An updated review of computer‐aided drug design and its application to COVID‐19
The recent outbreak of the deadly coronavirus disease 19 (COVID‐19) pandemic poses
serious health concerns around the world. The lack of approved drugs or vaccines continues …
serious health concerns around the world. The lack of approved drugs or vaccines continues …
Application of computational biology and artificial intelligence in drug design
Traditional drug design requires a great amount of research time and developmental
expense. Booming computational approaches, including computational biology, computer …
expense. Booming computational approaches, including computational biology, computer …
[HTML][HTML] Computational methods in drug discovery
SP Leelananda, S Lindert - Beilstein journal of organic …, 2016 - beilstein-journals.org
The process for drug discovery and development is challenging, time consuming and
expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut …
expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut …
Protein–ligand docking in the machine-learning era
Molecular docking plays a significant role in early-stage drug discovery, from structure-
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …
Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks
Motivation Capturing long-range interactions between structural but not sequence neighbors
of proteins is a long-standing challenging problem in bioinformatics. Recently, long short …
of proteins is a long-standing challenging problem in bioinformatics. Recently, long short …
Improved protein structure prediction using a new multi‐scale network and homologous templates
The accuracy of de novo protein structure prediction has been improved considerably in
recent years, mostly due to the introduction of deep learning techniques. In this work …
recent years, mostly due to the introduction of deep learning techniques. In this work …
The realm of unconventional noncovalent interactions in proteins: their significance in structure and function
VA Adhav, K Saikrishnan - Acs Omega, 2023 - ACS Publications
Proteins and their assemblies are fundamental for living cells to function. Their complex
three-dimensional architecture and its stability are attributed to the combined effect of …
three-dimensional architecture and its stability are attributed to the combined effect of …
DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model
Motivation Identifying the functional sites of a protein, such as the binding sites of proteins,
peptides, or other biological components, is crucial for understanding related biological …
peptides, or other biological components, is crucial for understanding related biological …
GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues
Y **a, CQ **a, X Pan, HB Shen - Nucleic acids research, 2021 - academic.oup.com
Abstract Knowledge of the interactions between proteins and nucleic acids is the basis of
understanding various biological activities and designing new drugs. How to accurately …
understanding various biological activities and designing new drugs. How to accurately …
Protein structure prediction: conventional and deep learning perspectives
Protein structure prediction is a way to bridge the sequence-structure gap, one of the main
challenges in computational biology and chemistry. Predicting any protein's accurate …
challenges in computational biology and chemistry. Predicting any protein's accurate …