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Computational Approaches in Drug Design: Novel Methodologies and Applications

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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 (20 May 2025) | Viewed by 9040

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Dr. Serena Vittorio

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Department of Pharmaceutical Sciences, Università degli Studi di Milano, via Mangiagalli 25, 20133 Milano, Italy
Interests: medicinal chemistry; drug discovery; enzyme inhibitors
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Special Issue Information

Dear Colleagues,

Computer-aided drug design (CADD) undoubtedly plays a key role in modern drug discovery. In recent years, this field has witnessed significant progress, mainly driven by the advancement of artificial intelligence (AI) algorithms in drug discovery programs. The growing availability of experimental data has promoted the development of highly accurate AI models with diverse applications such as predicting the pharmacological activity of molecules, ADME/Tox profiling and molecular properties’ prediction. Concurrently, increasing and more accessible computational power has further facilitated the use of conventional in silico drug discovery approaches, enabling the rapid screening of large chemical libraries.

On this ground, this Special Issues aims to present cutting-edge research that leverage computational methods to discover novel bioactive compounds, offering an overview of the current state of the art of this research field. Topics of this Special Issue include the development of new in silico approaches for drug discovery as well as the application of existing methodologies, such as molecular docking, molecular dynamics, pharmacophore modeling, homology modeling, QSAR and data-driven techniques, to drug design. Submissions may include original research or reviews in which CADD methods are developed and/or applied to identify new drugs candidates, optimize new hit compounds, perform structure–activity relationship studies, analyze ligand–target interactions and predict the ADME/Tox profile and the molecular properties of drug-like compounds.

Dr. Serena Vittorio
Guest Editor

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Keywords

drug design
computational approaches
molecular docking
molecular dynamics
pharmacophore modeling
homology modeling
computer aided drug design
ADME/Tox predictions
artificial intelligence

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Published Papers (5 papers)

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Research

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24 pages, 2199 KiB

Open AccessArticle

Design, Synthesis, Anti-Tumor Activity and Molecular Docking Studies of Novel Triphenylphosphine-Containing Formononetin Derivatives

by Hongjuan Cui, Yan Zhao, Wei Li, Huanjie Cui, Jiahong Han and Enbo Cai

Int. J. Mol. Sci. 2025, 26(11), 5280; https://doi.org/10.3390/ijms26115280 (registering DOI) - 30 May 2025

Viewed by 31

Abstract

Formononetin is widely used in anti-tumor research, but its poor water solubility leads to low absorption and poor utilization efficiency in vivo, limiting further development. The triphenylphosphine cation was partially attached to the 7-position hydroxyl group of formononetin to specifically target it into the mitochondria of tumor cells to enhance the anti-tumor effect. Detailed structural characterization via ¹H-NMR and ¹³C-NMR analysis confirmed the physical properties and chemical structures of 21 newly synthesized derivatives. The effects of these derivatives on tumor cells were assessed by in vitro and computational methods. MTT results from four tumor cell lines showed that formononetin derivatives containing triphenylphosphine had stronger anti-tumor activity than formononetin and exhibited more cytotoxic effects in cancer cells than in normal cells. In particular, the final product 2c (IC₅₀ = 12.19 ± 1.52 μM) showed more potent anti-tumor activity against A549 cells. It was also superior to formononetin and 5-FU. To identify the potential biological targets, the core-expressed gene SHMT2 in lung cancer mitochondria was screened using network pharmacology technology, and molecular docking analysis confirmed the stable binding of the end products to the amino acid residues of the core genes through the formation of hydrogen bonds and via other interactions. In addition, molecular docking simulations further confirmed that the end product exhibited excellent stability when bound to SHMT2. These results suggest that triphenylphosphine-containing formononetin derivatives are worthy of further exploration in the search for novel drug candidates for the treatment of cancer. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Design: Novel Methodologies and Applications)

24 pages, 14052 KiB

Open AccessArticle

Identification of DDR1 Inhibitors from Marine Compound Library Based on Pharmacophore Model and Scaffold Hopping

by Honghui Hu, Jiahua Tao and Lianxiang Luo

Int. J. Mol. Sci. 2025, 26(3), 1099; https://doi.org/10.3390/ijms26031099 - 27 Jan 2025

Viewed by 934

Abstract

Ulcerative colitis (UC) is a chronic inflammatory condition that affects the intestines. Research has shown that reducing the activity of DDR1 can help maintain intestinal barrier function in UC, making DDR1 a promising target for treatment. However, the development of DDR1 inhibitors as drugs has been hindered by issues such as toxicity and poor binding stability. As a result, there are currently no DDR1-targeting drugs available for clinical use, highlighting the need for new inhibitors. In a recent study, a dataset of 85 DDR1 inhibitors was analyzed to identify key characteristics for effective inhibition. A pharmacophore model was constructed and validated to screen a library of marine natural products for potential DDR1 inhibitors. Through high-throughput virtual screening and precise docking, 17 promising compounds were identified from a pool of over 52,000 molecules in the marine database. To improve binding affinity and reduce potential toxicity, scaffold hopping was employed to modify the 17 compounds, resulting in the generation of 1070 new compounds. These new compounds were further evaluated through docking and ADMET analysis, leading to the identification of three compounds—39713a, 34346a, and 34419a—with superior predicted activity and drug-like properties compared to the original 17 compounds. Further analysis showed that the binding free energy values of the three candidate compounds were less than −12.200 kcal/mol, which was similar to or better than −12.377 kcal/mol of the known positive compound VU6015929, and the drug-like properties were better than those of the positive compounds. Molecular dynamics simulations were then conducted on these three candidate compounds, confirming their stable interactions with the target protein. In conclusion, compounds 39713a, 34346a, and 34419a show promise as potential DDR1 inhibitors for the treatment of ulcerative colitis. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Design: Novel Methodologies and Applications)

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23 pages, 1089 KiB

Open AccessArticle

Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection

by Letizia Messa, Carolina Testa, Stephana Carelli, Federica Rey, Emanuela Jacchetti, Cristina Cereda, Manuela Teresa Raimondi, Stefano Ceri and Pietro Pinoli

Int. J. Mol. Sci. 2024, 25(17), 9576; https://doi.org/10.3390/ijms25179576 - 4 Sep 2024

Cited by 1 | Viewed by 1294

Abstract

The vast corpus of heterogeneous biomedical data stored in databases, ontologies, and terminologies presents a unique opportunity for drug design. Integrating and fusing these sources is essential to develop data representations that can be analyzed using artificial intelligence methods to generate novel drug candidates or hypotheses. Here, we propose Non-Negative Matrix Tri-Factorization as an invaluable tool for integrating and fusing data, as well as for representation learning. Additionally, we demonstrate how representations learned by Non-Negative Matrix Tri-Factorization can effectively be utilized by traditional artificial intelligence methods. While this approach is domain-agnostic and applicable to any field with vast amounts of structured and semi-structured data, we apply it specifically to computational pharmacology and drug repurposing. This field is poised to benefit significantly from artificial intelligence, particularly in personalized medicine. We conducted extensive experiments to evaluate the performance of the proposed method, yielding exciting results, particularly compared to traditional methods. Novel drug–target predictions have also been validated in the literature, further confirming their validity. Additionally, we tested our method to predict drug synergism, where constructing a classical matrix dataset is challenging. The method demonstrated great flexibility, suggesting its applicability to a wide range of tasks in drug design and discovery. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Design: Novel Methodologies and Applications)

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Review

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15 pages, 1219 KiB

Open AccessReview

In Silico Screening of Therapeutic Targets as a Tool to Optimize the Development of Drugs and Nutraceuticals in the Treatment of Diabetes mellitus: A Systematic Review

by Ana Francisca T. Gomes, Wendjilla F. de Medeiros, Isaiane Medeiros, Grasiela Piuvezam, Juliana Kelly da Silva-Maia, Ingrid Wilza L. Bezerra and Ana Heloneida de A. Morais

Int. J. Mol. Sci. 2024, 25(17), 9213; https://doi.org/10.3390/ijms25179213 - 25 Aug 2024

Cited by 3 | Viewed by 2773

Abstract

The Target-Based Virtual Screening approach is widely employed in drug development, with docking or molecular dynamics techniques commonly utilized for this purpose. This systematic review (SR) aimed to identify in silico therapeutic targets for treating Diabetes mellitus (DM) and answer the question: What therapeutic targets have been used in in silico analyses for the treatment of DM? The SR was developed following the guidelines of the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis, in accordance with the protocol registered in PROSPERO (CRD42022353808). Studies that met the PECo strategy (Problem, Exposure, Context) were included using the following databases: Medline (PubMed), Web of Science, Scopus, Embase, ScienceDirect, and Virtual Health Library. A total of 20 articles were included, which not only identified therapeutic targets in silico but also conducted in vivo analyses to validate the obtained results. The therapeutic targets most frequently indicated in in silico studies were GLUT4, DPP-IV, and PPARγ. In conclusion, a diversity of targets for the treatment of DM was verified through both in silico and in vivo reassessment. This contributes to the discovery of potential new allies for the treatment of DM. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Design: Novel Methodologies and Applications)

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29 pages, 14051 KiB

Open AccessReview

The Structure–Antiproliferative Activity Relationship of Pyridine Derivatives

by Ana-Laura Villa-Reyna, Martin Perez-Velazquez, Mayra Lizett González-Félix, Juan-Carlos Gálvez-Ruiz, Dulce María Gonzalez-Mosquera, Dora Valencia, Manuel G. Ballesteros-Monreal, Milagros Aguilar-Martínez and Mario-Alberto Leyva-Peralta

Int. J. Mol. Sci. 2024, 25(14), 7640; https://doi.org/10.3390/ijms25147640 - 11 Jul 2024

Cited by 4 | Viewed by 2258

Abstract

Pyridine, a compound with a heterocyclic structure, is a key player in medicinal chemistry and drug design. It is widely used as a framework for the design of biologically active molecules and is the second most common heterocycle in FDA-approved drugs. Pyridine is known for its diverse biological activity, including antituberculosis, antitumor, anticoagulant, antiviral, antimalarial, antileishmania, anti-inflammatory, anti-Alzheimer’s, antitrypanosomal, antimalarial, vasodilatory, antioxidant, antimicrobial, and antiproliferative effects. This review, spanning from 2022 to 2012, involved the meticulous identification of pyridine derivatives with antiproliferative activity, as indicated by their minimum inhibitory concentration values (IC50) against various cancerous cell lines. The aim was to determine the most favorable structural characteristics for their antiproliferative activity. Using computer programs, we constructed and calculated the molecular descriptors and analyzed the electrostatic potential maps of the selected pyridine derivatives. The study found that the presence and positions of the -OMe, -OH, -C=O, and NH2 groups in the pyridine derivatives enhanced their antiproliferative activity over the cancerous cellular lines studied. Conversely, pyridine derivatives with halogen atoms or bulky groups in their structures exhibited lower antiproliferative activity. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Design: Novel Methodologies and Applications)

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