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Cheminformatics in Drug Discovery and Material Design
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Cheminformatics in Drug Discovery and Material Design

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 (20 January 2025) | Viewed by 6510

Special Issue Editor

Dr. Georgia Melagraki

E-Mail Website
Guest Editor
Hellenic Military Academy, P.O. Box 16673 Vari, Greece
Interests: cheminformatics; nanoinformatics; machine learning; in silico drug discovery; in silico toxicology; risk assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cheminformatics is a rapidly advancing interdisciplinary field that plays a crucial role in drug and material discovery and development. The use of computational methods and tools has revolutionized the process of drug discovery by enabling efficient identification of potential drug targets and lead compounds. Cheminformatics tools can predict the properties of a new molecule based on its chemical structure, reducing the need for time-consuming and expensive laboratory experiments. These tools can also help optimize drug potency and reduce toxicity by predicting how a drug interacts with its target in the body. Similarly, cheminformatics can be used for the design of new materials with specific functional properties, such as catalytic activity or mechanical, optical, thermal and electronic properties, as required for particular applications. Cheminformatics has significantly contributed to accelerating the drug discovery and material design processes, ultimately leading to the development of more effective and efficient drugs and materials and reducing the risk of unintended toxicities. As such, it is essential to continue exploring and advancing the field to tackle some of the most significant societal challenges. This Special Issue aims to bring together researchers from various disciplines to present their latest findings and discuss future directions in cheminformatics research.

Specific topics include but are not limited to the following:

  • Development of novel machine learning algorithms for predicting drug activity and toxicity.
  • Application of deep learning techniques to analyze large-scale chemical datasets for drug or materials discovery.
  • Development of new computational tools for material property and function prediction and design, including the enhancement of safety and sustainability.
  • Integration of molecular docking and molecular dynamics simulations for optimizing drug–protein and material–protein (biomolecule) interactions.
  • Application of cheminformatics approaches for the identification of drug repurposing candidates.
  • In silico models and tools for material design for dual use.
  • Development of quantitative structure–activity relationship (QSAR) models for predicting drug and material properties.
  • Use of molecular descriptors and fingerprints for similarity analysis and virtual screening of chemical and material libraries.
  • Database development and FAIRification of data.

We welcome publications in all the above related fields and beyond and hope that this Special Issue will serve as a valuable reference and help stimulate advances in the field.

Dr. Georgia Melagraki
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.

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

  • chemoinformatics
  • nanoinformatics
  • machine learning
  • quantitative structure activity relationship (QSAR)
  • computer-aided drug design (CADD)
  • materials modeling
  • chemical databases

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Published Papers (4 papers)

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Research

20 pages, 2363 KiB  
Article
Graph and Multi-Level Sequence Fusion Learning for Predicting the Molecular Activity of BACE-1 Inhibitors
by Shaohua Zheng, Changwang Zhang, Youjia Chen and Meimei Chen
Int. J. Mol. Sci. 2025, 26(4), 1681; https://doi.org/10.3390/ijms26041681 - 16 Feb 2025
Viewed by 548
Abstract
The development of BACE-1 (β-site amyloid precursor protein cleaving enzyme 1) inhibitors is a crucial focus in exploring early treatments for Alzheimer’s disease (AD). Recently, graph neural networks (GNNs) have demonstrated significant advantages in predicting molecular activity. However, their reliance on graph structures [...] Read more.
The development of BACE-1 (β-site amyloid precursor protein cleaving enzyme 1) inhibitors is a crucial focus in exploring early treatments for Alzheimer’s disease (AD). Recently, graph neural networks (GNNs) have demonstrated significant advantages in predicting molecular activity. However, their reliance on graph structures alone often neglects explicit sequence-level semantic information. To address this limitation, we proposed a Graph and multi-level Sequence Fusion Learning (GSFL) model for predicting the molecular activity of BACE-1 inhibitors. Firstly, molecular graph structures generated from SMILES strings were encoded using GNNs with an atomic-level characteristic attention mechanism. Next, substrings at functional group, ion level, and atomic level substrings were extracted from SMILES strings and encoded using a BiLSTM–Transformer framework equipped with a hierarchical attention mechanism. Finally, these features were fused to predict the activity of BACE-1 inhibitors. A dataset of 1548 compounds with BACE-1 activity measurements was curated from the ChEMBL database. In the classification experiment, the model achieved an accuracy of 0.941 on the training set and 0.877 on the test set. For the test set, it delivered a sensitivity of 0.852, a specificity of 0.894, a MCC of 0.744, an F1-score of 0.872, a PRC of 0.869, and an AUC of 0.915. Compared to traditional computer-aided drug design methods and other machine learning algorithms, the proposed model can effectively improve the accuracy of the molecular activity prediction of BACE-1 inhibitors and has a potential application value. Full article
(This article belongs to the Special Issue Cheminformatics in Drug Discovery and Material Design)
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17 pages, 2679 KiB  
Article
Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists
by Maria Antoniou, Konstantinos D. Papavasileiou, Georgia Melagraki, Francesco Dondero, Iseult Lynch and Antreas Afantitis
Int. J. Mol. Sci. 2024, 25(10), 5216; https://doi.org/10.3390/ijms25105216 - 10 May 2024
Cited by 1 | Viewed by 1185
Abstract
A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various [...] Read more.
A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure–activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform’s graphical user interface (GUI). Full article
(This article belongs to the Special Issue Cheminformatics in Drug Discovery and Material Design)
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31 pages, 15389 KiB  
Article
De Novo Potent Peptide Nucleic Acid Antisense Oligomer Inhibitors Targeting SARS-CoV-2 RNA-Dependent RNA Polymerase via Structure-Guided Drug Design
by Kiran Shehzadi, Mingjia Yu and Jianhua Liang
Int. J. Mol. Sci. 2023, 24(24), 17473; https://doi.org/10.3390/ijms242417473 - 14 Dec 2023
Cited by 6 | Viewed by 2078
Abstract
Global reports of novel SARS-CoV-2 variants and recurrence cases continue despite substantial vaccination campaigns, raising severe concerns about COVID-19. While repurposed drugs offer some treatment options for COVID-19, notably, nucleoside inhibitors like Remdesivir stand out as curative therapies for COVID-19 that are approved [...] Read more.
Global reports of novel SARS-CoV-2 variants and recurrence cases continue despite substantial vaccination campaigns, raising severe concerns about COVID-19. While repurposed drugs offer some treatment options for COVID-19, notably, nucleoside inhibitors like Remdesivir stand out as curative therapies for COVID-19 that are approved by the US Food and Drug Administration (FDA). The emergence of highly contagious SARS-CoV-2 variants underscores the imperative for antiviral drugs adaptable to evolving viral mutations. RNA-dependent RNA polymerase (RdRp) plays a key role in viral genome replication. Currently, inhibiting viral RdRp function remains a pivotal strategy to tackle the notorious virus. Peptide nucleic acid (PNA) therapy shows promise by effectively targeting specific genome regions, reducing viral replication, and inhibiting infection. In our study, we designed PNA antisense oligomers conjugated with cell-penetrating peptides (CPP) aiming to evaluate their antiviral effects against RdRp target using structure-guided drug design, which involves molecular docking simulations, drug likeliness and pharmacokinetic evaluations, molecular dynamics simulations, and computing binding free energy. The in silico analysis predicts that chemically modified PNAs might act as antisense molecules in order to disrupt ribosome assembly at RdRp’s translation start site, and their chemically stable and neutral backbone might enhance sequence-specific RNA binding interaction. Notably, our findings demonstrate that PNA-peptide conjugates might be the most promising inhibitors of SARS-CoV-2 RdRp, with superior binding free energy compared to Remdesivir in the current COVID-19 medication. Specifically, PNA-CPP-1 could bind simultaneously to the active site residues of RdRp protein and sequence-specific RdRp-RNA target in order to control viral replication. Full article
(This article belongs to the Special Issue Cheminformatics in Drug Discovery and Material Design)
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14 pages, 3380 KiB  
Article
LQFM289: Electrochemical and Computational Studies of a New Trimetozine Analogue for Anxiety Treatment
by Jhon K. A. Pereira, André G. C. Costa, Edson S. B. Rodrigues, Isaac Y. L. Macêdo, Marx O. A. Pereira, Ricardo Menegatti, Severino C. B. de Oliveira, Freddy Guimarães, Luciano M. Lião, José R. Sabino and Eric de S. Gil
Int. J. Mol. Sci. 2023, 24(19), 14575; https://doi.org/10.3390/ijms241914575 - 26 Sep 2023
Cited by 1 | Viewed by 1626
Abstract
This study employs electrochemical and Density Functional Theory (DFT) calculation approaches to investigate the potential of a novel analogue of trimetozine (TMZ) antioxidant profile. The correlation between oxidative stress and psychological disorders indicates that antioxidants may be an effective alternative treatment option. Butylatedhydroxytoluene [...] Read more.
This study employs electrochemical and Density Functional Theory (DFT) calculation approaches to investigate the potential of a novel analogue of trimetozine (TMZ) antioxidant profile. The correlation between oxidative stress and psychological disorders indicates that antioxidants may be an effective alternative treatment option. Butylatedhydroxytoluene (BHT) is a synthetic antioxidant widely used in industry. The BHT-TMZ compound derived from molecular hybridization, known as LQFM289, has shown promising results in early trials, and this study aims to elucidate its electrochemical properties to further support its potential as a therapeutic agent. The electrochemical behavior of LQFM289 was investigated using voltammetry and a mechanism for the redox process was proposed based on the compound’s behavior. LQFM289 exhibits two distinct oxidation peaks: the first peak, Ep1a ≈ 0.49, corresponds to the oxidation of the phenolic fraction (BHT), and the second peak, Ep2a ≈ 1.2 V (vs. Ag/AgCl/KClsat), denotes the oxidation of the amino group from morpholine. Electroanalysis was used to identify the redox potentials of the compound, providing insight into its reactivity and stability in different environments. A redox mechanism was proposed based on the resulting peak potentials. The DFT calculation elucidates the electronic structure of LQFM289, resembling the precursors of molecular hybridization (BHT and TMZ), which may also dictate the pharmacophoric performance. Full article
(This article belongs to the Special Issue Cheminformatics in Drug Discovery and Material Design)
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