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Drug discovery with explainable artificial intelligence
v1v2 (latest)

Drug discovery with explainable artificial intelligence

1 July 2020
José Jiménez-Luna
F. Grisoni
G. Schneider
ArXiv (abs)PDFHTML

Papers citing "Drug discovery with explainable artificial intelligence"

50 / 100 papers shown
SelfAI: A self-directed framework for long-horizon scientific discovery
SelfAI: A self-directed framework for long-horizon scientific discovery
Xiao Wu
Ting-Zhu Huang
Liang-Jian Deng
Xiaobing Yu
Yu Zhong
...
Jianghao Wu
Xiaofeng Liu
Imran Razzak
Xiaojun Chang
Yutong Xie
LLMAG
185
0
0
29 Nov 2025
Interpreting GFlowNets for Drug Discovery: Extracting Actionable Insights for Medicinal Chemistry
Interpreting GFlowNets for Drug Discovery: Extracting Actionable Insights for Medicinal Chemistry
Amirtha Varshini A S
Duminda S. Ranasinghe
Hok Hei Tam
90
0
0
24 Nov 2025
Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Antoine de Mathelin
Christopher Tosh
Wesley Tansey
126
0
0
09 Sep 2025
Enhancing Chemical Explainability Through Counterfactual Masking
Enhancing Chemical Explainability Through Counterfactual Masking
Łukasz Janisiów
Marek Kochańczyk
Bartosz Zieliñski
Tomasz Danel
AAML
149
0
0
25 Aug 2025
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
Sonal Allana
Mohan Kankanhalli
Rozita Dara
441
6
0
05 May 2025
Addressing Model Overcomplexity in Drug-Drug Interaction Prediction With Molecular Fingerprints
Addressing Model Overcomplexity in Drug-Drug Interaction Prediction With Molecular Fingerprints
Manel Gil-Sorribes
Alexis Molina
210
0
0
30 Mar 2025
Logic Explanation of AI Classifiers by Categorical Explaining Functors
Logic Explanation of AI Classifiers by Categorical Explaining Functors
S. Fioravanti
Francesco Giannini
Paolo Frazzetto
Fabio Zanasi
Pietro Barbiero
304
0
0
20 Mar 2025
UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials
UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials
Gongbo Zhang
Yongbin Li
Renqian Luo
Pipi Hu
Zeru Zhao
...
Xiuyuan Hu
Li-ming Yuan
Lei Chen
Haiguang Liu
Tao Qin
DiffM
360
3
0
09 Mar 2025
Human-centered explanation does not fit all: The interplay of sociotechnical, cognitive, and individual factors in the effect AI explanations in algorithmic decision-making
Human-centered explanation does not fit all: The interplay of sociotechnical, cognitive, and individual factors in the effect AI explanations in algorithmic decision-making
Yongsu Ahn
Yu-Ru Lin
Malihe Alikhani
Eunjeong Cheon
946
1
0
17 Feb 2025
EXAGREE: Mitigating Explanation Disagreement with Stakeholder-Aligned Models
EXAGREE: Mitigating Explanation Disagreement with Stakeholder-Aligned Models
Sichao Li
Tommy Liu
Quanling Deng
Amanda S. Barnard
324
1
0
04 Nov 2024
Explainable AI needs formalization
Explainable AI needs formalization
Stefan Haufe
Rick Wilming
Benedict Clark
Rustam Zhumagambetov
Danny Panknin
Ahcène Boubekki
Danny Panknin
XAIELMLRM
543
4
0
22 Sep 2024
Towards Symbolic XAI -- Explanation Through Human Understandable Logical
  Relationships Between Features
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between FeaturesInformation Fusion (Inf. Fusion), 2024
Thomas Schnake
Farnoush Rezaei Jafaria
Jonas Lederer
Ping Xiong
Shinichi Nakajima
Stefan Gugler
G. Montavon
Klaus-Robert Müller
348
11
0
30 Aug 2024
TransMA: an explainable multi-modal deep learning model for predicting
  properties of ionizable lipid nanoparticles in mRNA delivery
TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery
Kun Wu
Zixu Wang
Xiulong Yang
Yangyang Chen
Zhenqi Han
Jialu Zhang
Lizhuang Liu
291
4
0
08 Jul 2024
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations
Rick Wilming
Artur Dox
Hjalmar Schulz
Marta Oliveira
Benedict Clark
Stefan Haufe
338
6
0
17 Jun 2024
SynthTree: Co-supervised Local Model Synthesis for Explainable
  Prediction
SynthTree: Co-supervised Local Model Synthesis for Explainable Prediction
Evgenii Kuriabov
Jia Li
203
1
0
16 Jun 2024
Lift Your Molecules: Molecular Graph Generation in Latent Euclidean
  Space
Lift Your Molecules: Molecular Graph Generation in Latent Euclidean SpaceInternational Conference on Learning Representations (ICLR), 2024
Mohamed Amine Ketata
Nicholas Gao
Johanna Sommer
Tom Wollschlager
Stephan Günnemann
DiffM
344
5
0
15 Jun 2024
Why do explanations fail? A typology and discussion on failures in XAI
Why do explanations fail? A typology and discussion on failures in XAI
Clara Bove
Thibault Laugel
Marie-Jeanne Lesot
C. Tijus
Marcin Detyniecki
311
9
0
22 May 2024
What is it for a Machine Learning Model to Have a Capability?
What is it for a Machine Learning Model to Have a Capability?British Journal for the Philosophy of Science (BJPS), 2024
Jacqueline Harding
Nathaniel Sharadin
ELM
292
12
0
14 May 2024
Interpretability Needs a New Paradigm
Interpretability Needs a New Paradigm
Andreas Madsen
Himabindu Lakkaraju
Siva Reddy
Sarath Chandar
223
10
0
08 May 2024
Strategic Integration of Artificial Intelligence in the C-Suite: The Role of the Chief AI Officer
Strategic Integration of Artificial Intelligence in the C-Suite: The Role of the Chief AI Officer
Marc Schmitt
319
11
0
30 Apr 2024
Variational Optimization for Quantum Problems using Deep Generative Networks
Variational Optimization for Quantum Problems using Deep Generative Networks
Lingxia Zhang
Xiaodie Lin
Peidong Wang
Kaiyan Yang
Xiao Zeng
Zhaohui Wei
Zizhu Wang
241
7
0
28 Apr 2024
Active Causal Learning for Decoding Chemical Complexities with Targeted
  Interventions
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions
Zachary R Fox
Ayana Ghosh
AI4CE
254
7
0
05 Apr 2024
Foundation Model for Advancing Healthcare: Challenges, Opportunities,
  and Future Directions
Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future DirectionsIEEE Reviews in Biomedical Engineering (RBME), 2024
Yuting He
Fuxiang Huang
Xinrui Jiang
Yuxiang Nie
Minghao Wang
Jiguang Wang
Hao Chen
LM&MAAI4CE
428
117
0
04 Apr 2024
An Incremental MaxSAT-based Model to Learn Interpretable and Balanced
  Classification Rules
An Incremental MaxSAT-based Model to Learn Interpretable and Balanced Classification Rules
Antônio Carlos Souza Ferreira Júnior
Thiago Alves Rocha
346
0
0
25 Mar 2024
How Human-Centered Explainable AI Interface Are Designed and Evaluated:
  A Systematic Survey
How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey
Thu Nguyen
Alessandro Canossa
Jichen Zhu
218
15
0
21 Mar 2024
Infrared Spectra Prediction for Diazo Groups Utilizing a Machine
  Learning Approach with Structural Attention Mechanism
Infrared Spectra Prediction for Diazo Groups Utilizing a Machine Learning Approach with Structural Attention Mechanism
Chengchun Liu
Fanyang Mo
214
0
0
05 Feb 2024
Deep Learning for Efficient GWAS Feature Selection
Deep Learning for Efficient GWAS Feature Selection
Kexuan Li
160
0
0
22 Dec 2023
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment
  Design
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
Clare Lyle
Arash Mehrjou
Pascal Notin
Andrew Jesson
Stefan Bauer
Y. Gal
Patrick Schwab
265
16
0
07 Dec 2023
Deployment of a Robust and Explainable Mortality Prediction Model: The
  COVID-19 Pandemic and Beyond
Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond
Jacob R. Epifano
Stephen Glass
Ravichandran Ramachandran
Sharad Patel
A. Masino
Ghulam Rasool
240
1
0
28 Nov 2023
Designing Interpretable ML System to Enhance Trust in Healthcare: A
  Systematic Review to Proposed Responsible Clinician-AI-Collaboration
  Framework
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework
Elham Nasarian
R. Alizadehsani
U. Acharya
Kwok-Leung Tsui
327
140
0
18 Nov 2023
Generative Explanations for Graph Neural Network: Methods and
  Evaluations
Generative Explanations for Graph Neural Network: Methods and Evaluations
Jialin Chen
Kenza Amara
Junchi Yu
Rex Ying
295
5
0
09 Nov 2023
Proto-lm: A Prototypical Network-Based Framework for Built-in
  Interpretability in Large Language Models
Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP), 2023
Sean Xie
Soroush Vosoughi
Saeed Hassanpour
373
8
0
03 Nov 2023
Explainable Artificial Intelligence for Drug Discovery and Development
  -- A Comprehensive Survey
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive SurveyIEEE Access (IEEE Access), 2023
R. Alizadehsani
Solomon Sunday Oyelere
Sadiq Hussain
Rene Ripardo Calixto
V. H. C. de Albuquerque
M. Roshanzamir
Mohamed Rahouti
Senthil Kumar Jagatheesaperumal
222
58
0
21 Sep 2023
The role of causality in explainable artificial intelligence
The role of causality in explainable artificial intelligence
Gianluca Carloni
Andrea Berti
Sara Colantonio
CMLXAI
333
46
0
18 Sep 2023
A Dual-Perspective Approach to Evaluating Feature Attribution Methods
A Dual-Perspective Approach to Evaluating Feature Attribution Methods
Yawei Li
Yanglin Zhang
Kenji Kawaguchi
Ashkan Khakzar
B. Bischl
Mina Rezaei
FAttXAI
248
0
0
17 Aug 2023
Identifying Explanation Needs of End-users: Applying and Extending the
  XAI Question Bank
Identifying Explanation Needs of End-users: Applying and Extending the XAI Question BankMessage Understanding Conference (MUC), 2023
Lars Sipos
Ulrike Schäfer
Katrin Glinka
Claudia Muller-Birn
219
18
0
18 Jul 2023
HOPE: High-order Polynomial Expansion of Black-box Neural Networks
HOPE: High-order Polynomial Expansion of Black-box Neural NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
Tingxiong Xiao
Weihang Zhang
Yuxiao Cheng
J. Suo
290
3
0
17 Jul 2023
Identifying Important Sensory Feedback for Learning Locomotion Skills
Identifying Important Sensory Feedback for Learning Locomotion SkillsNature Machine Intelligence (Nat. Mach. Intell.), 2023
Wanming Yu
Chuanyu Yang
C. McGreavy
Eleftherios Triantafyllidis
Guillaume Bellegarda
M. Shafiee
A. Ijspeert
Zhibin Li
280
23
0
29 Jun 2023
Pushing the Limits of Machine Design: Automated CPU Design with AI
Pushing the Limits of Machine Design: Automated CPU Design with AI
Shuyao Cheng
Pengwei Jin
Qi Guo
Zidong Du
Rui Zhang
...
Xishan Zhang
Yuejie Chu
W. Mao
Tianshi Chen
Yunji Chen
277
8
0
21 Jun 2023
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Adversarial attacks and defenses in explainable artificial intelligence: A surveyInformation Fusion (Inf. Fusion), 2023
Hubert Baniecki
P. Biecek
AAML
634
141
0
06 Jun 2023
Balancing Exploration and Exploitation: Disentangled $β$-CVAE in De
  Novo Drug Design
Balancing Exploration and Exploitation: Disentangled βββ-CVAE in De Novo Drug Design
Guang Jun Nicholas Ang
Deanne Chin
Bingquan Shen
242
0
0
02 Jun 2023
Theoretical Behavior of XAI Methods in the Presence of Suppressor
  Variables
Theoretical Behavior of XAI Methods in the Presence of Suppressor VariablesInternational Conference on Machine Learning (ICML), 2023
Rick Wilming
Leo Kieslich
Benedict Clark
Stefan Haufe
209
17
0
02 Jun 2023
CongFu: Conditional Graph Fusion for Drug Synergy Prediction
CongFu: Conditional Graph Fusion for Drug Synergy Prediction
Oleksii Tsepa
Bohdan Naida
Anna Goldenberg
Bo Wang
242
2
0
23 May 2023
Uncertainty Quantification in Machine Learning for Engineering Design
  and Health Prognostics: A Tutorial
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A TutorialMechanical systems and signal processing (MSSP), 2023
V. Nemani
Luca Biggio
Xun Huan
Zhen Hu
Olga Fink
Anh Tran
Yan Wang
Xiaoge Zhang
Chao Hu
AI4CE
321
151
0
07 May 2023
Categorical Foundations of Explainable AI: A Unifying Theory
Categorical Foundations of Explainable AI: A Unifying Theory
Pietro Barbiero
S. Fioravanti
Francesco Giannini
Alberto Tonda
Pietro Lio
Elena Di Lavore
XAI
274
6
0
27 Apr 2023
Unstructured and structured data: Can we have the best of both worlds
  with large language models?
Unstructured and structured data: Can we have the best of both worlds with large language models?IEEE Data Engineering Bulletin (IEEE Data Eng. Bull.), 2023
W. Tan
342
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Quantifying the Benefit of Artificial Intelligence for Scientific
  Research
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Jian Gao
Dashun Wang
196
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A Comprehensive Survey on Deep Graph Representation Learning
A Comprehensive Survey on Deep Graph Representation LearningNeural Networks (Neural Netw.), 2023
Wei Ju
Zheng Fang
Yiyang Gu
Zequn Liu
Qingqing Long
...
Jingyang Yuan
Yusheng Zhao
Yifan Wang
Xiao Luo
Ming Zhang
GNNAI4TS
573
283
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Graph Representation Learning for Interactive Biomolecule Systems
Graph Representation Learning for Interactive Biomolecule Systems
Xinye Xiong
Bingxin Zhou
Yu Guang Wang
AI4CEGNN
554
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05 Apr 2023
Distill n' Explain: explaining graph neural networks using simple
  surrogates
Distill n' Explain: explaining graph neural networks using simple surrogatesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Tamara A. Pereira
Erik Nasciment
Lucas Resck
Diego Mesquita
Amauri Souza
225
28
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