Papers with Code 아카이브

Molecular Property Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/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/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/MoleculeProp

공식 코드

Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery

https://github.com/divelab/MoleculeX

공식 코드

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks

https://github.com/dmirlab-group/sci

공식 코드

Identifying Semantic Component for Robust Molecular Property Prediction

https://github.com/dmis-lab/molpla

공식 코드

MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints

https://github.com/dmitrykazhdan/gci

공식 코드

GCI: A (G)raph (C)oncept (I)nterpretation Framework

https://github.com/dongkikim95/d-sla

공식 코드

Graph Self-supervised Learning with Accurate Discrepancy Learning

https://github.com/dqwang122/rlhex

공식 코드

Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning

https://github.com/duyhominhnguyen/conan-fgw

공식 코드

Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks

https://github.com/einae-nd/moama-dev

공식 코드

Motif-aware Attribute Masking for Molecular Graph Pre-training

https://github.com/fabiankruger/molprivacy

공식 코드

Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy

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