Drug Discovery
28개 벤치마크 · 논문 1,712편 · 이 태스크의 논문 보기 →
Benchmarks
QM9
Tox21
BACE
HIV dataset
MUV
ToxCast
BBBP
BindingDB
DAVIS-DTA
KIBA
LIT-PCBA(ALDH1)
LIT-PCBA(KAT2A)
LIT-PCBA(MAPK1)
SIDER
clintox
LIT-PCBA(ESR1_ant)
BindingDB IC50
PCBA
DRD2
Lipophilicity (logd74)
PDBbind
QED
egfr-inh
Most implemented
Semi-Supervised Classification with Graph Convolutional Networks
Neural Message Passing for Quantum Chemistry
Self-Normalizing Neural Networks
Gated Graph Sequence Neural Networks
Junction Tree Variational Autoencoder for Molecular Graph Generation
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Papers
NEAT-POCKET: Pocket-Conditioned Autoregressive 3D Molecular Generation with a Neighborhood-Guided Set Transformer
AI-driven de novo molecular design offers a promising route to accelerate early-stage drug discovery by generating novel ligands directly within target protein binding pockets. We present NEAT-POCKET, a pocket-conditione…
Drug DiscoveryMolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions
Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning models have been built to predict ADME properties to facilitate this opti…
Graph Neural NetworkDrug DiscoveryAnswer Probing-Guided Search for Diverse Solution Exploration of LLMs
Generating multiple diverse and high-quality solutions is valuable for many applications, such as code-test generation and drug discovery. However, Large Language Models (LLMs) tend to converge on a single high-confidenc…
Drug DiscoveryMol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules
Despite recent advances in molecular foundation models, several limitations remain, such as chemically invalid augmentations, modality collapse, and incomplete representation of biochemical environments. To address these…
Drug DiscoveryDesigning a Robust LLM-Based Evaluation System for Agentic AI in Drug Discovery Through Human Alignment
Agentic large language model (LLM) systems are reshaping scientific workflows in chemistry and drug discovery, but evaluating their open-ended, tool-augmented outputs remains a fundamental bottleneck. Reference-based met…
Drug DiscoveryDistributional Extrapolation for Interactions
Predicting combinatorial effects from limited-range observations is a fundamental challenge in many scientific domains, including drug discovery and hyperparameter optimization. We study combinatorial extrapolation, wher…
Hyperparameter OptimizationDrug Discovery