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Drug Design

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

https://github.com/siddhant-doshi/Dr-COVID

공식 코드

Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing

https://github.com/stemylonas/deepsurf_shrec22

공식 코드

SHREC 2022: Protein-ligand binding site recognition

https://github.com/tencent-ailab/grover

공식 코드

Self-Supervised Graph Transformer on Large-Scale Molecular Data

https://github.com/timduignan/caii-orb-d3-v2

공식 코드

Carbonic anhydrase II simulated with a universal neural network potential

https://github.com/tsa87/TacoGFN-SBDD

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

https://github.com/tsa87/cgflow

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Compositional Flows for 3D Molecule and Synthesis Pathway Co-design

https://github.com/tummfm/resolv

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Predicting solvation free energies with an implicit solvent machine learning potential

https://github.com/weilabmsu/svs

공식 코드

SVSBI: Sequence-based virtual screening of biomolecular interactions

https://github.com/wengong-jin/multiobj-rationale

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Multi-Objective Molecule Generation using Interpretable Substructures

https://github.com/wjm41/soapgp

공식 코드

Investigating 3D Atomic Environments for Enhanced QSAR

https://github.com/xian-sh/mol-mamba

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MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights

https://github.com/xunyoyo/YZS-Model

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YZS-model: A Predictive Model for Organic Drug Solubility Based on Graph Convolutional Networks and Transformer-Attention

https://github.com/yaledeus/ellidock

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Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction

https://github.com/yandrewl/clape

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Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning

https://github.com/yangling0818/binddm

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Binding-Adaptive Diffusion Models for Structure-Based Drug Design

https://github.com/yanliang3612/nucleusdiff

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Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design

https://github.com/yerevann/chemlactica

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Small Molecule Optimization with Large Language Models

https://github.com/zaixizhang/awesome-sbdd

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Geometric Deep Learning for Structure-Based Drug Design: A Survey

https://github.com/zaixizhang/flag

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Learning Subpocket Prototypes for Generalizable Structure-based Drug Design

https://github.com/zaixizhang/molcode

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An Equivariant Generative Framework for Molecular Graph-Structure Co-Design

https://github.com/zchwang/deeprmsd-vina_optimization

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A fully differentiable ligand pose optimization framework guided by deep learning and traditional scoring functions

https://github.com/zhaohanm/fusiondti

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FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction

https://github.com/zhouxiangxin1998/DualDiff

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Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design

https://github.com/zjunlp/MolGen

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Domain-Agnostic Molecular Generation with Chemical Feedback

https://github.com/zkysfls/2024-sbdd-benchmark

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Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?

https://github.com/zoom-wang112358/molleo

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Efficient Evolutionary Search Over Chemical Space with Large Language Models

https://huggingface.co/alimotahharynia/DrugGen

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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback

https://huggingface.co/spaces/alimotahharynia/GPT-2-Drug-Generator

공식 코드

DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/trn

Image-Conditioned Graph Generation for Road Network Extraction

https://github.com/AlgoMole/GeoBFN

MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space

https://github.com/AstraZeneca/chemicalx

CASTER: Predicting Drug Interactions with Chemical Substructure Representation

https://github.com/GilbertoQ/Bioinformatics

Protein-Ligand Scoring with Convolutional Neural Networks

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

Multi-Objective Molecule Generation using Interpretable Substructures

https://github.com/SeonghwanSeo/BBAR

Generative Flows on Synthetic Pathway for Drug Design

https://github.com/VirginiaSabando/MolecularEmbeddings

Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?

https://github.com/benevolentAI/guacamol_baselines

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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