa, Seyone Chithrananda, Vincent A. Voelz, Alán Aspuru-Guzik   +8 more
openaire   +4 more sources

MolPROP: Molecular Property prediction with multimodal language and graph fusion

Journal of Cheminformatics
Pretrained deep learning models self-supervised on large datasets of language, image, and graph representations are often fine-tuned on downstream tasks and have demonstrated remarkable adaptability in a variety of applications including chatbots ...
Zachary A. Rollins, Alan C. Cheng, Essam Metwally   +2 more
doaj   +2 more sources

Molecular modeling for physical property prediction [PDF]

, 2006
Multiscale modeling is becoming the standard approach for process study in a broader framework that promotes computer aided integrated product and process design. In addition to usual purity requirements, end products must meet new constraints in terms of environmental impact, safety of goods and people, specific properties.
Gerbaud, Vincent, Joulia, Xavier
openaire   +2 more sources

Self-Supervised Learning for Molecular Property Prediction [PDF]

, 2021
Predicting molecular properties remains a challenging task with numerous potential applications, notably in drug discovery. Recently, the development of deep learning, combined with rising amounts of data, has provided powerful tools to build predictive ...
Laurent, Dillard
core   +1 more source