Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey
The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. E...
Main Authors: | Roohallah Alizadehsani, Solomon Sunday Oyelere, Sadiq Hussain, Senthil Kumar Jagatheesaperumal, Rene Ripardo Calixto, Mohamed Rahouti, Mohamad Roshanzamir, Victor Hugo C. De Albuquerque |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10459028/ |
Similar Items
-
PERBEDAAN HASIL BELAJAR PESERTA DIDIK MENGGUNAKAN MODEL PEMBELAJARAN DISCOVERY LEARNING DAN MODEL PEMBELAJARAN STUDENT FACILITATOR AND EXPLAINING
by: Reynaldo Decaprio Thelessy, et al.
Published: (2022-04-01) -
Explainable Fragment‐Based Molecular Property Attribution
by: Lingxiang Jia, et al.
Published: (2022-10-01) -
Surrogate explanations for role discovery on graphs
by: Eoghan Cunningham, et al.
Published: (2023-05-01) -
xAAD–Post-Feedback Explainability for Active Anomaly Discovery
by: Damir Kopljar, et al.
Published: (2024-01-01) -
A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering
by: Håvard Horgen Thunold, et al.
Published: (2023-11-01)