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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/thunlp-mt/get

공식 코드

Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning

https://github.com/thunlp-mt/ps-vae

공식 코드

Molecule Generation by Principal Subgraph Mining and Assembling

https://github.com/tian-bai/optcs

공식 코드

Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization

https://github.com/tkipf/pygcn

공식 코드

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/tmlr-group/fabflex

공식 코드

Fast and Accurate Blind Flexible Docking

https://github.com/tmlr-group/neuralatom

공식 코드

Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel

https://github.com/tomaidd/nafm-official

공식 코드

NaFM: Pre-training a Foundation Model for Small-Molecule Natural Products

https://github.com/tomte812/chef

공식 코드

Cross-Domain Few-Shot Learning by Representation Fusion

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

공식 코드

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

https://github.com/tsa87/semi-jtvae

공식 코드

Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction

https://github.com/tum-daml/magnet

공식 코드

MAGNet: Motif-Agnostic Generation of Molecules from Shapes

https://github.com/ur-whitelab/chemcrow-public

공식 코드

ChemCrow: Augmenting large-language models with chemistry tools

https://github.com/ur-whitelab/chemcrow-runs

공식 코드

ChemCrow: Augmenting large-language models with chemistry tools

https://github.com/veali/mlt-le

공식 코드

MLT-LE: predicting drug-target binding affinity with multi-task residual neural networks

https://github.com/vencent-won/sggrl

공식 코드

Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry

https://github.com/vertaix/quality-weighted-vendi-score

공식 코드

Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design

https://github.com/vincent-1125/uncertainty-quantification-on-clinical-trial-outcome-prediction

공식 코드

Uncertainty Quantification on Clinical Trial Outcome Prediction

https://github.com/vishakhpk/iter-extrapolation

공식 코드

Extrapolative Controlled Sequence Generation via Iterative Refinement

https://github.com/volkamerlab/transformers4mpp_review

공식 코드

Transformers for molecular property prediction: Lessons learned from the past five years

https://github.com/vshy-dream/drexplainer

공식 코드

DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network

https://github.com/wangyu-sd/MechRetro

공식 코드

MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning

https://github.com/warmongeringbeaver/phendiff

공식 코드

PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images

https://github.com/weilabmsu/antoencoder-v01

공식 코드

Machine learning analysis of cocaine addiction informed by DAT, SERT, and NET-based interactome networks

https://github.com/weilabmsu/herg-prediction

공식 코드

Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models

https://github.com/weilabmsu/opioid-repurposing

공식 코드

Machine-learning Repurposing of DrugBank Compounds for Opioid Use Disorder

https://github.com/weilabmsu/svs

공식 코드

SVSBI: Sequence-based virtual screening of biomolecular interactions

https://github.com/weixiaoqimath/persistent_sheaf_Laplacians

공식 코드

Persistent Sheaf Laplacian Analysis of Protein Flexibility

https://github.com/wengong-jin/dsmbind

공식 코드

Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation

https://github.com/wengong-jin/icml18-jtnn

공식 코드

Junction Tree Variational Autoencoder for Molecular Graph Generation

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

공식 코드

Multi-Objective Molecule Generation using Interpretable Substructures

https://github.com/wenhao-gao/SynNet

공식 코드

Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design

https://github.com/wenhao-gao/askcos_synthesizability

공식 코드

The Synthesizability of Molecules Proposed by Generative Models

https://github.com/wenhao-gao/synformer

공식 코드

Generative Artificial Intelligence for Navigating Synthesizable Chemical Space

https://github.com/whitead/synspace

공식 코드

ChemCrow: Augmenting large-language models with chemistry tools

https://github.com/wyx2012/bg-dti

공식 코드

Heterogeneous networks in drug-target interaction prediction

https://github.com/wz-create/batmannet

공식 코드

BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular Representation

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