Papers with Code 아카이브

Atomistic Description

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

3

평가표

0

보존된 평가 기록

0

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/mizuno-group/notationalinconsistency

공식 코드

Impact of SMILES Notational Inconsistencies on Chemical Language Model Performance

https://github.com/molex2024/molex

공식 코드

Unveiling Molecular Secrets: An LLM-Augmented Linear Model for Explainable and Calibratable Molecular Property Prediction

https://github.com/nyushcs/moleculargpt

공식 코드

MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

https://github.com/oleneyl/geometry-aware-transformer

공식 코드

GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning

https://github.com/onurboyar/Crystal-LSBO

공식 코드

Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization

https://github.com/paperswithcode/galai

공식 코드

Galactica: A Large Language Model for Science

https://github.com/peterbjorgensen/msgnet

공식 코드

Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors

https://github.com/peterbjorgensen/vorosym

공식 코드

Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors

https://github.com/pfriedri/pcdiff-implant

공식 코드

Point Cloud Diffusion Models for Automatic Implant Generation

https://github.com/pharmolix/openbiomed

공식 코드

Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge

https://github.com/pnnl/FragNet

공식 코드

FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability

https://github.com/pnnl/cactus

공식 코드

CACTUS: Chemistry Agent Connecting Tool-Usage to Science

https://github.com/qizhipei/3d-molt5

공식 코드

3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling

https://github.com/rampasek/GraphGPS

공식 코드

Recipe for a General, Powerful, Scalable Graph Transformer

https://github.com/rampasek/HGNet

공식 코드

Hierarchical graph neural nets can capture long-range interactions

https://github.com/ryanchen227/lemon

공식 코드

Molecular Graph Contrastive Learning with Line Graph

https://github.com/scikit-fingerprints/scikit-fingerprints

공식 코드

Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python

https://github.com/sds-lab/sliceformer

공식 코드

Sliceformer: Make Multi-head Attention as Simple as Sorting in Discriminative Tasks

https://github.com/seojin-kim/hi-mol

공식 코드

Data-Efficient Molecular Generation with Hierarchical Textual Inversion

https://github.com/seyonechithrananda/bert-loves-chemistry

공식 코드

ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

https://github.com/sgfin/molecule_ge_coordinated_embeddings

공식 코드

Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profiles

https://github.com/shamim-hussain/tgt

공식 코드

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

https://github.com/shehzaidi/pre-training-via-denoising

공식 코드

Pre-training via Denoising for Molecular Property Prediction

https://github.com/shuix007/HMGNN

공식 코드

Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties

https://github.com/sichao-li/diverse-explanations-physical-sciences

공식 코드

Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences

https://github.com/sjiang87/deephyper

공식 코드

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

https://github.com/smiles724/bottleneck

공식 코드

Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions

https://github.com/smiles724/brot

공식 코드

Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport

https://github.com/smu-tao-group/ADMET_XGBoost

공식 코드

Accurate ADMET Prediction with XGBoost

https://github.com/snap-stanford/pretrain-gnns/

공식 코드

Strategies for Pre-training Graph Neural Networks

https://github.com/snudatalab/AugWard

공식 코드

AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification

https://github.com/soumyasanyal/mt-cgcnn

공식 코드

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

https://github.com/sshaghayeghs/llama-vs-chatgpt

공식 코드

Can Large Language Models Understand Molecules?

https://github.com/sshaghayeghs/llama-vs-gpt

공식 코드

Can Large Language Models Understand Molecules?

https://github.com/swansonk14/chemprop

공식 코드

Analyzing Learned Molecular Representations for Property Prediction

https://github.com/tech-srl/how_attentive_are_gats

공식 코드

How Attentive are Graph Attention Networks?

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.