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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/austint/tanimoto-random-features-neurips23

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Tanimoto Random Features for Scalable Molecular Machine Learning

https://github.com/automl-research/pas-ogb

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Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search

https://github.com/avakanski/Fatigue-Life-Prediction

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Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning

https://github.com/avakanski/creep-uncertainty-quantification

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Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks

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

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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

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Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

https://github.com/baratilab/vid2visc

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Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction

https://github.com/benatorc/OTGNN

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Optimal Transport Graph Neural Networks

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

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Path-Augmented Graph Transformer Network

https://github.com/benfinkelshtein/equivarianceeverywhere

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Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models

https://github.com/benjaminnNgo/ScalingTGNs

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Towards Neural Scaling Laws for Foundation Models on Temporal Graphs

https://github.com/bingsu12/momu

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A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language

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

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Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction

https://github.com/biomedsciai/biomed-multi-alignment

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MAMMAL -- Molecular Aligned Multi-Modal Architecture and Language

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

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Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties

https://github.com/bowenwang77/DR-Label

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DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction

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

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Neural Message Passing for Quantum Chemistry

https://github.com/briangodwinlim/sir-gcn

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Contextualized Messages Boost Graph Representations

https://github.com/brozosc/graph-neural-networks-for-surfactant-multi-property-prediction

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Graph Neural Networks for Surfactant Multi-Property Prediction

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

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

https://github.com/cfifty/ignite

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

https://github.com/changwenxu98/cloud

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CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning

https://github.com/chao1224/proteindt

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A Text-guided Protein Design Framework

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

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Structured Multi-task Learning for Molecular Property Prediction

https://github.com/chenlong164/peit

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

https://github.com/chnq/llm4mol

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Can Large Language Models Empower Molecular Property Prediction?

https://github.com/clmfap/clmfap

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

https://github.com/clott3/sib-cl

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Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science

https://github.com/coleygroup/polymer-chemprop-data

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A graph representation of molecular ensembles for polymer property prediction

https://github.com/coleygroup/sparrow

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An algorithmic framework for synthetic cost-aware decision making in molecular design

https://github.com/conf20/egg

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Embedding Graphs on Grassmann Manifold

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

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Molecular geometric deep learning

https://github.com/cubert-hyperspectral/cuvis.ai

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Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and Classification

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

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

https://github.com/danilonumeroso/MEG

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Explaining Deep Graph Networks with Molecular Counterfactuals

https://github.com/davzha/multiset-equivariance

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Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation

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