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Drug Discovery

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전체 기법 전체 데이터셋

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/machinelearninglifescience/poli

GuacaMol: Benchmarking Models for De Novo Molecular Design

https://github.com/mahendrathapa/graph-convolution-network

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/mahsa91/GKD

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/mahsa91/RA-GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/mc-robinson/mayr_reanalysis_supp_info

Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction

https://github.com/meliketoy/graph-cnn.pytorch

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/messi-q/gnnscvuldetector

Gated Graph Sequence Neural Networks

https://github.com/microsoft/fs-mol

Strategies for Pre-training Graph Neural Networks

https://github.com/microsoft/syntheseus

It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design

https://github.com/ml-jku/vnegnn

EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction

https://github.com/mostafachatillon/ChemGAN-challenge

ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?

https://github.com/mshunshin/SegNetCMR

Self-Normalizing Neural Networks

https://github.com/negarhdr/PGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/nieci2024/pyglcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/njchoma/exaLearnMol

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

https://github.com/nnaakkaaii/g2-MLP

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/nrel/m2p

Convolutional Networks on Graphs for Learning Molecular Fingerprints

https://github.com/nu-cucis/chemixnet

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

https://github.com/omrijsharon/NetworkModule

Self-Normalizing Neural Networks

https://github.com/onakanob/Peptide_Graph_Autograd

Convolutional Networks on Graphs for Learning Molecular Fingerprints

https://github.com/pascalnotin/uncertainty_guided_optimization

Junction Tree Variational Autoencoder for Molecular Graph Generation

https://github.com/peterbjorgensen/msgnet

Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials

https://github.com/pgniewko/solubility

Convolutional Networks on Graphs for Learning Molecular Fingerprints

https://github.com/pharmolix/openbiomed

MolFM: A Multimodal Molecular Foundation Model

https://github.com/priba/nmp_qc

Neural Message Passing for Quantum Chemistry

https://github.com/priba/siamese_ged

Neural Message Passing for Quantum Chemistry

https://github.com/pwc-1/Paper-9/tree/main/7/Profiling-Pareto-Front

Multi-Objective Molecule Generation using Interpretable Substructures

https://github.com/qyuan7/molecular_gcn

Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network

https://github.com/samgoldman97/ms-pred

MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers

https://github.com/sanjaradylov/moleculegen-ml

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

https://github.com/selmiss/gp-tlstgcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/seonghann/tsdiff

GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

https://github.com/shamim-hussain/egt_pytorch

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

https://github.com/shamim-hussain/egt_triangular

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

https://github.com/shenweichen/DeepCTR

An Overview of Multi-Task Learning in Deep Neural Networks

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