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Drug Discovery

Drug discovery is the task of applying machine learning to discover new candidate drugs.

( Image credit: A Turing Test for Molecular Generators )

Papers

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Showing 1–10 of 1337 papers

TitleStatusHype
Assay2Mol: large language model-based drug design using BioAssay contextCode0
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction—0
Graph Learning—0
Exploring Modularity of Agentic Systems for Drug Discovery—0
Large Language Model Agent for Modular Task Execution in Drug Discovery—0
Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design—0
A standard transformer and attention with linear biases for molecular conformer generation—0
PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket ConditioningCode2
Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
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All datasetsQM9Tox21BACEHIV datasetMUVToxCastBBBPBindingDBclintoxDAVIS-DTAKIBALIT-PCBA(ALDH1)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Molecular Graph ConvolutionsError ratio2.59—Unverified
2Gated Graph Sequence NNError ratio1.36—Unverified
3MPNNsError ratio0.68—Unverified
4DeepMoleNetError ratio0.53—Unverified
5DimeNetError ratio0.44—Unverified
6PaiNNError ratio0.41—Unverified
7DimeNet++Error ratio0.41—Unverified
8ComENetError ratio0.4—Unverified
9SphereNetError ratio0.39—Unverified
10MXMNetError ratio0.38—Unverified