https://github.com/zihao-wang/reactionood
공식 코드Towards out-of-distribution generalizable predictions of chemical kinetics properties
Papers with Code 아카이브
Property Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
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/zjunlp/mol-instructions
공식 코드Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
https://github.com/znavoyan/vae-embeddings
공식 코드Improving VAE based molecular representations for compound property prediction
https://github.com/znavoyan/vaedatasets
공식 코드Improving VAE based molecular representations for compound property prediction
https://github.com/zoisboukouvalas/pyiva
공식 코드https://huggingface.co/ibm/materials.smi-ted
공식 코드A Large Encoder-Decoder Family of Foundation Models For Chemical Language
https://huggingface.co/zequnl/molxpt
공식 코드MolXPT: Wrapping Molecules with Text for Generative Pre-training
https://github.com/AJS1NGH/Predicting-Molecular-Properties-Challenge
https://github.com/AustinApple/SSVAE-for-electrolyte-molecule-design
https://github.com/BrightBlueCheese/Modeling-of-Time-varying-Wireless-Communication-Channel-with-Fading-and-Shadowing
https://github.com/CoderPat/OpenGNN
https://github.com/CompRhys/roost
Materials Representation and Transfer Learning for Multi-Property Prediction
https://github.com/HHousen/object-discovery-pytorch
https://github.com/Jh-SYSU/MolRep
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
https://github.com/LRacoci/permutation-graphml
https://github.com/MUmarJaved/MultiAgent-Distributed-Reinforcement-Learning
https://github.com/Michedev/slot-attention-pytorch
https://github.com/Microsoft/gated-graph-neural-network-samples
https://github.com/Namkyeong/NoisyNodes_Pytorch
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
https://github.com/QData/DeepLearning4-ProteinSequenceProcessing
https://github.com/QData/DeepProtein
https://github.com/Samyu0304/thought-propagation
https://github.com/Saro00/DGN
https://github.com/SylwiaNowakowska/LLM_Fine_Tuning_Molecular_Properties
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
https://github.com/Tony-Y/cgnn
https://github.com/XuHangkun/Compound-Protein-Interaction
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
https://github.com/Yindong-Zhang/GraphConvolutionDrugTargetInteration
https://github.com/a-imamshah/savi-pytorch
https://github.com/a-imamshah/slotattention.jl
https://github.com/adityabingi/Slot-Attention
https://github.com/ai-hpc-research-team/slm4mol
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
https://github.com/aimat-lab/graph_attention_student
Global Concept Explanations for Graphs by Contrastive Learning
https://github.com/anonymous20201002/chemprop
Analyzing Learned Molecular Representations for Property Prediction
https://github.com/ardigen/MAT
https://github.com/chao1224/chatdrug
https://github.com/chao1224/geossl
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.