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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/frazier-lab/partial_kgfn

공식 코드

Fast Bayesian Optimization of Function Networks with Partial Evaluations

https://github.com/fudong03/CropNet

공식 코드

An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions

https://github.com/futianfan/mimosa

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MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization

https://github.com/futianfan/reinforced-genetic-algorithm

공식 코드

Reinforced Genetic Algorithm for Structure-based Drug Design

https://github.com/gaingod-xu/acmlproject

공식 코드

Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning

https://github.com/garimachib01/GPCR_AlphaFold

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Characterizing the Conformational States of G Protein Coupled Receptors Generated with AlphaFold

https://github.com/genedisco/genedisco

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GeneDisco: A Benchmark for Experimental Design in Drug Discovery

https://github.com/geommoldesign/hmr

공식 코드

Learning Harmonic Molecular Representations on Riemannian Manifold

https://github.com/gersteinlab/chemagent

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ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning

https://github.com/gfnorg/multi-objective-gfn

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Multi-Objective GFlowNets

https://github.com/gitter-lab/Assay2Mol

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Assay2Mol: large language model-based drug design using BioAssay context

https://github.com/gmh14/geo-deg

공식 코드

Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

https://github.com/gmum/MAT

공식 코드

Molecule Attention Transformer

https://github.com/gmum/chienn

공식 코드

ChiENN: Embracing Molecular Chirality with Graph Neural Networks

https://github.com/gnina/gnina

공식 코드

Protein-Ligand Scoring with Convolutional Neural Networks

https://github.com/google-research/google-research/tree/master/fm4tlp

공식 코드

Transfer Learning for Temporal Link Prediction

https://github.com/google/jax-md

공식 코드

JAX, M.D.: A Framework for Differentiable Physics

https://github.com/gracezhao1997/EEGSDE

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Equivariant Energy-Guided SDE for Inverse Molecular Design

https://github.com/graph-0/cdgs

공식 코드

Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation

https://github.com/graph-0/jodo

공식 코드

Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation

https://github.com/gregory-kyro/cardiogenai

공식 코드

CardioGenAI: A Machine Learning-Based Framework for Re-Engineering Drugs for Reduced hERG Liability

https://github.com/gregory-kyro/hac-net

공식 코드

HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction

https://github.com/grimmlab/graphxform

공식 코드

GraphXForm: Graph transformer for computer-aided molecular design

https://github.com/gsk-ai/leak-proof-cmap

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Leak Proof CMap; a framework for training and evaluation of cell line agnostic L1000 similarity methods

https://github.com/gt4sd/gt4sd-core

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Accelerating Material Design with the Generative Toolkit for Scientific Discovery

https://github.com/gt4sd/multitask_text_and_chemistry_t5

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Unifying Molecular and Textual Representations via Multi-task Language Modelling

https://github.com/guanghelee/iclr20-lcn

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Oblique Decision Trees from Derivatives of ReLU Networks

https://github.com/guanlueli/AMDiff

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Molecule Generation for Target Protein Binding with Hierarchical Consistency Diffusion Model

https://github.com/gwatkinson/jump_download

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Weakly supervised cross-modal learning in high-content screening

https://github.com/hankerwu/tamgent

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Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design

https://github.com/hannahrosafriesacher/CalibrationStudy

공식 코드

Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models

https://github.com/haoxuanli-pku/adrnet

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ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction

https://github.com/helikarlab/como

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COMO: A Pipeline for Multi-Omics Data Integration in Metabolic Modeling and Drug Discovery

https://github.com/heorhiis/fimba-attack

공식 코드

FIMBA: Evaluating the Robustness of AI in Genomics via Feature Importance Adversarial Attacks

https://github.com/hhw-zhou/tsmmg

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Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

https://github.com/hibakob/syntheticmicrobundle

공식 코드

MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles

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