Drug Response Prediction

Drug response prediction (DRP) aims to computationally predict how individual patients or cell lines will respond to specific drugs, enabling personalized medicine and accelerating drug discovery. Current research heavily utilizes deep learning, particularly graph neural networks and transformers, to integrate diverse data sources like genomics, transcriptomics, and drug chemical structures, often employing techniques like multi-modal fusion and transfer learning to improve prediction accuracy and interpretability. These advancements hold significant promise for optimizing cancer treatment strategies and streamlining the drug development process by identifying effective therapies and minimizing the need for extensive and costly clinical trials.

Papers

September 18, 2024

Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data
Jamie C. Overbeek, Alexander Partin, Thomas S. Brettin, Nicholas Chia, Oleksandr Narykov, Priyanka Vasanthakumari, Andreas Wilke, Yitan Zhu, Austin Clyde, Sara Jones, Rohan Gnanaolivu, Yuanhang Liu, Jun Jiang, Chen Wang, Carter Knutson, Andrew McNaughton, Neeraj Kumar, Gayara Demini Fernando, Souparno Ghosh, Cesar Sanchez-Villalobos, Ruibo Zhang, Ranadip Pal, M. Ryan Weil, Rick L. Stevens
Model Evaluation Reusable Data Exhaust Drug Response Prediction Precision Oncology

August 30, 2024

Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph Neural Networks for Drug Response Prediction
Xiaodi Li, Jianfeng Gui, Qian Gao, Haoyuan Shi, Zhenyu Yue
Graph Neural Network Inherent Interpretability Edge Inference Drug Response Prediction Interpretable Graph Post Hoc Interpretability

August 22, 2024

DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network
Haoyuan Shi, Tao Xu, Xiaodi Li, Qian Gao, Junfeng Xia, Zhenyu Yue
Graph Convolutional Network Interpretable Model Model Interpretability Drug Response Prediction

July 5, 2024

Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses
Tianshu Feng, Rohan Gnanaolivu, Abolfazl Safikhani, Yuanhang Liu, Jun Jiang, Nicholas Chia, Alexander Partin, Priyanka Vasanthakumari, Yitan Zhu, Chen Wang
Variational Method Drug Response Prediction Cancer Dataset Transcriptomic Data Encoder Representation Human Interpretability

May 23, 2024

Regressor-free Molecule Generation to Support Drug Response Prediction
Kun Li, Xiuwen Gong, Shirui Pan, Jia Wu, Bo Du, Wenbin Hu
Drug Discovery Molecular Generation Classifier Free Guidance Drug Response Prediction

May 7, 2024

WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer
Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish, Prathosh AP, Vaibhav Rajan
Weak Supervision Cancer Diagnosis Deriving Wisdom Downstream Classification Drug Response Prediction Drug Response Supervised Representation

March 6, 2024

DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation
Yushuai Wu, Ting Zhang, Hao Zhou, Hainan Wu, Hanwen Sunchu, Lei Hu, Xiaofang Chen, Suyuan Zhao, Gaochao Liu, Chao Sun, Jiahuan Zhang, Yizhen Luo, Peng Liu, Zaiqing Nie, Yushuai Wu
Drug Response Prediction

February 16, 2024

Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information
Aishwarya Jayagopal, Hansheng Xue, Ziyang He, Robert J. Walsh, Krishna Kumar Hariprasannan, David Shao Peng Tan, Tuan Zea Tan, Jason J. Pitt, Anand D. Jeyasekharan, Vaibhav Rajan
Transformer Megatron Decepticons Transfer Learning Medication Recommendation Drug Response Prediction Personalized Treatment Auxiliary Information

December 17, 2023

CLDR: Contrastive Learning Drug Response Models from Natural Language Supervision
Kun Li, Wenbin Hu
Contrastive Learning Framework Language Supervision Drug Response Prediction Drug Response Regression Representation

November 17, 2023

TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representation
Xiaoqiong Xia, Chaoyu Zhu, Yuqi Shan, Fan Zhong, Lei Liu
Deep Learning Model Transfer Learning Multimodal Data Fusion Drug Response Prediction Drug Representation

October 5, 2023

Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening
Kun Li, Yong Luo, Xiantao Cai, Wenbin Hu, Bo Du
Zero Shot Learning Drug Response Prediction Drug Response Preclinical Drug Screening

September 18, 2023

Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction
Shaika Chowdhury, Sivaraman Rajaganapathy, Lichao Sun, James Cerhan, Nansu Zong
Global Evaluation GPT 3 Drug Feature Drug Response Prediction Precision Oncology Drug Response Treatment Response Prediction

June 30, 2023

Precision Anti-Cancer Drug Selection via Neural Ranking
Vishal Dey, Xia Ning
High Throughput Drug Target Interaction Neural Ranker Drug Response Prediction Drug Pair Scoring

March 9, 2023

Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals
Abdolkarim Saeedi, Arash Maghsoudi, Fereidoun Nowshiravan Rahatabad
Recurrent Neural Network LSTM Network EEG Signal Analysis CNN LSTM Drug Response Prediction Major Depressive Disorder

November 18, 2022

Deep learning methods for drug response prediction in cancer: predominant and emerging trends
Alexander Partin, Thomas S. Brettin, Yitan Zhu, Oleksandr Narykov, Austin Clyde, Jamie Overbeek, Rick L. Stevens
Deep Learning Deep Learning Model NCD Method Recent Trend Cancer Diagnosis Drug Response Prediction Cancer Response

November 10, 2022

Hybrid quantum neural network for drug response prediction
Asel Sagingalieva, Mohammad Kordzanganeh, Nurbolat Kenbayev, Daria Kosichkina, Tatiana Tomashuk, Alexey Melnikov
Neural Network Quantum Machine Learning Deep Quantum Drug Response Prediction Personalized Medicine Hybrid Quantum

October 23, 2022

TransEDRP: Dual Transformer model with Edge Emdedded for Drug Respond Prediction
Li Kun, Hu Wenbin
Transformer Architecture Graph Transformer Drug Response Prediction Drug Molecule

October 14, 2022

Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms
Shengjia Chen, Nikunj Gupta, Woodward B. Galbraith, Valay Shah, Jacopo Cirrone
Machine Learning Human Prediction Machine Learning Algorithm Drug Response Prediction Rheumatoid Arthritis

August 31, 2022

A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction
Tony Hauptmann, Stefan Kramer
Latent Representation Neural Architecture Multi Omics Data Multi Omics Drug Response Prediction

May 5, 2022

A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior
Mingyu Lu, Yifang Chen, Su-In Lee
Environment Exploration Contextual Bandit Drug Discovery Drug Feature Drug Response Prediction Precision Oncology Neural Bandit Functional Prior

Drug Response Prediction

Papers

Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data

Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph Neural Networks for Drug Response Prediction

DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network

Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses

Regressor-free Molecule Generation to Support Drug Response Prediction

WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer

DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation

Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information

CLDR: Contrastive Learning Drug Response Models from Natural Language Supervision

TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representation

Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening

Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction

Precision Anti-Cancer Drug Selection via Neural Ranking

Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals

Deep learning methods for drug response prediction in cancer: predominant and emerging trends

Hybrid quantum neural network for drug response prediction

TransEDRP: Dual Transformer model with Edge Emdedded for Drug Respond Prediction

Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms

A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction

A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior