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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/ml-jku/clamp

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Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language

https://github.com/ml-research/cna_modules

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Graph Neural Networks Need Cluster-Normalize-Activate Modules

https://github.com/mlvlab/llamo

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LLaMo: Large Language Model-based Molecular Graph Assistant

https://github.com/molex2024/molex

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Unveiling Molecular Secrets: An LLM-Augmented Linear Model for Explainable and Calibratable Molecular Property Prediction

https://github.com/mvseiler/deep_ela

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Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems

https://github.com/mzweilin/EvadeML-Zoo

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MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

https://github.com/navid-naderi/WEGL

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Wasserstein Embedding for Graph Learning

https://github.com/nyu-dl/conditional-molecular-design-ssvae

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Conditional molecular design with deep generative models

https://github.com/nyushcs/moleculargpt

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MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

https://github.com/oleneyl/geometry-aware-transformer

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GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning

https://github.com/particlegrid/particlegrid

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ParticleGrid: Enabling Deep Learning using 3D Representation of Materials

https://github.com/peterbjorgensen/msgnet

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Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors

https://github.com/peterbjorgensen/vorosym

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Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors

https://github.com/petergroth/kermut

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Kermut: Composite kernel regression for protein variant effects

https://github.com/pfnet-research/bayesgrad

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BayesGrad: Explaining Predictions of Graph Convolutional Networks

https://github.com/pharmolix/openbiomed

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Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge

https://github.com/phil-fradkin/contrastive_rna

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Splicing Up Your Predictions with RNA Contrastive Learning

https://github.com/pnnl/FragNet

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FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability

https://github.com/pnnl/cactus

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CACTUS: Chemistry Agent Connecting Tool-Usage to Science

https://github.com/pnnl/doa

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Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models

https://github.com/proteingpt/proteingpt

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ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding

https://github.com/ptarau/stanzagraphs

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A Gaze into the Internal Logic of Graph Neural Networks, with Logic

https://github.com/qitianwu/sgformer

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SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

https://github.com/qizhipei/3d-molt5

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3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling

https://github.com/radoslav11/sp-mpnn

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Shortest Path Networks for Graph Property Prediction

https://github.com/rampasek/HGNet

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Hierarchical graph neural nets can capture long-range interactions

https://github.com/ramprasad-group/canonicalize_psmiles

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polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics

https://github.com/ramprasad-group/polybert

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polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics

https://github.com/rangan2510/MultiPathGCN

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Multipath Graph Convolutional Neural Networks

https://github.com/rose-stl-lab/limo

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LIMO: Latent Inceptionism for Targeted Molecule Generation

https://github.com/ryanchen227/lemon

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Molecular Graph Contrastive Learning with Line Graph

https://github.com/sadmanomee/ood_materials_benchmark

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Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study

https://github.com/samsungsailmontreal/anymolgencritic

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Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees

https://github.com/schwallergroup/mhnn

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Molecular Hypergraph Neural Networks

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

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Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python

https://github.com/sds-lab/sliceformer

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Sliceformer: Make Multi-head Attention as Simple as Sorting in Discriminative Tasks

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