Drug Discovery
377 papers with code • 28 benchmarks • 25 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Drug Discovery models and implementationsDatasets
QM9
Tox21
BACE (β-secretase enzyme)
SIDER
QED
BindingDB
Latest papers with no code
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving convoluted meta-learning strategies.
Molecular Generative Adversarial Network with Multi-Property Optimization
Deep generative models, such as generative adversarial networks (GANs), have been employed for $de~novo$ molecular generation in drug discovery.
Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting
This study introduces a novel approach, combining substruct counting, $k$-mers, and Daylight-like fingerprints, to expand the representation of chemical structures in SMILES strings.
EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications
Artificial intelligence (AI) techniques are widely applied in the life sciences.
Bioinformatics and Biomedical Informatics with ChatGPT: Year One Review
The year 2023 marked a significant surge in the exploration of applying large language model (LLM) chatbots, notably ChatGPT, across various disciplines.
Exploring the Potential of Large Language Models in Graph Generation
In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments.
Leap: molecular synthesisability scoring with intermediates
The notion of synthesisability is dynamic as it evolves depending on the availability of key compounds.
Advances of Deep Learning in Protein Science: A Comprehensive Survey
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes.
Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecule
In this paper, we present the first systematic survey on multimodal frameworks for molecules research.
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
DecompOpt presents a new generation paradigm which combines optimization with conditional diffusion models to achieve desired properties while adhering to the molecular grammar.
