Drug Discovery
372 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
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
recursionpharma/maes_microscopy
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Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images.
A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions
ai-hpc-research-team/slm4crp
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The task of chemical reaction predictions (CRPs) plays a pivotal role in advancing drug discovery and material science.
Drug-target interaction prediction by integrating heterogeneous information with mutual attention network
lipi12q/drugman
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DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug-target prediction.
FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation
qizhipei/fabind
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Molecular docking is a pivotal process in drug discovery.
Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation
DevSlem/Mol-AIR
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We believe that Mol-AIR represents a significant advancement in drug discovery, offering a more efficient path to discovering novel therapeutics.
A Python library for efficient computation of molecular fingerprints
In this project, we created a Python library that computes molecular fingerprints efficiently and delivers an interface that is comprehensive and enables the user to easily incorporate the library into their existing machine learning workflow.
Grad-CAMO: Learning Interpretable Single-Cell Morphological Profiles from 3D Cell Painting Images
Despite their black-box nature, deep learning models are extensively used in image-based drug discovery to extract feature vectors from single cells in microscopy images.
NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks
r08b46009/code_for_migu_nana
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In this paper, we propose novel semantic data augmentation methods, Novel Augmentation of New Node Attributes (NaNa), and Molecular Interactions and Geometric Upgrading (MiGu) to incorporate backbone chemical and side-chain biophysical information into protein classification tasks and a co-embedding residual learning framework.
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
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.
