Papers with Code 아카이브

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/mllab4cs/deepglstm

공식 코드

Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity

https://github.com/molecularmodelinglab/bigbind

공식 코드

An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models

https://github.com/molecularsets/moses

공식 코드

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

https://github.com/molex2024/molex

공식 코드

Unveiling Molecular Secrets: An LLM-Augmented Linear Model for Explainable and Calibratable Molecular Property Prediction

https://github.com/morgancthomas/molscore

공식 코드

ACEGEN: Reinforcement learning of generative chemical agents for drug discovery

https://github.com/mproszewska/B-XAIC

공식 코드

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data

https://github.com/mrzzmrzz/scop

공식 코드

SCOP: A Sequence-Structure Contrast-Aware Framework for Protein Function Prediction

https://github.com/mtcrawshaw/meta

공식 코드

SLAW: Scaled Loss Approximate Weighting for Efficient Multi-Task Learning

https://github.com/namkyeong/amole

공식 코드

Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule Language Models

https://github.com/naruto7283/gx2mol

공식 코드

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning

https://github.com/networkgeometry/hyperbolic-benchmark-gnn

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Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance

https://github.com/ngminhtri0394/c2p2

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Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferring

https://github.com/nguyenlabuky/ggl-mutation

공식 코드

GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding Free Energy Changes

https://github.com/ninglab/Modof

공식 코드

A Deep Generative Model for Molecule Optimization via One Fragment Modification

https://github.com/ninglab/TransferAct

공식 코드

Improving Compound Activity Classification via Deep Transfer and Representation Learning

https://github.com/ninglab/drugranker

공식 코드

Precision Anti-Cancer Drug Selection via Neural Ranking

https://github.com/njchoma/DGAPN

공식 코드

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

https://github.com/nvlabs/retmol

공식 코드

Retrieval-based Controllable Molecule Generation

https://github.com/nyushcs/moleculargpt

공식 코드

MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

https://github.com/ohuelab/fastlomap

공식 코드

Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free Energy Perturbation

https://github.com/ohuelab/npgpt

공식 코드

NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language Models

https://github.com/openforcefield/FE-Benchmarks-Best-Practices

공식 코드

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks

https://github.com/openforcefield/protein-ligand-benchmark

공식 코드

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks

https://github.com/openmm/openmmforcefields

공식 코드

End-to-End Differentiable Molecular Mechanics Force Field Construction

https://github.com/orgw/TurboHopp

공식 코드

TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models

https://github.com/osu-nlp-group/llm4chem

공식 코드

LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset

https://github.com/otouat/gnnevaluationmetrics

공식 코드

GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators

https://github.com/paperswithcode/galai

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Galactica: A Large Language Model for Science

https://github.com/paularindam/CheMixNet

공식 코드

CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations

https://github.com/peizhenbai/drugban

공식 코드

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

https://github.com/pengsl-lab/mssl

공식 코드

Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery

https://github.com/pengsl-lab/mssl2drug

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Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery

https://github.com/phenixace/molregpt

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Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective

https://github.com/plainerman/latent-tps

공식 코드

Transition Path Sampling with Boltzmann Generator-based MCMC Moves

https://github.com/pnnl/FragNet

공식 코드

FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability

https://github.com/pnnl/darkchem

공식 코드

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

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