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Drug Design

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코드

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

https://github.com/bowen-gao/ProFSA

ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment

https://github.com/cataai/posex

Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction

https://github.com/cch1999/posecheck

Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?

https://github.com/celinehocquette/numsynth-aaai23

Learning programs with magic values

https://github.com/chemprop/chemprop

Multi-Objective Molecule Generation using Interpretable Substructures

https://github.com/chenruduan/oareactdiff

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

https://github.com/cool21th/ai_drug_discovery

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

https://github.com/deepchem/deepchem

Self-Supervised Graph Transformer on Large-Scale Molecular Data

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

Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction

https://github.com/dengjianyuan/respite_mpp

Self-Supervised Graph Transformer on Large-Scale Molecular Data

https://github.com/hits-mbm-dev/grappa

Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

https://github.com/hughnew/gapdiff

3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction

https://github.com/hyeonahkimm/genetic_gfn

De novo Drug Design using Reinforcement Learning with Multiple GPT Agents

https://github.com/insilicomedicine/fcd_torch

Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery

https://github.com/iwasakishuto/DeepScreening

DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins

https://github.com/jaechanglim/GFML

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

https://github.com/jaechanglim/molecule-generator

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

https://github.com/lamda-bbo/offline-moo

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

https://github.com/ml-jku/mhn-react

Hopfield Networks is All You Need

https://github.com/openmm/openmmforcefields

Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

https://github.com/pascalnotin/uncertainty_guided_optimization

Improving black-box optimization in VAE latent space using decoder uncertainty

https://github.com/ppsp-team/pyhkbs

Hopfield Networks is All You Need

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

Multi-Objective Molecule Generation using Interpretable Substructures

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

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

https://github.com/seonghwanseo/pharmaconet

TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design

https://github.com/swansonk14/chemprop

Multi-Objective Molecule Generation using Interpretable Substructures

https://github.com/tadorfer/NLProt

Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

https://github.com/victor369basu/ProteinStructurePrediction

Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

https://github.com/yinanhuang/3dlinker

3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design

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