Drug Discovery
376 papers with code • 28 benchmarks • 24 datasets
Drug discovery is the task of applying machine learning to discover new candidate drugs.
( Image credit: A Turing Test for Molecular Generators )
Benchmarks
These leaderboards are used to track progress in Drug Discovery
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Libraries
Use these libraries to find Drug Discovery models and implementationsDatasets
QM9
Tox21
BACE (β-secretase enzyme)
SIDER
QED
BindingDB
Latest papers
Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model
hhw-zhou/tsmmg
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While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge.
Forward Learning of Graph Neural Networks
facebookresearch/forwardgnn
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To address these limitations, the forward-forward algorithm (FF) was recently proposed as an alternative to BP in the image classification domain, which trains NNs by performing two forward passes over positive and negative data.
An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models
Structure-based virtual screening (SBVS) is a key workflow in computational drug discovery.
MolBind: Multimodal Alignment of Language, Molecules, and Proteins
Recent advancements in biology and chemistry have leveraged multi-modal learning, integrating molecules and their natural language descriptions to enhance drug discovery.
CardioGenAI: A Machine Learning-Based Framework for Re-Engineering Drugs for Reduced hERG Liability
Drug-induced cardiotoxicity is a major health concern which can lead to serious adverse effects including life-threatening cardiac arrhythmias via the blockade of the voltage-gated hERG potassium ion channel.
Generative deep learning-enabled ultra-large field-of-view lens-free imaging
Advancements in high-throughput biomedical applications necessitate real-time, large field-of-view (FOV) imaging capabilities.
3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs
However, practical applications call for methods that generate diverse, and ideally novel, molecules with the desired properties.
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks
ml-jku/gnn-vpa
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Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling.
Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage
In such cases, marginally valid conformal prediction intervals may not provide valid coverage for the focal unit(s) due to selection bias.
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
QizhiPei/BioT5
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However, previous efforts like BioT5 faced challenges in generalizing across diverse tasks and lacked a nuanced understanding of molecular structures, particularly in their textual representations (e. g., IUPAC).
