Why Deep Models Often Cannot Beat Non-deep Counterparts on Molecular Property Prediction? [PDF]
, 2023 Molecular property prediction (MPP) is a crucial task in the drug discovery pipeline, which has recently gained considerable attention thanks to advances in deep neural networks.
Stan Z., Li +3 more
core +1 more source
Heterogenous Ensemble of Models for Molecular Property Prediction
CoRR, 2022 Previous works have demonstrated the importance of considering different modalities on molecules, each of which provide a varied granularity of information for downstream property prediction tasks. Our method combines variants of the recent TransformerM architecture with Transformer, GNN, and ResNet backbone architectures.
Sajad Darabi +6 more
openaire +3 more sources
Causal Mechanism-Based Molecular Property Prediction [PDF]
Jisuanji gongchengIn the field of quantum chemistry, molecular property prediction is a fundamental and critical task, which is widely used in many fields such as drug discovery and chemical synthesis prediction.
CAI Ruichu, XU Zunhong, CHEN Daoxin, YANG Zhenhui, LI Zijian, HAO Zhifeng
doaj +1 more source
Quantitative evaluation of explainable graph neural networks for molecular property prediction
, 2022 Graph neural networks (GNNs) have received increasing attention because of their expressive power on topological data, but they are still criticized for their lack of interpretability.
Zheng, Shuangjia +3 more
core +1 more source
DEEP LEARNING FOR MOLECULAR PROPERTY PREDICTION [PDF]
, 2023 Drug discovery has always been a crucial task for society, and molecular property prediction is one of the fundamental problem. It is responsible for identifying the target properties or severe side-effects, so that certain molecules can be selected as ...
Ma, Hehuan
core +1 more source
Mol‐BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction [PDF]
Wireless Communications and Mobile Computing, 2021 Molecular property prediction is an essential task in drug discovery. Most computational approaches with deep learning techniques either focus on designing novel molecular representation or combining with some advanced models together. However, researchers pay fewer attention to the potential benefits in massive unlabeled molecular data (e.g., ZINC ...
Juncai Li, Xiaofei Jiang
openaire +2 more sources
Evidential meta-model for molecular property prediction
Bioinformatics, 2023 Abstract Motivation The usefulness of supervised molecular property prediction (MPP) is well-recognized in many applications. However, the insufficiency and the imbalance of labeled data make the learning problem difficult.
KyungPyo Ham, Lee Sael
openaire +2 more sources
Computer Aided Aroma Design. I. Molecular knowledge framework [PDF]
, 2008 Computer Aided Aroma Design (CAAD) is likely to become a hot issue as the REACH EC document targets many aroma compounds to require substitution. The two crucial steps in CAMD are the generation of candidate molecules and the estimation of properties ...
Nacef, Salif +5 more
core +1 more source
Algebraic graph-assisted bidirectional transformers for molecular property prediction
Nature Communications, 2021 Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Here, the authors propose an algebraic graph-assisted bidirectional transformer, which can incorporate massive unlabeled molecular data into ...
Dong Chen +6 more
doaj +1 more source
Autonomous, multi-property-driven molecular discovery: from predictions to measurements and back [PDF]
, 2023 A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties.
Charles J., McGill +17 more
core +2 more sources