Papers with Code 아카이브

Atomistic Description

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

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보존된 평가 기록

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코드

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

https://github.com/agave233/GeomGCL

공식 코드

GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction

https://github.com/ahmed-a-a-elhag/gad

공식 코드

Graph Anisotropic Diffusion

https://github.com/aidotse/leakpro

공식 코드

Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy

https://github.com/alexhernandezgarcia/gflownet

공식 코드

Crystal-GFN: sampling crystals with desirable properties and constraints

https://github.com/amitaysicherman/reactembed

공식 코드

ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction Networks

https://github.com/andoree/smm4h_2021_classification

공식 코드

Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification

https://github.com/arch4ngel21/scikit-fingerprints

공식 코드

A Python library for efficient computation of molecular fingerprints

https://github.com/aspuru-guzik-group/assessing_mol_prediction_confidence

공식 코드

Assigning Confidence to Molecular Property Prediction

https://github.com/atomicarchitects/equiformer

공식 코드

Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

https://github.com/atomistic-machine-learning/SchNet

공식 코드

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

https://github.com/atomistic-machine-learning/schnetpack

공식 코드

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

https://github.com/austint/tanimoto-random-features-neurips23

공식 코드

Tanimoto Random Features for Scalable Molecular Machine Learning

https://github.com/awslabs/dgl-lifesci

공식 코드

DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science

https://github.com/awslabs/dgl-lifesci/blob/master/python/dgllife/model/model_zoo/mgcn_predictor.py

공식 코드

Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

https://github.com/benatorc/OTGNN

공식 코드

Optimal Transport Graph Neural Networks

https://github.com/benatorc/PA-Graph-Transformer

공식 코드

Path-Augmented Graph Transformer Network

https://github.com/benevolentAI/guacamol

공식 코드

GuacaMol: Benchmarking Models for De Novo Molecular Design

https://github.com/benevolentAI/guacamol_baselines

공식 코드

GuacaMol: Benchmarking Models for De Novo Molecular Design

https://github.com/bingsu12/momu

공식 코드

A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language

https://github.com/biomed-AI/MolRep

공식 코드

Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction

https://github.com/bkmi/equivariant-benchmark

공식 코드

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties

https://github.com/boqian-li/GarmentDreamer

공식 코드

GarmentDreamer: 3DGS Guided Garment Synthesis with Diverse Geometry and Texture Details

https://github.com/brain-research/mpnn

공식 코드

Neural Message Passing for Quantum Chemistry

https://github.com/bz99bz/m-3

공식 코드

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery

https://github.com/cfifty/ignite

공식 코드

Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction

https://github.com/chao1224/n_gram_graph

공식 코드

N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules

https://github.com/chao1224/sgnn-ebm

공식 코드

Structured Multi-task Learning for Molecular Property Prediction

https://github.com/chenlong164/peit

공식 코드

Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models

https://github.com/chnq/llm4mol

공식 코드

Can Large Language Models Empower Molecular Property Prediction?

https://github.com/clmfap/clmfap

공식 코드

CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic Screening

https://github.com/cs-bio/mol-gdl

공식 코드

Molecular geometric deep learning

https://github.com/cuent/ogb-kdd21

공식 코드

On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction

https://github.com/deepgraphlearning/torchdrug

공식 코드

TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery

https://github.com/dengjianyuan/Survey_AI_Drug_Discovery

공식 코드

Artificial Intelligence in Drug Discovery: Applications and Techniques

https://github.com/dengjianyuan/respite_mpp

공식 코드

Unraveling Key Elements Underlying Molecular Property Prediction: A Systematic Study

https://github.com/divelab/AIRS

공식 코드

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

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