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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (18 August 2023) | Viewed by 11814

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Prof. Dr. Yaron Ilan

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Department of Medicine, Hadassah Medical Center, Faculty of Medicine, Hebrew University, Jerusalem 12000, Israel
Interests: computer-based drug; therapeutic regimens
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer-aided drug design and computer-based therapeutic regimens are computational techniques and artificial intelligence-based systems aimed at assisting the drug discovery process and improving the clinical response to drugs. Computer-aided drug design comprises computational chemistry, molecular modeling, and rational drug design. Artificial intelligence platforms aim to improve the adherence and effectiveness of drugs, thus improving the clinical response of patients using personalized variables. The present Special Issue aims to provide an insight into some of the new developments in this field, highlighting the practicality of these newly developed systems for patient care.

Prof. Dr. Yaron Ilan
Guest Editor

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

computer-aided drug design
artificial intelligence
system biology
complex systems

Published Papers (7 papers)

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Editorial

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3 pages, 170 KiB

Open AccessEditorial

Special Issue “Computer-Aided Drug Discovery and Treatment”

by Yaron Ilan

Int. J. Mol. Sci. 2024, 25(5), 2683; https://doi.org/10.3390/ijms25052683 - 26 Feb 2024

Viewed by 564

Abstract

This Special Issue aims to highlight some of the latest developments in drug discovery [...] Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery and Treatment)

Research

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18 pages, 3672 KiB

Open AccessArticle

Structure-Based In Silico Screening of Marine Phlorotannins for Potential Walrus Calicivirus Inhibitor

by Nalae Kang, Eun-A Kim, Seong-Yeong Heo and Soo-Jin Heo

Int. J. Mol. Sci. 2023, 24(21), 15774; https://doi.org/10.3390/ijms242115774 - 30 Oct 2023

Cited by 2 | Viewed by 874

Abstract

A new calicivirus isolated from a walrus was reported in 2004. Since unknown marine mammalian zoonotic viruses could pose great risks to human health, this study aimed to develop therapeutic countermeasures to quell any potential outbreak of a pandemic caused by this virus. We first generated a 3D model of the walrus calicivirus capsid protein and identified compounds from marine natural products, especially phlorotannins, as potential walrus calicivirus inhibitors. A 3D model of the target protein was generated using homology modeling based on two publicly available template sequences. The sequence of the capsid protein exhibited 31.3% identity and 42.7% similarity with the reference templates. The accuracy and reliability of the predicted residues were validated via Ramachandran plotting. Molecular docking simulations were performed between the capsid protein 3D model and 17 phlorotannins. Among them, five phlorotannins demonstrated markedly stable docking profiles; in particular, 2,7-phloroglucinol-6,6-bieckol showed favorable structural integrity and stability during molecular dynamics simulations. The results indicate that the phlorotannins are promising walrus calicivirus inhibitors. Overall, the study findings showcase the rapid turnaround of in silico-based drug discovery approaches, providing useful insights for developing potential therapies against novel pathogenic viruses, especially when the 3D structures of the viruses remain experimentally unknown. Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery and Treatment)

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27 pages, 7020 KiB

Open AccessArticle

Pharmacoinformatic Investigation of Silymarin as a Potential Inhibitor against Nemopilema nomurai Jellyfish Metalloproteinase Toxin-like Protein

by Ravi Deva Asirvatham, Du Hyeon Hwang, Ramachandran Loganathan Mohan Prakash, Changkeun Kang and Euikyung Kim

Int. J. Mol. Sci. 2023, 24(10), 8972; https://doi.org/10.3390/ijms24108972 - 18 May 2023

Cited by 1 | Viewed by 1777

Abstract

Jellyfish stings pose a major threat to swimmers and fishermen worldwide. These creatures have explosive cells containing one large secretory organelle called a nematocyst in their tentacles, which contains venom used to immobilize prey. Nemopilema nomurai, a venomous jellyfish belonging to the phylum Cnidaria, produces venom (NnV) comprising various toxins known for their lethal effects on many organisms. Of these toxins, metalloproteinases (which belong to the toxic protease family) play a significant role in local symptoms such as dermatitis and anaphylaxis, as well as systemic reactions such as blood coagulation, disseminated intravascular coagulation, tissue injury, and hemorrhage. Hence, a potential metalloproteinase inhibitor (MPI) could be a promising candidate for reducing the effects of venom toxicity. For this study, we retrieved the Nemopilema nomurai venom metalloproteinase sequence (NnV-MPs) from transcriptome data and modeled its three-dimensional structure using AlphaFold2 in a Google Colab notebook. We employed a pharmacoinformatics approach to screen 39 flavonoids and identify the most potent inhibitor against NnV-MP. Previous studies have demonstrated the efficacy of flavonoids against other animal venoms. Based on our analysis, Silymarin emerged as the top inhibitor through ADMET, docking, and molecular dynamics analyses. In silico simulations provide detailed information on the toxin and ligand binding affinity. Our results demonstrate that Silymarin’s strong inhibitory effect on NnV-MP is driven by hydrophobic affinity and optimal hydrogen bonding. These findings suggest that Silymarin could serve as an effective inhibitor of NnV-MP, potentially reducing the toxicity associated with jellyfish envenomation. Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery and Treatment)

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9 pages, 2036 KiB

Open AccessCommunication

Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy

by Vasilios Tanos, Marios Neofytou, Panayiotis Tanos, Constantinos S. Pattichis and Marios S. Pattichis

Int. J. Mol. Sci. 2022, 23(21), 12782; https://doi.org/10.3390/ijms232112782 - 24 Oct 2022

Cited by 3 | Viewed by 1231

Abstract

This review of our experience in computer-assisted tissue image analysis (CATIA) research shows that significant information can be extracted and used to diagnose and distinguish normal from abnormal endometrium. CATIA enabled the evaluation and differentiation between the benign and malignant endometrium during diagnostic hysteroscopy. The efficacy of texture analysis in the endometrium image during hysteroscopy was examined in 40 women, where 209 normal and 209 abnormal regions of interest (ROIs) were extracted. There was a significant difference between normal and abnormal endometrium for the statistical features (SF) features mean, variance, median, energy and entropy; for the spatial grey-level difference matrix (SGLDM) features contrast, correlation, variance, homogeneity and entropy; and for the gray-level difference statistics (GLDS) features homogeneity, contrast, energy, entropy and mean. We further evaluated 52 hysteroscopic images of 258 normal and 258 abnormal endometrium ROIs, and tissue diagnosis was verified by histopathology after biopsy. The YCrCb color system with SF, SGLDM and GLDS color texture features based on support vector machine (SVM) modeling correctly classified 81% of the cases with a sensitivity and a specificity of 78% and 81%, respectively, for normal and hyperplastic endometrium. New technical and computational advances may improve optical biopsy accuracy and assist in the precision of lesion excision during hysteroscopy. The exchange of knowledge, collaboration, identification of tasks and CATIA method selection strategy will further improve computer-aided diagnosis implementation in the daily practice of hysteroscopy. Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery and Treatment)

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23 pages, 5640 KiB

Open AccessArticle

Targeting the SARS-CoV-2 HR1 with Small Molecules as Inhibitors of the Fusion Process

by Davide Gentile, Alessandro Coco, Vincenzo Patamia, Chiara Zagni, Giuseppe Floresta and Antonio Rescifina

Int. J. Mol. Sci. 2022, 23(17), 10067; https://doi.org/10.3390/ijms231710067 - 3 Sep 2022

Cited by 11 | Viewed by 1913

Abstract

The rapid and global propagation of the novel human coronavirus that causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has produced an immediate urgency to discover promising targets for the treatment of this virus. In this paper, we studied the spike protein S2 domain of SARS-CoV-2 as it is the most conserved component and controls the crucial fusion process of SARS-CoV-2 as a target for different databases of small organic compounds. Our in silico methodology, based on pharmacophore modeling, docking simulation and molecular dynamics simulations, was first validated with ADS-J1, a potent small-molecule HIV fusion inhibitor that has already proved effective in binding the HR1 domain and inhibiting the fusion core of SARS-CoV-1. It then focused on finding novel small molecules and new peptides as fusion inhibitors. Our methodology identified several small molecules and peptides as potential inhibitors of the fusion process. Among these, NF 023 hydrate (MolPort-006-822-583) is one of the best-scored compounds. Other compounds of interest are ZINC00097961973, Salvianolic acid, Thalassiolin A and marine_160925_88_2. Two interesting active peptides were also identified: AP00094 (Temporin A) and AVP1227 (GBVA5). The inhibition of the spike protein of SARS-CoV-2 is a valid target to inhibit the virus entry in human cells. The discussed compounds reported in this paper led to encouraging results for future in vitro tests against SARS-CoV-2. Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery and Treatment)

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20 pages, 17271 KiB

Open AccessArticle

Computer-Aided and AILDE Approaches to Design Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors

by Juan Shi, Shuang Gao, Jia-Yu Wang, Tong Ye, Ming-Li Yue, Ying Fu and Fei Ye

Int. J. Mol. Sci. 2022, 23(14), 7822; https://doi.org/10.3390/ijms23147822 - 15 Jul 2022

Cited by 7 | Viewed by 1867

Abstract

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a pivotal enzyme in tocopherol and plastoquinone synthesis and a potential target for novel herbicides. Thirty-five pyridine derivatives were selected to establish a Topomer comparative molecular field analysis (Topomer CoMFA) model to obtain correlation information between HPPD inhibitory activity and the molecular structure. A credible and predictive Topomer CoMFA model was established by “split in two R-groups” cutting methods and fragment combinations (q² = 0.703, r² = 0.957, ONC = 6). The established model was used to screen out more active compounds and was optimized through the auto in silico ligand directing evolution (AILDE) platform to obtain potential HPPD inhibitors. Twenty-two new compounds with theoretically good HPPD inhibition were obtained by combining the high-activity contribution substituents in the existing molecules with the R-group search via Topomer search. Molecular docking results revealed that most of the 22 fresh compounds could form stable π-π interactions. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction and drug-like properties made 9 compounds potential HPPD inhibitors. Molecular dynamics simulation indicated that Compounds Y12 and Y14 showed good root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values and stability. According to the AILDE online verification, 5 new compounds with potential HPPD inhibition were discovered as HPPD inhibitor candidates. This study provides beneficial insights for subsequent HPPD inhibitor design. Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery and Treatment)

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Review

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40 pages, 3280 KiB

Open AccessReview

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

by Stefanos Tsimenidis, Eleni Vrochidou and George A. Papakostas

Int. J. Mol. Sci. 2022, 23(20), 12272; https://doi.org/10.3390/ijms232012272 - 14 Oct 2022

Cited by 8 | Viewed by 2732

Abstract

Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data. Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery and Treatment)

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