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Biomedicines
Special Issues
Artificial Intelligence for Drug Discovery and Developments
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Artificial Intelligence for Drug Discovery and Developments

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Drug Discovery, Development and Delivery".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 7129

Special Issue Editors

Prof. Dr. Lei Wang

E-Mail Website
Guest Editor
1. Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China
2. Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
Interests: bioinformatics; system biology; machine learning and complex networks
Prof. Dr. Zhu-Hong You

E-Mail Website
Guest Editor
School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
Interests: complex network; pattern recognition; deep learning and bioinformatics data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent computing technology has demonstrated its capabilities in almost all fields of science and engineering. The use of intelligent computing techniques to explore mechanisms in drug discovery and to draw meaningful conclusions is playing an increasingly important role in biology and medicine. Intelligent computational methods have shown their superior potential in drug target identification, drug repurposing, and drug molecular recombination, helping to solve problems such as drug design and disease diagnosis. Biological and medical data have the characteristics of complex format, large amount of data, high data dimension, poor data quality, and high levels of noise. Therefore, the use of intelligent computing techniques to analyze and interpret these data is becoming a hot topic in computational biology research. Therefore, we are organizing a Special Issue of Biomedicines entitled Artificial Intelligence for Drug Discovery and Development and are soliciting technical papers on drug discovery, proteomics, non-coding RNA association recognition, and bioinformatics through intelligent computing technologies. The purpose of this Special Issue is to present the latest research advances in the field of bioinformatics, and we welcome your contributions!

Prof. Dr. Lei Wang
Prof. Dr. Zhu-Hong You
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioinformatics
  • intelligent drug design
  • protein-protein interactions
  • drug-target interactions
  • gene expression
  • disease
  • non-coding RNA
  • intelligent computing
  • machine-learning
  • neural network

Published Papers (4 papers)

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Research

13 pages, 2235 KiB  
Article
High-Accuracy ncRNA Function Prediction via Deep Learning Using Global and Local Sequence Information
by Alessandro Orro and Gabriele A. Trombetti
Biomedicines 2023, 11(6), 1631; https://doi.org/10.3390/biomedicines11061631 - 03 Jun 2023
Viewed by 1306
Abstract
The prediction of the biological function of non-coding ribonucleic acid (ncRNA) is an important step towards understanding the regulatory mechanisms underlying many diseases. Since non-coding RNAs are present in great abundance in human cells and are functionally diverse, developing functional prediction tools is [...] Read more.
The prediction of the biological function of non-coding ribonucleic acid (ncRNA) is an important step towards understanding the regulatory mechanisms underlying many diseases. Since non-coding RNAs are present in great abundance in human cells and are functionally diverse, developing functional prediction tools is necessary. With recent advances in non-coding RNA biology and the availability of complete genome sequences for a large number of species, we now have a window of opportunity for studying non-coding RNA biology. However, the computational methods used to predict the non-coding RNA functions are mostly either scarcely accurate, when based on sequence information alone, or prohibitively expensive in terms of computational burden when a secondary structure prediction is needed. We propose a novel computational method to predict the biological function of non-coding RNA genes that is based on a collection of deep network architectures utilizing solely ncRNA sequence information and which does not rely on or require expensive secondary ncRNA structure information. The approach presented in this work exhibits comparable or superior accuracy to methods that employ both sequence and structural features, at a much lower computational cost. Full article
(This article belongs to the Special Issue Artificial Intelligence for Drug Discovery and Developments)
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13 pages, 1438 KiB  
Article
Application of Convolutional Neural Networks Using Action Potential Shape for In-Silico Proarrhythmic Risk Assessment
by Da Un Jeong, Yedam Yoo, Aroli Marcellinus and Ki Moo Lim
Biomedicines 2023, 11(2), 406; https://doi.org/10.3390/biomedicines11020406 - 30 Jan 2023
Cited by 4 | Viewed by 1238
Abstract
This study proposes a convolutional neural network (CNN) model using action potential (AP) shapes as input for proarrhythmic risk assessment, considering the hypothesis that machine-learning features automatically extracted from AP shapes contain more meaningful information than do manually extracted indicators. We used 28 [...] Read more.
This study proposes a convolutional neural network (CNN) model using action potential (AP) shapes as input for proarrhythmic risk assessment, considering the hypothesis that machine-learning features automatically extracted from AP shapes contain more meaningful information than do manually extracted indicators. We used 28 drugs listed in the comprehensive in vitro proarrhythmia assay (CiPA), consisting of eight high-risk, eleven intermediate-risk, and nine low-risk torsadogenic drugs. We performed drug simulations to generate AP shapes using experimental drug data, obtaining 2000 AP shapes per drug. The proposed CNN model was trained to classify the TdP risk into three levels, high-, intermediate-, and low-risk, based on in silico AP shapes generated using 12 drugs. We then evaluated the performance of the proposed model for 16 drugs. The classification accuracy of the proposed CNN model was excellent for high- and low-risk drugs, with AUCs of 0.914 and 0.951, respectively. The model performance for intermediate-risk drugs was good, at 0.814. Our proposed model can accurately assess the TdP risks of drugs from in silico AP shapes, reflecting the pharmacokinetics of ionic currents. We need to secure more drugs for future studies to improve the TdP-risk-assessment robustness. Full article
(This article belongs to the Special Issue Artificial Intelligence for Drug Discovery and Developments)
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15 pages, 2236 KiB  
Article
GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity
by Haelee Bae and Hojung Nam
Biomedicines 2023, 11(1), 67; https://doi.org/10.3390/biomedicines11010067 - 27 Dec 2022
Cited by 2 | Viewed by 2465
Abstract
Drug-target binding affinity (DTA) prediction is an essential step in drug discovery. Drug-target protein binding occurs at specific regions between the protein and drug, rather than the entire protein and drug. However, existing deep-learning DTA prediction methods do not consider the interactions between [...] Read more.
Drug-target binding affinity (DTA) prediction is an essential step in drug discovery. Drug-target protein binding occurs at specific regions between the protein and drug, rather than the entire protein and drug. However, existing deep-learning DTA prediction methods do not consider the interactions between drug substructures and protein sub-sequences. This work proposes GraphATT-DTA, a DTA prediction model that constructs the essential regions for determining interaction affinity between compounds and proteins, modeled with an attention mechanism for interpretability. We make the model consider the local-to-global interactions with the attention mechanism between compound and protein. As a result, GraphATT-DTA shows an improved prediction of DTA performance and interpretability compared with state-of-the-art models. The model is trained and evaluated with the Davis dataset, the human kinase dataset; an external evaluation is achieved with the independently proposed human kinase dataset from the BindingDB dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence for Drug Discovery and Developments)
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13 pages, 2186 KiB  
Article
SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks
by Ying Wang, Lin-Lin Wang, Leon Wong, Yang Li, Lei Wang and Zhu-Hong You
Biomedicines 2022, 10(7), 1543; https://doi.org/10.3390/biomedicines10071543 - 29 Jun 2022
Cited by 1 | Viewed by 1288
Abstract
Protein is the basic organic substance that constitutes the cell and is the material condition for the life activity and the guarantee of the biological function activity. Elucidating the interactions and functions of proteins is a central task in exploring the mysteries of [...] Read more.
Protein is the basic organic substance that constitutes the cell and is the material condition for the life activity and the guarantee of the biological function activity. Elucidating the interactions and functions of proteins is a central task in exploring the mysteries of life. As an important protein interaction, self-interacting protein (SIP) has a critical role. The fast growth of high-throughput experimental techniques among biomolecules has led to a massive influx of available SIP data. How to conduct scientific research using the massive amount of SIP data has become a new challenge that is being faced in related research fields such as biology and medicine. In this work, we design an SIP prediction method SIPGCN using a deep learning graph convolutional network (GCN) based on protein sequences. First, protein sequences are characterized using a position-specific scoring matrix, which is able to describe the biological evolutionary message, then their hidden features are extracted by the deep learning method GCN, and, finally, the random forest is utilized to predict whether there are interrelationships between proteins. In the cross-validation experiment, SIPGCN achieved 93.65% accuracy and 99.64% specificity in the human data set. SIPGCN achieved 90.69% and 99.08% of these two indicators in the yeast data set, respectively. Compared with other feature models and previous methods, SIPGCN showed excellent results. These outcomes suggest that SIPGCN may be a suitable instrument for predicting SIP and may be a reliable candidate for future wet experiments. Full article
(This article belongs to the Special Issue Artificial Intelligence for Drug Discovery and Developments)
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