https://github.com/PaddlePaddle/PaddleHelix
공식 코드LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction
Papers with Code 아카이브
Property Prediction
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Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PolymathicAI/AstroCLIP
공식 코드https://github.com/QizhiPei/Awesome-Biomolecule-Language-Cross-Modeling
공식 코드https://github.com/QizhiPei/BioT5
공식 코드Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
https://github.com/Ramprasad-Group/copolymer_informatics
공식 코드https://github.com/SINGROUP/dscribe
공식 코드DScribe: Library of Descriptors for Machine Learning in Materials Science
https://github.com/TommyDzh/RingFormer
공식 코드RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction
https://github.com/TrustAI/testRNN
공식 코드testRNN: Coverage-guided Testing on Recurrent Neural Networks
https://github.com/UlrikFriisJensen/CHILI
공식 코드https://github.com/Vencent-Won/KRGTS-public
공식 코드Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction
https://github.com/Wenlin-Chen/ADKF-IFT
공식 코드Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
https://github.com/YKQ98/Matformer
공식 코드Periodic Graph Transformers for Crystal Material Property Prediction
https://github.com/Yasir-Ghunaim/efficient-atom
공식 코드Towards Data-Efficient Pretraining for Atomic Property Prediction
https://github.com/Yinghao-Li/UncertaintyBenchmark
공식 코드MUBen: Benchmarking the Uncertainty of Molecular Representation Models
https://github.com/ZJU-Fangyin/KCL
공식 코드Molecular Contrastive Learning with Chemical Element Knowledge Graph
https://github.com/ZeXingZ/DIG-Mol
공식 코드Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction
https://github.com/Zhenger959/MoleculeCLA
공식 코드MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis
https://github.com/aalto-quml/pipe
공식 코드https://github.com/acharkq/prott3
공식 코드ProtT3: Protein-to-Text Generation for Text-based Protein Understanding
https://github.com/addtt/object-centric-library
공식 코드Generalization and Robustness Implications in Object-Centric Learning
https://github.com/agave233/GeomGCL
공식 코드GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction
https://github.com/ahmed-a-a-elhag/gad
공식 코드https://github.com/ahmedesamy/dsgrl
공식 코드https://github.com/ai-hpc-research-team/git-mol
공식 코드GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text
https://github.com/aidotse/leakpro
공식 코드Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy
https://github.com/aimat-lab/megan_global_explanations
공식 코드Global Concept Explanations for Graphs by Contrastive Learning
https://github.com/airi-institute/smm
공식 코드https://github.com/aklipf/pegnn
공식 코드Equivariant Message Passing Neural Network for Crystal Material Discovery
https://github.com/amal-gader/genic
공식 코드GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
https://github.com/amitaysicherman/reactembed
공식 코드https://github.com/andoree/smm4h_2021_classification
공식 코드Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification
https://github.com/arahosu/musicslots
공식 코드https://github.com/arch4ngel21/scikit-fingerprints
공식 코드A Python library for efficient computation of molecular fingerprints
https://github.com/artemisp/semantic-norms
공식 코드Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction
https://github.com/ask-berkeley/coarsenconf
공식 코드CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation
https://github.com/aspuru-guzik-group/assessing_mol_prediction_confidence
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.