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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/3biocompbio/bcellepitope

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Critical review of conformational B-cell epitope prediction methods

https://github.com/99andBeyond/Apollo1060

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Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

https://github.com/Augus1999/bayesian-flow-network-for-chemistry

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Bayesian Flow Is All You Need to Sample Out-of-Distribution Chemical Spaces

https://github.com/Bilkent-CYBORG/VOGP

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Vector Optimization with Gaussian Process Bandits

https://github.com/CristoferQ/PeptideDatabase

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Peptipedia: a comprehensive database for peptide research supported by Assembled predictive models and Data Mining approaches

https://github.com/DeepLearnXMU/CMTMR

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Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment

https://github.com/ExpectozJJ/Persistent-Ollivier-Ricci-Curvature

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Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug design

https://github.com/GraphPKU/GeoRCG

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Geometric Representation Condition Improves Equivariant Molecule Generation

https://github.com/HBioquant/DiffBindFR

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DiffBindFR: An SE(3) Equivariant Network for Flexible Protein-Ligand Docking

https://github.com/HITS-MCM/MD-IFP

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A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories

https://github.com/Hanjun-Dai/graphnn

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Discriminative Embeddings of Latent Variable Models for Structured Data

https://github.com/HeathArhturLouis/Rethinking-Molecular-Design-Integrating-Latent-Variable-and-Autoregressive-Models-for-Enhanced-Goal

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Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation

https://github.com/MachineLearningLifeScience/hdbo_benchmark

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A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences

https://github.com/MoleculeAI/TAGMol

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TAGMol: Target-Aware Gradient-guided Molecule Generation

https://github.com/PKUterran/GeometryEncoding

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Equivalent Distance Geometry Error for Molecular Conformation Comparison

https://github.com/SeonghwanSeo/RxnFlow

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Generative Flows on Synthetic Pathway for Drug Design

https://github.com/YanjunLi-CS/DeepAtom_SupplementaryMaterials

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DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction

https://github.com/ZJU-Fangyin/KCL

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Molecular Contrastive Learning with Chemical Element Knowledge Graph

https://github.com/acellera/acegen-open

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ACEGEN: Reinforcement learning of generative chemical agents for drug discovery

https://github.com/adibayaseen/HKRCPI

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Insights into performance evaluation of com-pound-protein interaction prediction methods

https://github.com/algomole/molcraft

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MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space

https://github.com/alifbinabdulqayyum/pg-gmd

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Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy

https://github.com/alimotahharynia/GPT-2-Drug-Generator

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

https://github.com/amazon-science/LC-PLM

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Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers

https://github.com/amerorchis/Hybrid-Quantum-GAN

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Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation

https://github.com/amine179/curiosity-rl-for-drug-design

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Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug Design

https://github.com/anirudhgha/AMH

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An Efficient MCMC Approach to Energy Function Optimization in Protein Structure Prediction

https://github.com/arneschneuing/diffsbdd

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Structure-based Drug Design with Equivariant Diffusion Models

https://github.com/aryandeshwal/ladder

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Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

https://github.com/ask-berkeley/badger-sbdd

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General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design

https://github.com/aspuru-guzik-group/assessing_mol_prediction_confidence

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Assigning Confidence to Molecular Property Prediction

https://github.com/badripatro/mamba360

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Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges

https://github.com/balytskyijaroslaw/rapid-net

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Accurate Pocket Identification for Binding-Site-Agnostic Docking

https://github.com/beam-labs/crossbind

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CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues

https://github.com/bioinf-jku/FCD

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

https://github.com/borgwardtlab/pst

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Endowing Protein Language Models with Structural Knowledge

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