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In Silico Drug Design and Discovery: Big Data for Small Molecule Design II

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 5356

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Special Issue Editors

Dr. Carmen Cerchia

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Department of Pharmacy, University of Naples “Federico II”, via D. Montesano, 49, 80131 Napoli, Italy
Interests: computer-aided drug design; drug discovery; medicinal chemistry; structure-based drug design; molecular modeling; polypharmacology; data mining
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Antonio Lavecchia

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Department of Pharmacy, “Drug Discovery Lab”, University of Naples “Federico II”, Via D. Montesano 49, 80131 Naples, Italy
Interests: drug discovery; medicinal chemistry; molecular modeling; polypharmacology; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Life sciences heavily rely on data collected in different ways, for example, via experimental work, medical observations, or computer simulations, etc. Advances in novel technologies, such as high-throughput screening and readout, next-generation sequencing, and “-omics” approaches, represent the main drivers of the exponentially increasing amount of data being generated, with a significant among being available in public databases (i.e. ChEMBL, PubChem, PDB).

Taking advantage of this wealth of information is critical to improve decision making in drug discovery projects; for instance, structure–activity relationships (SARs) can be extracted on a large-scale and used to complement chemical optimization efforts.

Therefore, there is a growing demand in computational approaches to exploit such an amount of data along with its complexity, utilizing data mining and visualization techniques, machine/deep learning algorithms, and generative models in the process.

Within this context, this Special Issue aims to showcase recent progresses and current trends in the use of in silico approaches leveraging big data and extracting useful knowledge to support all aspects of drug design and discovery. Topics of interest include, but are not limited to, bio/chemoinformatics, machine learning, deep learning, and generative models. Experimental and theoretical research studies are welcome; multi-disciplinary approaches are particularly encouraged.

We look forward to your contributions.

Dr. Carmen Cerchia
Prof. Dr. Antonio Lavecchia
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

drug discovery
medicinal chemistry
chemoinformatics
bioinformatics
machine learning
deep learning

Related Special Issue

In Silico Drug Design and Discovery: Big Data for Small Molecule Design in Biomolecules (7 articles)

Published Papers (3 papers)

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Research

13 pages, 1433 KiB

Open AccessArticle

Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures

by Yuki Umemori, Koichi Handa, Saki Yoshimura, Michiharu Kageyama and Takeshi Iijima

Biomolecules 2024, 14(5), 535; https://doi.org/10.3390/biom14050535 - 30 Apr 2024

Viewed by 1232

Abstract

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results. Full article

(This article belongs to the Special Issue In Silico Drug Design and Discovery: Big Data for Small Molecule Design II)

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18 pages, 4297 KiB

Open AccessArticle

Structural and Functional Characterization of Lipoxygenases from Diatoms by Bioinformatics and Modelling Studies

by Deborah Giordano, Simone Bonora, Ilenia D’Orsi, Domenico D’Alelio and Angelo Facchiano

Biomolecules 2024, 14(3), 276; https://doi.org/10.3390/biom14030276 - 25 Feb 2024

Viewed by 1670

Abstract

Lipoxygenases make several biological functions in cells, based on the products of the catalyzed reactions. In diatoms, microalgae ubiquitous in aquatic ecosystems, lipoxygenases have been noted for the oxygenation of fatty acids with the production of oxylipins, which are involved in many physiological and pathological processes in marine organisms. The interest in diatoms’ lipoxygenases and oxylipins has increased due to their possible biotechnological applications, ranging from ecology to medicine. We investigated using bioinformatics and molecular docking tools the lipoxygenases of diatoms and the possible interaction with substrates. A large-scale analysis of sequence resources allowed us to retrieve 45 sequences of lipoxygenases from diatoms. We compared and analyzed the sequences by multiple alignments and phylogenetic trees, suggesting the possible clustering in phylogenetic groups. Then, we modelled the 3D structure of representative enzymes from the different groups and investigated in detail the structural and functional properties by docking simulations with possible substrates. The results allowed us to propose a classification of the lipoxygenases from diatoms based on their sequence features, which may be reflected in specific structural differences and possible substrate specificity. Full article

(This article belongs to the Special Issue In Silico Drug Design and Discovery: Big Data for Small Molecule Design II)

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26 pages, 17568 KiB

Open AccessArticle

Characteristic Binding Landscape of Estrogen Receptor-α36 Protein Enhances Promising Cancer Drug Design

by Adeniyi T. Adewumi and Salerwe Mosebi

Biomolecules 2023, 13(12), 1798; https://doi.org/10.3390/biom13121798 - 14 Dec 2023

Viewed by 1557

Abstract

Breast cancer (BC) remains the most common cancer among women worldwide, and estrogen receptor-α expression is a critical diagnostic factor for BC. Estrogen receptor (ER-α36) is a dominant-negative effector of ER-α66-mediated estrogen-responsive gene pathways. ER-α36 is a novel target that mediates the non-genomic estrogen signaling pathway. However, the crystallized structure of ER-α36 remains unavailable for molecular studies. ER-positive and triple-negative BC tumors aggressively resist the FDA-approved drugs; therefore, highly potent structure-based inhibitors with preeminent benefits over toxicity will preferably replace the current BC treatment. Broussoflanol B (BFB), a B. papyrifera bark compound, exhibits potent growth inhibitory activity in ER-negative BC cells by inducing cell cycle arrest. For the first time, we unravel the comparative dynamic events of the enzymes’ structures and the binding mechanisms of BFB when bound to the ER-α36 and ER-α66 ligand-binding domain using an all-atom molecular dynamics simulations approach and MM/PBSA-binding-free energy calculations. The dynamic findings have revealed that ER-α36 and ER-α66 LBD undergo timescale “coiling”, opening and closing conformations favoring the high-affinity BFB-bound ER-α36 (ΔG = −52.57 kcal/mol) compared to the BFB-bound ER-α66 (ΔG = −42.41 kcal/mol). Moreover, the unbound (1.260 Å) and bound ER-α36 (1.182 Å) exhibit the highest flexibilities and atomistic motions relative to the ER-α66 systems. The RMSF (Å) of the unbound ER-α36 and ER-α66 exhibit lesser stabilities than the BFB-bound systems, resulting in higher structural flexibilities and atomistic motions than the bound variants. These findings present a model that describes the mechanisms by which the BFB compound induces downregulation-accompanied cell cycle arrest at the Gap₀ and Gap₁ phases. Full article

(This article belongs to the Special Issue In Silico Drug Design and Discovery: Big Data for Small Molecule Design II)

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