https://github.com/lihan97/kpgt
공식 코드KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction
Papers with Code 아카이브
Molecular Property Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 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/linusbao/MoSE
공식 코드Homomorphism Counts as Structural Encodings for Graph Learning
https://github.com/liugangcode/InfoAlign
공식 코드https://github.com/lukecavabarrett/pna
공식 코드https://github.com/luoyk1999/molecular-homology
공식 코드Improving Self-supervised Molecular Representation Learning using Persistent Homology
https://github.com/max-veit/velociraptor
공식 코드Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
https://github.com/maxischuh/twinbooster
공식 코드https://github.com/maxxxzdn/implicit-steerable-kernels
공식 코드https://github.com/mehmetfdemirel/aware
공식 코드https://github.com/meliao/rotation-invariant-random-features
공식 코드Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds
https://github.com/microsoft/DVMP
공식 코드https://github.com/mims-harvard/TDC
공식 코드Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
https://github.com/mizuno-group/notationalinconsistency
공식 코드Impact of SMILES Notational Inconsistencies on Chemical Language Model Performance
https://github.com/molex2024/molex
공식 코드https://github.com/nyushcs/moleculargpt
공식 코드MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
https://github.com/oleneyl/geometry-aware-transformer
공식 코드https://github.com/paperswithcode/galai
공식 코드https://github.com/pfriedri/pcdiff-implant
공식 코드Point Cloud Diffusion Models for Automatic Implant Generation
https://github.com/pharmolix/openbiomed
공식 코드Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge
https://github.com/pnnl/FragNet
공식 코드https://github.com/pnnl/cactus
공식 코드https://github.com/qizhipei/3d-molt5
공식 코드3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling
https://github.com/rampasek/GraphGPS
공식 코드https://github.com/rampasek/HGNet
공식 코드Hierarchical graph neural nets can capture long-range interactions
https://github.com/ryanchen227/lemon
공식 코드https://github.com/scikit-fingerprints/scikit-fingerprints
공식 코드Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python
https://github.com/sds-lab/sliceformer
공식 코드Sliceformer: Make Multi-head Attention as Simple as Sorting in Discriminative Tasks
https://github.com/seojin-kim/hi-mol
공식 코드Data-Efficient Molecular Generation with Hierarchical Textual Inversion
https://github.com/seyonechithrananda/bert-loves-chemistry
공식 코드ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
https://github.com/sgfin/molecule_ge_coordinated_embeddings
공식 코드https://github.com/shamim-hussain/tgt
공식 코드https://github.com/shehzaidi/pre-training-via-denoising
공식 코드Pre-training via Denoising for Molecular Property Prediction
https://github.com/sichao-li/diverse-explanations-physical-sciences
공식 코드Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences
https://github.com/sjiang87/deephyper
공식 코드Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
https://github.com/smiles724/bottleneck
공식 코드https://github.com/smiles724/brot
공식 코드Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.