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Biomolecules
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Advances in Drug Design and Development for Human Therapeutics Using...
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Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 30510

Special Issue Editors

Prof. Dr. Dongqing Wei

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Guest Editor
Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: bioinformatics; computational physics; computational chemistry; quantum computing; drug design; AI drug; protein dynamics; personalized medicine; high pressure physics; energetic materials
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gilles Peslherbe

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Guest Editor
Centre for Research in Molecular Modeling (CERMM), Concordia University, Montreal, QC H4B1R6, Canada
Interests: biophysical chemistry; drug repurposing and molecular modeling; computational chemistry; quantum computing; materials and multi-scale modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Gurudeeban Selvaraj

E-Mail Website
Guest Editor
Centre for Research in Molecular Modeling (CERMM), Concordia University, Montreal, QC H4B1R6, Canada
Interests: biomedical informatics; multi-omics; machine learning; quantum computing machine learning; drug design; molecular property prediction; precision medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Yanjing Wang

E-Mail Website
Guest Editor
Department of Bioinformatics and Biostatistics, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: machine learning and drug design; computational structural biology; cancer genomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and the related sub-technologies (machine learning and deep learning) are anticipated to make the development of novel therapeutics quicker, more effective, and inexpensive. AI can be applied to all the key areas of the pharmaceutical industries, such as drug discovery and development, drug repurposing, and improving productivity within a short period of time. The contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Thus, this Special Issue aims to present an overview of recent advances in computational modeling, machine learning, and deep learning to identify therapeutic targets, candidate drugs, molecular interactions, and their mechanisms of action. This Special Issue seeks high-quality original and review articles on these themes, including also the use of AI in drug design, poly-pharmacology, drug repositioning, drug screening, target identification, drug resistance prediction, and chemical synthesis.

Prof. Dongqing Wei
Prof. Dr. Gilles Peslherbe
Dr. Gurudeeban Selvaraj
Dr. Yanjing Wang
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. Biomolecules 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 2700 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

  • AI in a quantitative structure-activity relationship (QSAR)
  • deep learning in drug discovery
  • drug delivery and AI
  • graph neural networks
  • AI models for drug resistance prediction
  • molecular dynamic simulations
  • structure and ligand-based pharmacophore
  • target protein structure prediction
  • AI-based peptide inhibitor design
  • AI models for drug property prediction
  • AI-based webservers and drug databases

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Related Special Issue

Published Papers (7 papers)

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Editorial

Jump to: Research

3 pages, 163 KiB  
Editorial
Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence—I
by Dongqing Wei, Gilles H. Peslherbe, Gurudeeban Selvaraj and Yanjing Wang
Biomolecules 2022, 12(12), 1846; https://doi.org/10.3390/biom12121846 - 10 Dec 2022
Cited by 2 | Viewed by 1499
Abstract
Artificial intelligence (AI) has emerged as a key player in modern healthcare, especially in the pharmaceutical industry for the development of new drugs and vaccine candidates [...] Full article
(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence)

Research

Jump to: Editorial

16 pages, 2273 KiB  
Article
DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer’s Disease
by Jacqueline Chyr, Haoran Gong and Xiaobo Zhou
Biomolecules 2022, 12(2), 196; https://doi.org/10.3390/biom12020196 - 24 Jan 2022
Cited by 8 | Viewed by 4906
Abstract
Alzheimer’s disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. There is an urgent need to [...] Read more.
Alzheimer’s disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. There is an urgent need to develop novel anti-AD therapies; however, drug discovery is a time-consuming, expensive, and high-risk process. Drug repositioning, on the other hand, is an attractive approach to identify drugs for AD treatment. Thus, we developed a novel deep learning method called DOTA (Drug repositioning approach using Optimal Transport for Alzheimer’s disease) to repurpose effective FDA-approved drugs for AD. Specifically, DOTA consists of two major autoencoders: (1) a multi-modal autoencoder to integrate heterogeneous drug information and (2) a Wasserstein variational autoencoder to identify effective AD drugs. Using our approach, we predict that antipsychotic drugs with circadian effects, such as quetiapine, aripiprazole, risperidone, suvorexant, brexpiprazole, olanzapine, and trazadone, will have efficacious effects in AD patients. These drugs target important brain receptors involved in memory, learning, and cognition, including serotonin 5-HT2A, dopamine D2, and orexin receptors. In summary, DOTA repositions promising drugs that target important biological pathways and are predicted to improve patient cognition, circadian rhythms, and AD pathogenesis. Full article
(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence)
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23 pages, 3302 KiB  
Article
MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra
by Aditya Divyakant Shrivastava, Neil Swainston, Soumitra Samanta, Ivayla Roberts, Marina Wright Muelas and Douglas B. Kell
Biomolecules 2021, 11(12), 1793; https://doi.org/10.3390/biom11121793 - 30 Nov 2021
Cited by 40 | Viewed by 7981
Abstract
The ‘inverse problem’ of mass spectrometric molecular identification (‘given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came’) is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number [...] Read more.
The ‘inverse problem’ of mass spectrometric molecular identification (‘given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came’) is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem (‘calculate a small molecule’s likely fragmentation and hence at least some of its mass spectrum from its structure alone’) is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the ‘translation’ a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the ‘true’ molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are ‘similar’ to the top hit. In addition to using the ‘top hits’ directly, we can produce a rank order of these by ‘round-tripping’ candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to ‘learn’ millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra. Full article
(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence)
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19 pages, 4901 KiB  
Article
EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction
by Yuan Jin, Jiarui Lu, Runhan Shi and Yang Yang
Biomolecules 2021, 11(12), 1783; https://doi.org/10.3390/biom11121783 - 29 Nov 2021
Cited by 17 | Viewed by 3875
Abstract
The identification of drug-target interaction (DTI) plays a key role in drug discovery and development. Benefitting from large-scale drug databases and verified DTI relationships, a lot of machine-learning methods have been developed to predict DTIs. However, due to the difficulty in extracting useful [...] Read more.
The identification of drug-target interaction (DTI) plays a key role in drug discovery and development. Benefitting from large-scale drug databases and verified DTI relationships, a lot of machine-learning methods have been developed to predict DTIs. However, due to the difficulty in extracting useful information from molecules, the performance of these methods is limited by the representation of drugs and target proteins. This study proposes a new model called EmbedDTI to enhance the representation of both drugs and target proteins, and improve the performance of DTI prediction. For protein sequences, we leverage language modeling for pretraining the feature embeddings of amino acids and feed them to a convolutional neural network model for further representation learning. For drugs, we build two levels of graphs to represent compound structural information, namely the atom graph and substructure graph, and adopt graph convolutional network with an attention module to learn the embedding vectors for the graphs. We compare EmbedDTI with the existing DTI predictors on two benchmark datasets. The experimental results show that EmbedDTI outperforms the state-of-the-art models, and the attention module can identify the components crucial for DTIs in compounds. Full article
(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence)
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15 pages, 3499 KiB  
Article
MCN-CPI: Multiscale Convolutional Network for Compound–Protein Interaction Prediction
by Shuang Wang, Mingjian Jiang, Shugang Zhang, Xiaofeng Wang, Qing Yuan, Zhiqiang Wei and Zhen Li
Biomolecules 2021, 11(8), 1119; https://doi.org/10.3390/biom11081119 - 29 Jul 2021
Cited by 29 | Viewed by 3576
Abstract
In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. However, the compound–protein interaction is complicated and the [...] Read more.
In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. However, the compound–protein interaction is complicated and the features extracted by most deep models are not comprehensive, which limits the performance to a certain extent. In this paper, we proposed a multiscale convolutional network that extracted the local and global features of the protein and the topological feature of the compound using different types of convolutional networks. The results showed that our model obtained the best performance compared with the existing deep learning methods. Full article
(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence)
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15 pages, 5275 KiB  
Article
Remdesivir MD Simulations Suggest a More Favourable Binding to SARS-CoV-2 RNA Dependent RNA Polymerase Mutant P323L Than Wild-Type
by Anwar Mohammad, Fahd Al-Mulla, Dong-Qing Wei and Jehad Abubaker
Biomolecules 2021, 11(7), 919; https://doi.org/10.3390/biom11070919 - 22 Jun 2021
Cited by 27 | Viewed by 3975
Abstract
SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) protein is the target for the antiviral drug Remdesivir (RDV). With RDV clinical trials on COVID-19 patients showing a reduced hospitalisation time. During the spread of the virus, the RdRp has developed several mutations, with the most frequent [...] Read more.
SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) protein is the target for the antiviral drug Remdesivir (RDV). With RDV clinical trials on COVID-19 patients showing a reduced hospitalisation time. During the spread of the virus, the RdRp has developed several mutations, with the most frequent being A97V and P323L. The current study sought to investigate whether A97V and P323L mutations influence the binding of RDV to the RdRp of SARS-CoV-2 compared to wild-type (WT). The interaction of RDV with WT-, A97V-, and P323L-RdRp were measured using molecular dynamic (MD) simulations, and the free binding energies were extracted. Results showed that RDV that bound to WT- and A97V-RdRp had a similar dynamic motion and internal residue fluctuations, whereas RDV interaction with P323L-RdRp exhibited a tighter molecular conformation, with a high internal motion near the active site. This was further corroborated with RDV showing a higher binding affinity to P323L-RdRp (−24.1 kcal/mol) in comparison to WT-RdRp (−17.3 kcal/mol). This study provides insight into the potential significance of administering RDV to patients carrying the SARS-CoV-2 P323L-RdRp mutation, which may have a more favourable chance of alleviating the SARS-CoV-2 illness in comparison to WT-RdRp carriers, thereby suggesting further scientific consensus for the usage of Remdesivir as clinical candidate against COVID-19. Full article
(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence)
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14 pages, 33454 KiB  
Article
Computational Study on Selective PDE9 Inhibitors on PDE9-Mg/Mg, PDE9-Zn/Mg, and PDE9-Zn/Zn Systems
by Dakshinamurthy Sivakumar, Sathishkumar Mudedla, Seonghun Jang, Hyunjun Kim, Hyunjin Park, Yonwon Choi, Joongyo Oh and Sangwook Wu
Biomolecules 2021, 11(5), 709; https://doi.org/10.3390/biom11050709 - 10 May 2021
Cited by 7 | Viewed by 2857
Abstract
PDE9 inhibitors have been studied to validate their potential to treat diabetes, neurodegenerative disorders, cardiovascular diseases, and erectile dysfunction. In this report, we have selected highly potent previously reported selective PDE9 inhibitors BAY73-6691R, BAY73-6691S, 28r, 28s, 3r, 3s, PF-0447943, PF-4181366, and 4r to [...] Read more.
PDE9 inhibitors have been studied to validate their potential to treat diabetes, neurodegenerative disorders, cardiovascular diseases, and erectile dysfunction. In this report, we have selected highly potent previously reported selective PDE9 inhibitors BAY73-6691R, BAY73-6691S, 28r, 28s, 3r, 3s, PF-0447943, PF-4181366, and 4r to elucidate the differences in their interaction patterns in the presence of different metal systems such as Zn/Mg, Mg/Mg, and Zn/Zn. The initial complexes were generated by molecular docking followed by molecular dynamics simulation for 100 ns in triplicate for each system to understand the interactions’ stability. The results were carefully analyzed, focusing on the ligands’ non-bonded interactions with PDE9 in different metal systems. Full article
(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence)
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