Concepts of artificial intelligence for computer-assisted drug discovery
X Yang, Y Wang, R Byrne, G Schneider… - Chemical …, 2019 - ACS Publications
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …
opportunities for the discovery and development of innovative drugs. Various machine …
Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship
The paper presents a comprehensive overview of the use of artificial intelligence (AI)
systems in drug design. Neural networks, which are one of the systems employed in AI, are …
systems in drug design. Neural networks, which are one of the systems employed in AI, are …
Machine learning in arrhythmia and electrophysiology
Machine learning (ML), a branch of artificial intelligence, where machines learn from big
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …
Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector …
Y Zhang, S Lu, X Zhou, M Yang, L Wu, B Liu… - …, 2016 - journals.sagepub.com
In order to detect multiple sclerosis (MS) subjects from healthy controls (HCs) in magnetic
resonance imaging, we developed a new system based on machine learning. The MS …
resonance imaging, we developed a new system based on machine learning. The MS …
Review of machine learning and deep learning models for toxicity prediction
The ever-increasing number of chemicals has raised public concerns due to their adverse
effects on human health and the environment. To protect public health and the environment …
effects on human health and the environment. To protect public health and the environment …
Machine learning and artificial intelligence: a paradigm shift in big data-driven drug design and discovery
Background: The lengthy and expensive process of developing a novel medicine often takes
many years and entails a significant financial burden due to its poor success rate …
many years and entails a significant financial burden due to its poor success rate …
Critical assessment of artificial intelligence methods for prediction of hERG channel inhibition in the “big data” era
The rise of novel artificial intelligence (AI) methods necessitates their benchmarking against
classical machine learning for a typical drug-discovery project. Inhibition of the potassium …
classical machine learning for a typical drug-discovery project. Inhibition of the potassium …
[HTML][HTML] hERG-Att: Self-attention-based deep neural network for predicting hERG blockers
A voltage-gated potassium channel encoded by the human ether-à-go-go-related gene
(hERG) regulates cardiac action potential, and it is involved in cardiotoxicity with compounds …
(hERG) regulates cardiac action potential, and it is involved in cardiotoxicity with compounds …
Experimentally validated pharmacoinformatics approach to predict hERG inhibition potential of new chemical entities
The hERG (human ether-a-go-go-related gene) encoded potassium ion (K+) channel plays
a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major …
a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major …
Prediction of hERG K+ channel blockage using deep neural networks
Y Zhang, J Zhao, Y Wang, Y Fan, L Zhu… - Chemical biology & …, 2019 - Wiley Online Library
Human ether‐a‐go‐go‐related gene (hERG) K+ channel blockage may cause severe
cardiac side‐effects and has become a serious issue in safety evaluation of drug …
cardiac side‐effects and has become a serious issue in safety evaluation of drug …