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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/code-implementation1/Code9/tree/main/pfnn

Development and evaluation of a deep learning model for protein-ligand binding affinity prediction

https://github.com/cool21th/ai_drug_discovery

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

https://github.com/cts198859/deeprl_dist

Neural Message Passing for Quantum Chemistry

https://github.com/cts198859/deeprl_network

Neural Message Passing for Quantum Chemistry

https://github.com/cybermonic/cage-4-submission

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/cyclone923/ChemBo

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

https://github.com/cyclone923/jtvae

Junction Tree Variational Autoencoder for Molecular Graph Generation

https://github.com/darnbi/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/debbiemarkslab/neural-fingerprint-theano

Convolutional Networks on Graphs for Learning Molecular Fingerprints

https://github.com/deepchem/deepchem

Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity

https://github.com/deepchem/moleculenet

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/deepmodeling/Uni-Mol

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

https://github.com/diogofbraga/OrphanPrincipalComponentAnalysis

Corresponding Projections for Orphan Screening

https://github.com/divelab/good

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

https://github.com/dmbernaal/Daedalus

Self-Normalizing Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/dimenet

Directional Message Passing for Molecular Graphs

https://github.com/dmlc/dgl/tree/master/examples/pytorch/ggnn

Gated Graph Sequence Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/jtnn

Junction Tree Variational Autoencoder for Molecular Graph Generation

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/domschl/syncognite

Self-Normalizing Neural Networks

https://github.com/dongchen06/macacc

Neural Message Passing for Quantum Chemistry

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

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

https://github.com/edapinenut/cbgbench

Generating 3D Molecules for Target Protein Binding

https://github.com/emartinezs44/SparkGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/entslscheia/GGNN_Reasoning

Gated Graph Sequence Neural Networks

https://github.com/fanzhenliu/dagad

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/fau-is/grm

Gated Graph Sequence Neural Networks

https://github.com/fransou/is-meta-learning-necessary

Strategies for Pre-training Graph Neural Networks

https://github.com/fredjo89/heterogeneous-mpnn

Neural Message Passing for Quantum Chemistry

https://github.com/fvilmos/multi_task_learning_shape_color_position

An Overview of Multi-Task Learning in Deep Neural Networks

https://github.com/gajeelSF/Reinforcement-Learning-Branch-and-Bound

Neural Message Passing for Quantum Chemistry

https://github.com/genedisco/genedisco-starter

GeneDisco: A Benchmark for Experimental Design in Drug Discovery

https://github.com/gerkone/painn-jax

Equivariant message passing for the prediction of tensorial properties and molecular spectra

https://github.com/gmum/huggingmolecules

Relative Molecule Self-Attention Transformer

https://github.com/gmum/umwpl2021

Molecule Attention Transformer

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