Papers with Code 아카이브
Molecular Property Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 18
- 보존된 평가 기록
- 224
Molecular property prediction is the task of predicting the properties of a molecule from its structure.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fengshikun/unimap
공식 코드https://github.com/g-benton/learning-invariances
공식 코드https://github.com/gaingod-xu/acmlproject
공식 코드https://github.com/genentech/molcap-arena
공식 코드https://github.com/georgelamb19/chempropBayes
공식 코드Bayesian Graph Neural Networks for Molecular Property Prediction
https://github.com/gmh14/geo-deg
공식 코드Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
https://github.com/gmum/chienn
공식 코드ChiENN: Embracing Molecular Chirality with Graph Neural Networks
https://github.com/gmum/progrest
공식 코드ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction
https://github.com/graphcore/ogb-lsc-pcqm4mv2
공식 코드GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction
https://github.com/hannesstark/3dinfomax
공식 코드https://github.com/henrium/molsets
공식 코드MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling
https://github.com/hhaji/funqg
공식 코드FunQG: Molecular Representation Learning Via Quotient Graphs
https://github.com/hicai-zju/gs-meta
공식 코드Graph Sampling-based Meta-Learning for Molecular Property Prediction
https://github.com/hubiodatalab/selformer
공식 코드SELFormer: Molecular Representation Learning via SELFIES Language Models
https://github.com/ibm/graph-space-gps
공식 코드https://github.com/ibm/graph_space_gps
공식 코드https://github.com/ibm/molformer
공식 코드Large-Scale Chemical Language Representations Capture Molecular Structure and Properties
https://github.com/idruglab/hignn
공식 코드https://github.com/j-adamczyk/apis_tox_dataset
공식 코드ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey bees
https://github.com/jacksonburns/chemeleon
공식 코드Descriptor-based Foundation Models for Molecular Property Prediction
https://github.com/jacksonburns/fastprop
공식 코드https://github.com/jfpuget/nvidia-pcqm4mv2
공식 코드Heterogenous Ensemble of Models for Molecular Property Prediction
https://github.com/jinhojsk515/SPMM
공식 코드Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model
https://github.com/johnathan-xie/sma
공식 코드Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
https://github.com/jpfolch/catechol_solvent_selection
공식 코드The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
https://github.com/jw9730/tokengt
공식 코드https://github.com/karolismart/dropgnn
공식 코드DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
https://github.com/keiradams/chiro
공식 코드Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
https://github.com/kimsu55/toxexpert
공식 코드Learning Topology-Specific Experts for Molecular Property Prediction
https://github.com/lars-research/3d-pgt
공식 코드https://github.com/lars-research/pacia
공식 코드PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction
https://github.com/larsbratholm/champs_kaggle
공식 코드A community-powered search of machine learning strategy space to find NMR property prediction models
https://github.com/learningmatter-mit/NeuralForceField
공식 코드https://github.com/lenscloth/grpe
공식 코드https://github.com/lhirschfeld/ChempropUncertaintyQuantification
공식 코드Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.