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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/deargen/cmg

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Controlled Molecule Generator for Optimizing Multiple Chemical Properties

https://github.com/deep-symbolic-mathematics/Multimodal-Math-Pretraining

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SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training

https://github.com/deep-symbolic-mathematics/Multimodal-Symbolic-Regression

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SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training

https://github.com/deepgraphlearning/gearnet

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Protein Representation Learning by Geometric Structure Pretraining

https://github.com/deepgraphlearning/torchdrug

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TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery

https://github.com/deepmodeling/crystalformer

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CrystalFormer-RL: Reinforcement Fine-Tuning for Materials Design

https://github.com/dengjianyuan/Survey_AI_Drug_Discovery

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Artificial Intelligence in Drug Discovery: Applications and Techniques

https://github.com/dengjianyuan/respite_mpp

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Unraveling Key Elements Underlying Molecular Property Prediction: A Systematic Study

https://github.com/dhw059/densegnn

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DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

https://github.com/divelab/AIRS

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Complete and Efficient Graph Transformers for Crystal Material Property Prediction

https://github.com/divelab/AIRS/tree/main/OpenMat/PotNet

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Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction

https://github.com/divelab/MoleculeProp

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

https://github.com/divelab/MoleculeX

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

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

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Identifying Semantic Component for Robust Molecular Property Prediction

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

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

https://github.com/dmitrykazhdan/gci

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

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

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Graph Self-supervised Learning with Accurate Discrepancy Learning

https://github.com/dptech-corp/Uni-Mol

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Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+

https://github.com/dqwang122/rlhex

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

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

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

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

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Motif-aware Attribute Masking for Molecular Graph Pre-training

https://github.com/ellenzzn/chatmol

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Interactive Molecular Discovery with Natural Language

https://github.com/fabiankruger/molprivacy

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Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy

https://github.com/facebookresearch/JMP

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From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

https://github.com/fanmengwang/mmpolymer

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MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction

https://github.com/fanyun-sun/InfoGraph

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InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

https://github.com/fengshikun/unimap

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UniMAP: Universal SMILES-Graph Representation Learning

https://github.com/fpour/tgb_baselines

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Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/g-benton/learning-invariances

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Learning Invariances in Neural Networks

https://github.com/gaingod-xu/acmlproject

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Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning

https://github.com/ganeshjawahar/fine-tweet

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Interpreting the Syntactic and Social Elements of the Tweet Representations via Elementary Property Prediction Tasks

https://github.com/genentech/molcap-arena

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MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property Prediction

https://github.com/geommoldesign/hmr

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Learning Harmonic Molecular Representations on Riemannian Manifold

https://github.com/georgelamb19/chempropBayes

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Bayesian Graph Neural Networks for Molecular Property Prediction

https://github.com/gmh14/geo-deg

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Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

https://github.com/gmp007/propertyextractor

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Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property Prediction

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