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/lhirschfeld/ChempropUncertaintyQuantification

공식 코드

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

https://github.com/lian-xiao/biobridge

공식 코드

Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction

https://github.com/likunnb/TransEDRP

공식 코드

Towards a Better Model with Dual Transformer for Drug Response Prediction

https://github.com/lipi12q/drugman

공식 코드

Drug-target interaction prediction by integrating heterogeneous information with mutual attention network

https://github.com/lirongwu/psc-cpi

공식 코드

PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction

https://github.com/liugangcode/MCD

공식 코드

Graph Diffusion Transformers for Multi-Conditional Molecular Generation

https://github.com/ljatynu/imchgan

공식 코드

Heterogeneous networks in drug-target interaction prediction

https://github.com/luoyunan/DTINet

공식 코드

Heterogeneous networks in drug-target interaction prediction

https://github.com/ma921/adabatal

공식 코드

Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach

https://github.com/ma921/sober

공식 코드

SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces

https://github.com/macrohongz/hampdti

공식 코드

Heterogeneous networks in drug-target interaction prediction

https://github.com/mahsasheikh/DrugGen

공식 코드

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

https://github.com/maplightrx/maplight-tdc

공식 코드

ADMET property prediction through combinations of molecular fingerprints

https://github.com/margotyjx/fmint

공식 코드

FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model

https://github.com/mattragoza/liGAN

공식 코드

Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models

https://github.com/maxischuh/BarlowDTI

공식 코드

Barlow Twins Deep Neural Network for Advanced 1D Drug-Target Interaction Prediction

https://github.com/maxischuh/twinbooster

공식 코드

TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction

https://github.com/mc-robinson/validating_validation_supp_info

공식 코드

Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction

https://github.com/medicinebiology-ai/eeg-dti

공식 코드

Heterogeneous networks in drug-target interaction prediction

https://github.com/mengcongwo/fl_privacy_blockcrossing

공식 코드

Differentially Private Federated Learning via Reconfigurable Intelligent Surface

https://github.com/mhlee216/mgcvae

공식 코드

MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencoder

https://github.com/microljy/graph_level_drug_discovery

공식 코드

Learning Graph-Level Representation for Drug Discovery

https://github.com/microsoft/molecule-generation

공식 코드

Learning to Extend Molecular Scaffolds with Structural Motifs

https://github.com/mims-harvard/TDC

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Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

https://github.com/minkaixu/geodiff

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GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

https://github.com/minys233/dynafomer

공식 코드

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning

https://github.com/mirunacrt/synflownet

공식 코드

SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints

https://github.com/ml-jku/chef

공식 코드

Cross-Domain Few-Shot Learning by Representation Fusion

https://github.com/ml-jku/chem-xlstm

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Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences

https://github.com/ml-jku/clamp

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Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language

https://github.com/ml-jku/convex-init

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Principled Weight Initialisation for Input-Convex Neural Networks

https://github.com/ml-jku/dna-xlstm

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Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences

https://github.com/ml-jku/gnn-vpa

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GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks

https://github.com/ml-jku/mhnfs

공식 코드

Context-enriched molecule representations improve few-shot drug discovery

https://github.com/ml-jku/prot-xlstm

공식 코드

Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences

https://github.com/ml-jku/sars-cov-inhibitors-chemai

공식 코드

Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks

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