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Drug Discovery

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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/hkmztrk/DeepDTA

공식 코드

DeepDTA: Deep Drug-Target Binding Affinity Prediction

https://github.com/hliulab/scadadrug

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Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learning

https://github.com/hliulab/xtransfercdr

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Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses

https://github.com/huaishengzhu/3mdiffusion

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3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation

https://github.com/huaxiuyao/ATS

공식 코드

Meta-learning with an Adaptive Task Scheduler

https://github.com/hubiodatalab/selformer

공식 코드

SELFormer: Molecular Representation Learning via SELFIES Language Models

https://github.com/hxyfighter/3dmolformer

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3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery

https://github.com/hxyfighter/llmsunderstandmol

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Empirical Evidence for the Fragment level Understanding on Drug Molecular Structure of LLMs

https://github.com/hyeonahkimm/genetic_gfn

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Genetic-guided GFlowNets for Sample Efficient Molecular Optimization

https://github.com/hysonlab/ligand_generation

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Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning

https://github.com/hyunp2/alphafold

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APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

https://github.com/iambic-therapeutics/np-bench

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NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models

https://github.com/ibm/afdistill

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AlphaFold Distillation for Protein Design

https://github.com/ibm/molformer

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Large-Scale Chemical Language Representations Capture Molecular Structure and Properties

https://github.com/ibm/otter-knowledge

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Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery

https://github.com/idea-iitd/gshot

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GSHOT: Few-shot Generative Modeling of Labeled Graphs

https://github.com/idea-iitd/induce

공식 코드

Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity

https://github.com/idea-xl/instructmol

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InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery

https://github.com/idiap/abroad-re

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Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach

https://github.com/idiap/gme-sampler

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Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach

https://github.com/idruglab/hignn

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HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention

https://github.com/igashov/difflinker

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Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design

https://github.com/ingenii-solutions/ingenii-quantum-hybrid-networks

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A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery

https://github.com/innvariant/deepgg

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DeepGG: a Deep Graph Generator

https://github.com/insilicomedicine/DILBERT

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Drug and Disease Interpretation Learning with Biomedical Entity Representation Transformer

https://github.com/insitro/kindel

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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors

https://github.com/insitro/sams-vae

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Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder

https://github.com/intelligence-csd-auth-gr/DTI_MF2A

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Optimizing Area Under the Curve Measures via Matrix Factorization for Predicting Drug-Target Interaction with Multiple Similarities

https://github.com/intelligent-molecular-systems/llm_finetuning_for_biochemistry

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Leveraging Large Language Models for enzymatic reaction prediction and characterization

https://github.com/isidroc/kekulescope

공식 코드

KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images

https://github.com/ivanniu/inversiongnn

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InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization

https://github.com/jacklin18/DeepGS

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DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction

https://github.com/jaechanglim/DTI_PDBbind

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PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions

https://github.com/jiaqingxie/deepprotein

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DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning

https://github.com/jiaxianyan/mbp

공식 코드

Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction

https://github.com/jinhojsk515/ldmol

공식 코드

LDMol: Text-to-Molecule Diffusion Model with Structurally Informative Latent Space

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