Drug Embeddings

Drug embeddings represent molecules as numerical vectors, enabling machine learning models to analyze and predict their properties and interactions. Current research focuses on developing sophisticated embedding methods, including those leveraging graph neural networks and variational autoencoders, to capture both chemical structure and biological activity, often incorporating multimodal data like text and patient health information. These advancements are improving drug discovery and repurposing efforts, particularly in predicting drug synergy, adverse drug reactions, and personalized medicine recommendations, by facilitating more accurate and efficient analyses of large datasets. The resulting improved models are leading to more effective and safer drug development strategies.

Papers

April 18, 2024

CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state
Xiang Li, Shunpan Liang, Yu Lei, Chen Li, Yulei Hou, Tengfei Ma
Medication Recommendation Causal View Relation Discovery Personalized Treatment Recommendation Drug Embeddings

November 17, 2023

Interpretable Modeling of Single-cell perturbation Responses to Novel Drugs Using Cycle Consistence Learning
Wei Huang, Aichun Zhu, Hui Liu
Interpretable Model Cycle Consistency New Drug Drug Embeddings

October 21, 2022

Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification
Andrey Sakhovskiy, Elena Tutubalina
Text Modality Multimodal Model Molecular Property Prediction BERT Based Molecular Descriptor Drug Representation Adverse Drug Drug Embeddings

September 19, 2022

Distributed representations of graphs for drug pair scoring
Paul Scherer, Pietro Liò, Mateja Jamnik
Graph Drawing Meaningful Representation Drug Embeddings Drug Pair Scoring

July 6, 2022

Hyperbolic Molecular Representation Learning for Drug Repositioning
Ke Yu, Shyam Visweswaran, Kayhan Batmanghelich
Semi Supervised Hyperbolic Representation Drug Representation Drug Repositioning Drug Embeddings

February 10, 2022

Target-aware Molecular Graph Generation
Cheng Tan, Zhangyang Gao, Stan Z. Li
Molecular Graph Molecular Generation Target Embeddings Drug Embeddings

February 7, 2022

RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro
Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma, Thomas Gaudelet, Andrew Anighoro, Torsten Gross, Francisco Martinez-Pena, Eileen L. Tang, Suraj M S, Cristian Regep, Jeremy Hayter, Maksym Korablyov, Nicholas Valiante, Almer van der Sloot, Mike Tyers, Charles Roberts, Michael M. Bronstein, Luke L. Lairson, Jake P. Taylor-King, Yoshua Bengio
Prompt Recovery High Throughput Synergistic Information Sequential Optimization Drug Combination Drug Synergy Drug Embeddings

Drug Embeddings

Papers

CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state

Interpretable Modeling of Single-cell perturbation Responses to Novel Drugs Using Cycle Consistence Learning

Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification

Distributed representations of graphs for drug pair scoring

Hyperbolic Molecular Representation Learning for Drug Repositioning

Target-aware Molecular Graph Generation

RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro