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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/wzn99/drugpilot

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DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery

https://github.com/xarakas/spheroid-tnf-v2-emews

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Parallel Model Exploration for Tumor Treatment Simulations

https://github.com/xiaodanxing/sam4organoid

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SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis

https://github.com/xiaomingaaa/biokdn

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Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction

https://github.com/xiaoqiongxia/transcdr

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TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representation

https://github.com/xuefeng-cs/erp

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Entropy-Reinforced Planning with Large Language Models for Drug Discovery

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/xzenglab/resources-for-ddis-prediction-using-dl

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Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

https://github.com/yali-f/gladmamba

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GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model

https://github.com/yasir-ghunaim/kano-chebi

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Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction

https://github.com/yazdanimehdi/fragxsitedti

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FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation

https://github.com/yifeiwang15/acml

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Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning

https://github.com/yigang-chen/scope-dti

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SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction

https://github.com/yijia-xiao/pgraphdta

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PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps

https://github.com/yikunpku/atomas

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Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation

https://github.com/ying531/jomi-paper

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Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage

https://github.com/ying531/selcf_paper

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Selection by Prediction with Conformal p-values

https://github.com/yinghao-li/muben

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MUBen: Benchmarking the Uncertainty of Molecular Representation Models

https://github.com/yinhanhe123/new_LLM4GNNExplanation

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Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction

https://github.com/yivan-wyygdsg/agmi

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AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks

https://github.com/yoshihidesawada/CompGAN

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Study of Deep Generative Models for Inorganic Chemical Compositions

https://github.com/youngseungjeon/ragppi

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RAGPPI: RAG Benchmark for Protein-Protein Interactions in Drug Discovery

https://github.com/youqingxiaozhua/deepternary

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SE(3)-Equivariant Ternary Complex Prediction Towards Target Protein Degradation

https://github.com/yu-group/imodels-experiments

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Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability

https://github.com/yueming-yin/gafse

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An open unified deep graph learning framework for discovering drug leads

https://github.com/yuewan2/molmcl

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Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation

https://github.com/yulun-rayn/SGAnCP4ADD

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

https://github.com/yulun-rayn/dgapn

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

https://github.com/yulun-rayn/graphvci

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Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information

https://github.com/yunhuijang/hggt

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Graph Generation with $K^2$-trees

https://github.com/yutxie/mars

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MARS: Markov Molecular Sampling for Multi-objective Drug Discovery

https://github.com/yuyangw/MolCLR

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Molecular Contrastive Learning of Representations via Graph Neural Networks

https://github.com/zabaras/GSVAE

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Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder

https://github.com/zaixizhang/FoldMark

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FoldMark: Protecting Protein Generative Models with Watermarking

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

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