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Advanced In Silico Methods and Digital Platforms for Rational Drug Design and Predictive Toxicology

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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: 10 July 2024 | Viewed by 2680

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Special Issue Editors

Prof. Dr. Orazio Nicolotti

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Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
Interests: computer-aided drug design; predictive toxicology; chemoinformatics; artificial intelligence; digital platforms
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Dr. Daniela Trisciuzzi

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Department of Pharmacy, Pharmaceutical Sciences Università degli Studi di Bari “Aldo Moro”, 70125 Bari, Italy
Interests: peptide–protein interactions; protein–protein interactions; drug repurposing; binding site mapping; machine learning; classification models; molecular docking; predictive toxicology
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Dr. Nicola Gambacorta

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Guest Editor

Division of Medical Genetics, IRCSS Foundation “Casa Sollievo dalla Sofferenza” San Giovanni Rotondo, 71013 Foggia, Italy
Interests: artificial intelligence; machine learning; cheminformatics; molecular docking; molecular dynamics; 3D-pharmacophore modeling; drug repurposing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the unprecedented advancements in computer-assisted drug discovery and predictive toxicology have allowed powerful and reliable models to be built from large amounts of data. In this respect, pharmaceutical companies and academia have made remarkable investments to generate customizable tools, services, and technologies that are capable of reaching impressive standards.

For this Special Issue, we call on medicinal chemists and toxicologists to share their experiences on the design of novel methods and the implementation of digital platforms, in order to provide practical answers to challenging issues related, but not limited to, drug repurposing, target fishing, bioactivity prediction, de novo design, molecular docking, molecular dynamics, homology modeling, virtual screening, QSAR, and alternative methods for the prediction of toxicological endpoints.

In light of this, we are delighted to invite international scientists to contribute to this Special Issue with research articles, mini-perspectives, and brief articles describing recent methods and platforms for drug discovery and predictive toxicology.

Feasible topics of interest can include:

Drug repurposing;
Target fishing;
Bioactivity prediction;
de novo design;
Chemoinformatics;
Molecular docking;
Molecular dynamics;
Homology modeling;
Virtual screening;
QSAR and read across;
Alternative methods.

Prof. Dr. Orazio Nicolotti
Dr. Daniela Trisciuzzi
Dr. Nicola Gambacorta
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

computer-aided drug design
predictive toxicology
chemoinformatics
artificial intelligence
explainable artificial intelligence
machine learning
deep learning
digital platforms

Published Papers (3 papers)

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Research

34 pages, 25925 KiB

Open AccessArticle

Exploring the Anticancer Potential of Semisynthetic Derivatives of 7α-Acetoxy-6β-hydroxyroyleanone from Plectranthus sp.: An In Silico Approach

by Anna Merecz-Sadowska, Vera M. S. Isca, Przemysław Sitarek, Tomasz Kowalczyk, Magdalena Małecka, Karolina Zajdel, Hanna Zielińska-Bliźniewska, Mariusz Jęcek, Patricia Rijo and Radosław Zajdel

Int. J. Mol. Sci. 2024, 25(8), 4529; https://doi.org/10.3390/ijms25084529 - 20 Apr 2024

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Abstract

The diterpene 7α-acetoxy-6β-hydroxyroyleanone isolated from Plectranthus grandidentatus demonstrates promising antibacterial, anti-inflammatory and anticancer properties. However, its bioactivity may be enhanced via strategic structural modifications of such natural products through semisynthesis. The anticancer potential of 7α-acetoxy-6β-hydroxyroyleanone and five derivatives was analyzed in silico via the prediction of chemicals absorption, distribution, metabolism, excretion, and toxicity (ADMET), quantum mechanical calculations, molecular docking and molecular dynamic simulation. The protein targets included regulators of apoptosis and cell proliferation. Additionally, network pharmacology was used to identify potential targets and signaling pathways. Derivatives 7α-acetoxy-6β-hydroxy-12-O-(2-fluoryl)royleanone and 7α-acetoxy-6β-(4-fluoro)benzoxy-12-O-(4-fluoro)benzoylroyleanone achieved high predicted binding affinities towards their respective protein panels, with stable molecular dynamics trajectories. Both compounds demonstrated favorable ADMET parameters and toxicity profiles. Their stability and reactivity were confirmed via geometry optimization. Network analysis revealed their involvement in cancer-related pathways. Our findings justify the inclusion of 7α-acetoxy-6β-hydroxy-12-O-(2-fluoryl)royleanone and 7α-acetoxy-6β-(4-fluoro)benzoxy-12-O-(4-fluoro)benzoylroyleanone in in vitro analyses as prospective anticancer agents. Our binding mode analysis and stability simulations indicate their potential as selective inhibitors. The data will guide studies into their structure optimization, enhancing efficacy and drug-likeness. Full article

(This article belongs to the Special Issue Advanced In Silico Methods and Digital Platforms for Rational Drug Design and Predictive Toxicology)

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11 pages, 2526 KiB

Open AccessArticle

Targeting Cathepsin L in Cancer Management: Leveraging Machine Learning, Structure-Based Virtual Screening, and Molecular Dynamics Studies

by Abdulraheem Ali Almalki, Alaa Shafie, Ali Hazazi, Hamsa Jameel Banjer, Maha M. Bakhuraysah, Sarah Abdullah Almaghrabi, Ahad Amer Alsaiari, Fouzeyyah Ali Alsaeedi, Amal Adnan Ashour, Afaf Alharthi, Nahed S. Alharthi and Farah Anjum

Int. J. Mol. Sci. 2023, 24(24), 17208; https://doi.org/10.3390/ijms242417208 - 07 Dec 2023

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Abstract

Cathepsin L (CTSL) expression is dysregulated in a variety of cancers. Extensive empirical evidence indicates their direct participation in cancer growth, angiogenic processes, metastatic dissemination, and the development of treatment resistance. Currently, no natural CTSL inhibitors are approved for clinical use. Consequently, the development of novel CTSL inhibition strategies is an urgent necessity. In this study, a combined machine learning (ML) and structure-based virtual screening strategy was employed to identify potential natural CTSL inhibitors. The random forest ML model was trained on IC₅₀ values. The accuracy of the trained model was over 90%. Furthermore, we used this ML model to screen the Biopurify and Targetmol natural compound libraries, yielding 149 hits with prediction scores >0.6. These hits were subsequently selected for virtual screening using a structure-based approach, yielding 13 hits with higher binding affinity compared to the positive control (AZ12878478). Two of these hits, ZINC4097985 and ZINC4098355, have been shown to strongly bind CTSL proteins. In addition to drug-like properties, both compounds demonstrated high affinity, ligand efficiency, and specificity for the CTSL binding pocket. Furthermore, in molecular dynamics simulations spanning 200 ns, these compounds formed stable protein-ligand complexes. ZINC4097985 and ZINC4098355 can be considered promising candidates for CTSL inhibition after experimental validation, with the potential to provide therapeutic benefits in cancer management. Full article

(This article belongs to the Special Issue Advanced In Silico Methods and Digital Platforms for Rational Drug Design and Predictive Toxicology)

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12 pages, 2046 KiB

Open AccessArticle

MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database

by Angelica Mazzolari, Pietro Perazzoni, Emanuela Sabato, Filippo Lunghini, Andrea R. Beccari, Giulio Vistoli and Alessandro Pedretti

Int. J. Mol. Sci. 2023, 24(13), 11064; https://doi.org/10.3390/ijms241311064 - 04 Jul 2023

Cited by 2 | Viewed by 1147

Abstract

The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program. Full article

(This article belongs to the Special Issue Advanced In Silico Methods and Digital Platforms for Rational Drug Design and Predictive Toxicology)

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