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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/AGH-ML-and-Chemoinformatics-Group/peptides_molecular_fingerprints_classification

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Molecular Fingerprints Are Strong Models for Peptide Function Prediction

https://github.com/AIRI-Institute/nablaDFT

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$\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

https://github.com/AIforGreatGood/charge3net

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Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials

https://github.com/AbhiroopBhattacharya/MatMMFuse

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MatMMFuse: Multi-Modal Fusion model for Material Property Prediction

https://github.com/AkiraTOSEI/SMOACS

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Rethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable Targets

https://github.com/BaratiLab/AugLiChem

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AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning

https://github.com/BiomedSciAI/biomed-multi-view

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Multi-view biomedical foundation models for molecule-target and property prediction

https://github.com/BrightBlueCheese/transformers_and_chemistry

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The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA

https://github.com/CRIPAC-DIG/Pin-Tuning

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Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction

https://github.com/CSUBioGroup/MolMetaLM

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MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language Model

https://github.com/ChakradharG/PeptideBERT

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PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction

https://github.com/ChangwenXu98/TransPolymer

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TransPolymer: a Transformer-based language model for polymer property predictions

https://github.com/Cortys/rankgnn

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Ranking Structured Objects with Graph Neural Networks

https://github.com/Dandelionym/llm_for_mechanisms

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Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?

https://github.com/Data-reindeer/MOCO

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Improving Molecular Pretraining with Complementary Featurizations

https://github.com/Deagogishvili/chapter-multi-task

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PatchProt: Hydrophobic patch prediction using protein foundation models

https://github.com/EricAlcaide/generalized-readout-phase

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Improving Graph Property Prediction with Generalized Readout Functions

https://github.com/FedML-AI/SpreadGNN

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SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks

https://github.com/GSK-AI/mocop

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Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation

https://github.com/Graph-COM/SPE

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On the Stability of Expressive Positional Encodings for Graphs

https://github.com/HaoZhongkai/AS_Molecule

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ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

https://github.com/Henrium/LESets

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Do Graph Neural Networks Work for High Entropy Alloys?

https://github.com/Hugh415/HimNet

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Learning Hierarchical Interaction for Accurate Molecular Property Prediction

https://github.com/Hyacinth-YX/mixture-of-collaborative-experts

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Enhancing Molecular Property Prediction via Mixture of Collaborative Experts

https://github.com/IntelLabs/EquiTriton

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Deconstructing equivariant representations in molecular systems

https://github.com/Jiaxin-Xu/PairwiseShapley

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From Abstract to Actionable: Pairwise Shapley Values for Explainable AI

https://github.com/JinA0218/drugood-densify

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Robust Molecular Property Prediction via Densifying Scarce Labeled Data

https://github.com/JohnnyY8/Domain-Adversarial-Multi-task-Framework

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Domain-Adversarial Multi-Task Framework for Novel Therapeutic Property Prediction of Compounds

https://github.com/KKbeckang/LiGPT-Beta

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Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

https://github.com/MMunibas/PhysNet_DER

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Uncertainty quantification for predictions of atomistic neural networks

https://github.com/MasterAI-EAM/MatEmbedding

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From Tokens to Materials: Leveraging Language Models for Scientific Discovery

https://github.com/MoleculeAI/TAGMol

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TAGMol: Target-Aware Gradient-guided Molecule Generation

https://github.com/MustBeOne/SA-GAT-SR

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SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction

https://github.com/NSAPH-Projects/topological-equivariant-networks

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E(n) Equivariant Topological Neural Networks

https://github.com/Nanolbw/SuGAr

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Subgraph Aggregation for Out-of-Distribution Generalization on Graphs

https://github.com/Open-Catalyst-Project/Open-Catalyst-Dataset

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The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

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