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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/junxia97/mole-bert

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A Systematic Survey of Chemical Pre-trained Models

https://github.com/kaidic/gst

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Learning Large Graph Property Prediction via Graph Segment Training

https://github.com/kdmsit/crysgnn

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CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials

https://github.com/kdmsit/crysmmnet

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CrysMMNet: Multimodal Representation for Crystal Property Prediction

https://github.com/keiradams/chiro

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Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations

https://github.com/kimsu55/toxexpert

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Learning Topology-Specific Experts for Molecular Property Prediction

https://github.com/krishnaswamylab/dymag

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DYMAG: Rethinking Message Passing Using Dynamical-systems-based Waveforms

https://github.com/language-plus-molecules/lpm-24-dataset

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L+M-24: Building a Dataset for Language + Molecules @ ACL 2024

https://github.com/lars-research/3d-pgt

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Automated 3D Pre-Training for Molecular Property Prediction

https://github.com/lars-research/pacia

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PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction

https://github.com/larsbratholm/champs_kaggle

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A community-powered search of machine learning strategy space to find NMR property prediction models

https://github.com/lcmd-epfl/equireact

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3DReact: Geometric deep learning for chemical reactions

https://github.com/learningmatter-mit/NeuralForceField

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Molecular machine learning with conformer ensembles

https://github.com/learningmatter-mit/geom

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GEOM: Energy-annotated molecular conformations for property prediction and molecular generation

https://github.com/learningmatter-mit/matex

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Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules

https://github.com/lhirschfeld/ChempropUncertaintyQuantification

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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

https://github.com/lihan97/kpgt

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KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction

https://github.com/linusbao/MoSE

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Homomorphism Counts as Structural Encodings for Graph Learning

https://github.com/liugangcode/GREA

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Graph Rationalization with Environment-based Augmentations

https://github.com/liugangcode/InfoAlign

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Learning Molecular Representation in a Cell

https://github.com/liugangcode/data_centric_transfer

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Data-Centric Learning from Unlabeled Graphs with Diffusion Model

https://github.com/lrcfmd/Site-Net-DeepInfoMax

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Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics

https://github.com/luispintoc/unlocking-chemical-insights

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Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder Layers

https://github.com/luoyk1999/molecular-homology

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Improving Self-supervised Molecular Representation Learning using Persistent Homology

https://github.com/lvkd84/graphfp

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Fragment-based Pretraining and Finetuning on Molecular Graphs

https://github.com/mahindrautela/deepgenerator_compositematerialgwrepresentations

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Towards deep generation of guided wave representations for composite materials

https://github.com/maplightrx/maplight-tdc

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ADMET property prediction through combinations of molecular fingerprints

https://github.com/marko-petkovic/porousequivariantnetworks

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Equivariant Parameter Sharing for Porous Crystalline Materials

https://github.com/marko-petkovic/zeolite-property-prediction

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Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites

https://github.com/masterai-eam/darwin

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DARWIN 1.5: Large Language Models as Materials Science Adapted Learners

https://github.com/maxischuh/twinbooster

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TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction

https://github.com/maxxxzdn/implicit-steerable-kernels

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Implicit Convolutional Kernels for Steerable CNNs

https://github.com/mehmetfdemirel/aware

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Attentive Walk-Aggregating Graph Neural Networks

https://github.com/meliao/rotation-invariant-random-features

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Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds

https://github.com/microsoft/DVMP

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Dual-view Molecule Pre-training

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

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Impact of SMILES Notational Inconsistencies on Chemical Language Model Performance

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