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Drug Discovery

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하위 태스크: 3
평가표: 28
보존된 평가 기록: 96

Drug discovery is the task of applying machine learning to discover new candidate drugs.

<span style="color:grey; opacity: 0.6">( Image credit: A Turing Test for Molecular Generators )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/BioImageInformatics/tfmodels

Self-Normalizing Neural Networks

https://github.com/BioSystemsUM/DeepMol

https://github.com/BrightBlueCheese/Modeling-of-Time-varying-Wireless-Communication-Channel-with-Fading-and-Shadowing

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

https://github.com/CAVED123/CheMixNet

CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations

https://github.com/CAVED123/DeepPurpose

DeepDTA: Deep Drug-Target Binding Affinity Prediction

https://github.com/CoderPat/OpenGNN

Neural Message Passing for Quantum Chemistry

https://github.com/GilbertoQ/Bioinformatics

AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery

https://github.com/HamzaGbada/GCN-Numpy

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/HazyResearch/metal

An Overview of Multi-Task Learning in Deep Neural Networks

https://github.com/HoganZhang/pygcn_python3

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/IllinoisGraphBenchmark/IGB-Datasets

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/IlyaTyagin/AGATHA-C-GP

AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approach

https://github.com/JakeColtman/bartpy

BART: Bayesian additive regression trees

https://github.com/JamesChuanggg/ggnn.pytorch

Gated Graph Sequence Neural Networks

https://github.com/Jh-SYSU/MolRep

Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction

https://github.com/JosephKijewski/DrugDesign

Junction Tree Variational Autoencoder for Molecular Graph Generation

https://github.com/KimMeen/GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/LRacoci/permutation-graphml

Neural Message Passing for Quantum Chemistry

https://github.com/LiamWilbraham/jtnnencoder

Junction Tree Variational Autoencoder for Molecular Graph Generation

https://github.com/Lin-Group-at-UMass/FBGNN-MBE

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

https://github.com/LouisDumont/GCN---re-implementation

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/ML-GSAI/EGSDE

Equivariant Energy-Guided SDE for Inverse Molecular Design

https://github.com/MUmarJaved/MultiAgent-Distributed-Reinforcement-Learning

Neural Message Passing for Quantum Chemistry

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Self_Normalizing_Neural_Networks

Self-Normalizing Neural Networks

https://github.com/Microsoft/gated-graph-neural-network-samples

Gated Graph Sequence Neural Networks

https://github.com/Microsoft/graph-partition-neural-network-samples

Gated Graph Sequence Neural Networks

https://github.com/Mind23-2/MindCode-88/tree/main/snn_mlp

Self-Normalizing Neural Networks

https://github.com/MindSpore-paper-code-3/code6/tree/main/ats

Meta-learning with an Adaptive Task Scheduler

https://github.com/PaddlePaddle/PaddleScience

MoFlow: An Invertible Flow Model for Generating Molecular Graphs

https://github.com/Samyu0304/thought-propagation

Neural Message Passing for Quantum Chemistry

https://github.com/Sarikaya-Lab-GEMSEC/Peptide_Graph_Autograd

Convolutional Networks on Graphs for Learning Molecular Fingerprints

https://github.com/Saro00/DGN

Neural Message Passing for Quantum Chemistry

https://github.com/SarthakTiwari/Rationales-extaction-from-active-molecules

Multi-Objective Molecule Generation using Interpretable Substructures

https://github.com/SejeongPark8354/MforDrugDev

A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

https://github.com/SeonghwanSeo/BBAR

Generative Flows on Synthetic Pathway for Drug Design

https://github.com/SystemicCypher/Neural-Molecule-Fingerprints

Convolutional Networks on Graphs for Learning Molecular Fingerprints

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