Papers with Code 아카이브

Drug Discovery

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/prescient-design/StrainRelief

공식 코드

Strain Problems got you in a Twist? Try StrainRelief: A Quantum-Accurate Tool for Ligand Strain Calculations

https://github.com/prescient-design/lobster

공식 코드

Concept Bottleneck Language Models For protein design

https://github.com/prescient-design/nebula

공식 코드

NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries

https://github.com/pricexu/disco

공식 코드

Discrete-state Continuous-time Diffusion for Graph Generation

https://github.com/pz-white/drugban

공식 코드

Interpretable bilinear attention network with domain adaptation improves drug-target prediction

https://github.com/qiangbo1222/hierdiff

공식 코드

Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D

https://github.com/qizhipei/fabind

공식 코드

FABind: Fast and Accurate Protein-Ligand Binding

https://github.com/qizhipei/smt-dta

공식 코드

SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction

https://github.com/qizhipei/ssm-dta

공식 코드

SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction

https://github.com/qsong-github/scdrugmap

공식 코드

scDrugMap: Benchmarking Large Foundation Models for Drug Response Prediction

https://github.com/r08b46009/code_for_migu_nana

공식 코드

NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks

https://github.com/radh55sh/BAPULM

공식 코드

BAPULM: Binding Affinity Prediction using Language Models

https://github.com/rathapech/DTI_LMP

공식 코드

A generalized method toward drug-target interaction prediction via low-rank matrix projection

https://github.com/recursionpharma/maes_microscopy

공식 코드

Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

https://github.com/reginaib/ac-dti

공식 코드

Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks

https://github.com/ricardo-romero-ochoa/bio-deep-learning

공식 코드

Integration of Genetic Algorithms and Deep Learning for the Generation and Bioactivity Prediction of Novel Tyrosine Kinase Inhibitors

https://github.com/rifqat/Comparative-Analysis

공식 코드

A Comparative Analysis of the Ensemble Methods for Drug Design

https://github.com/rivas-lab/Smiles2Dock

공식 코드

Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking

https://github.com/rizaozcelik/pydebiaseddta

공식 코드

DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models

https://github.com/rl-arch/lensgan

공식 코드

Physics-informed generative real-time lens-free imaging

https://github.com/rlacombe/accgeoldm

공식 코드

Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models

https://github.com/roestlab/massformer

공식 코드

MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers

https://github.com/rose-stl-lab/limo

공식 코드

LIMO: Latent Inceptionism for Targeted Molecule Generation

https://github.com/rose-stl-lab/mf-lal

공식 코드

MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning

https://github.com/rosettacommons/rosetta

공식 코드

REvoLd: Ultra-Large Library Screening with an Evolutionary Algorithm in Rosetta

https://github.com/rpirie96/rgmolsa

공식 코드

Riemannian Geometry and Molecular Surfaces I: Spectrum of the Laplacian

https://github.com/rsinghlab/K-Paths

공식 코드

K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction

https://github.com/ryienh/graph-dock

공식 코드

Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks

https://github.com/samgoldman97/enz-pred

공식 코드

Machine learning modeling of family wide enzyme-substrate specificity screens

https://github.com/sandbox-quantum/idolpro-scoring

공식 코드

Guided Multi-objective Generative AI to Enhance Structure-based Drug Design

https://github.com/sarfrazahmad307/ibitter-stack

공식 코드

iBitter-Stack: A Multi-Representation Ensemble Learning Model for Accurate Bitter Peptide Identification

https://github.com/schwallergroup/augmented_memory

공식 코드

Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation

https://github.com/schwallergroup/saturn

공식 코드

Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation

https://github.com/sci-assess/sciassess

공식 코드

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis

https://github.com/scir-hi/moltailor

공식 코드

MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts

https://github.com/scu-sen/recursion-cellular-image-classification-challenge

공식 코드

DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep Learning

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