Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
Artificial intelligence in drug discovery: recent advances and future perspectives
J Jiménez-Luna, F Grisoni, N Weskamp… - Expert opinion on drug …, 2021 - Taylor & Francis
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The
widespread adoption of machine learning, in particular deep learning, in multiple scientific …
widespread adoption of machine learning, in particular deep learning, in multiple scientific …
Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism
T Wang, J Sun, Q Zhao - Computers in biology and medicine, 2023 - Elsevier
Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big
concern during drug development in the pharmaceutical industry. Failure or inhibition of …
concern during drug development in the pharmaceutical industry. Failure or inhibition of …
scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention
R Meng, S Yin, J Sun, H Hu, Q Zhao - Computers in biology and medicine, 2023 - Elsevier
In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful
technique for investigating cellular heterogeneity and structure. However, analyzing scRNA …
technique for investigating cellular heterogeneity and structure. However, analyzing scRNA …
GraphDTA: predicting drug–target binding affinity with graph neural networks
The development of new drugs is costly, time consuming and often accompanied with safety
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …
HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks
Identifying new indications for drugs plays an essential role at many phases of drug
research and development. Computational methods are regarded as an effective way to …
research and development. Computational methods are regarded as an effective way to …
MRMD2. 0: a python tool for machine learning with feature ranking and reduction
S He, F Guo, Q Zou - Current Bioinformatics, 2020 - ingentaconnect.com
Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background:
Dimensionality reduction is the key issue of the machine learning process. It does not only …
Dimensionality reduction is the key issue of the machine learning process. It does not only …
COVID-19 Coronavirus spike protein analysis for synthetic vaccines, a peptidomimetic antagonist, and therapeutic drugs, and analysis of a proposed achilles' heel …
B Robson - Computers in biology and medicine, 2020 - Elsevier
This paper continues a recent study of the spike protein sequence of the COVID-19 virus
(SARS-CoV-2). It is also in part an introductory review to relevant computational techniques …
(SARS-CoV-2). It is also in part an introductory review to relevant computational techniques …
ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning
Motivation Recently, peptides have emerged as a promising class of pharmaceuticals for
various diseases treatment poised between traditional small molecule drugs and therapeutic …
various diseases treatment poised between traditional small molecule drugs and therapeutic …
Geometric interaction graph neural network for predicting protein–ligand binding affinities from 3d structures (gign)
Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …
Recent advances have shown great potential in applying machine learning (ML) for PLA …