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Drug Discovery
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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>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Assay2Mol: large language model-based drug design using BioAssay context
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction
Graph Learning
Exploring Modularity of Agentic Systems for Drug Discovery
Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design
Large Language Model Agent for Modular Task Execution in Drug Discovery
A standard transformer and attention with linear biases for molecular conformer generation
PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning
Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation
Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling
Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion
Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery
Bures-Wasserstein Flow Matching for Graph Generation
Fast Bayesian Optimization of Function Networks with Partial Evaluations
Robust Molecular Property Prediction via Densifying Scarce Labeled Data
Learning Causality for Modern Machine Learning
Generalization Analysis for Bayesian Optimal Experiment Design under Model Misspecification
CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction
Graph Neural Networks in Modern AI-aided Drug Discovery
AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Prediction
Agentomics-ML: Autonomous Machine Learning Experimentation Agent for Genomic and Transcriptomic Data
Latent Guided Sampling for Combinatorial Optimization
STELLA: Towards Protein Function Prediction with Multimodal LLMs Integrating Sequence-Structure Representations
AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents
How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
Recent Developments in GNNs for Drug Discovery
SOC-DGL: Social Interaction Behavior Inspired Dual Graph Learning Framework for Drug-Target Interaction Identification
Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation
Aligning Protein Conformation Ensemble Generation with Physical Feedback
Enhancing Drug Discovery: Autoencoder-Based Latent Space Augmentation for Improved Molecular Solubility Prediction using LatMixSol
RAGPPI: RAG Benchmark for Protein-Protein Interactions in Drug Discovery
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
A Graph Completion Method that Jointly Predicts Geometry and Topology Enables Effective Molecule Assembly
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