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In Silico Approaches to Drug Design and Discovery
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In Silico Approaches to Drug Design and Discovery

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: 20 April 2026 | Viewed by 2842

Special Issue Editor

Prof. Dr. Alejandro Speck-Planche

E-Mail Website
Guest Editor
LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
Interests: multi-target drug discovery; chemoinformatics; QSAR-based approaches; virtual screening; multi-scale de novo drug design; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In silico approaches continue to positively impact drug discovery, accelerating the identification and/or rational design of efficacious and safer therapeutics. This Special Issue invites high-quality contributions that explore computational strategies within the drug design and discovery realm, including (but not limited to) molecular modeling, traditional ligand- and structure-based drug design methods, chemoinformatics, QSAR/QSPR, machine learning, virtual screening, and computational de novo design, amongst others. We welcome original research, reviews, and perspective articles that advance computational strategies across diverse therapeutic areas (infectious diseases, cancers, neurological disorders, cardiovascular medical conditions, and many others). We are particularly interested in studies that highlight the potentialities and evolution of in silico approaches in the context of small-molecule drug discovery, biosequence-based bioactive molecules (peptides, aptamers, and microRNAs), and nanomedicine research. This Issue aims to highlight the key role of in silico approaches in shaping the next generation of therapeutics—we look forward to your contributions.

Prof. Dr. Alejandro Speck-Planche
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 250 words) can be sent to the Editorial Office for assessment.

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

  • chemoinformatics
  • computational de novo design and generative AI methods
  • machine learning
  • deep learning
  • molecular modeling
  • pharmacophore modeling
  • molecular docking
  • molecular dynamics
  • QSAR/QSPR
  • virtual screening

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Published Papers (3 papers)

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Research

22 pages, 826 KB  
Article
Computational Phenotypic Drug Discovery for Anticancer Chemotherapy: PTML Modeling of Multi-Cell Inhibitors of Colorectal Cancer Cell Lines
by Alejandro Speck-Planche and M. Natália D. S. Cordeiro
Int. J. Mol. Sci. 2025, 26(23), 11453; https://doi.org/10.3390/ijms262311453 - 26 Nov 2025
Viewed by 302
Abstract
Colorectal cancer is one of the most dangerous neoplastic diseases in terms of both mortality and incidence. Thus, anti-colorectal cancer agents are urgently needed. Computational approaches have great potential to accelerate the phenotypic discovery of versatile anticancer agents. Here, by combining perturbation-theory machine [...] Read more.
Colorectal cancer is one of the most dangerous neoplastic diseases in terms of both mortality and incidence. Thus, anti-colorectal cancer agents are urgently needed. Computational approaches have great potential to accelerate the phenotypic discovery of versatile anticancer agents. Here, by combining perturbation-theory machine learning (PTML) modeling with the fragment-based topological design (FBTD) approach, we provide key computational evidence on the computer-aided de novo design and prediction of new molecules virtually exhibiting multi-cell inhibitory activity against different colorectal cancer cell lines. The PTML model created in this study achieved sensitivity and specificity values exceeding 80% in training and test sets. The FBTD approach was employed to physicochemically and structurally interpret the PTML model. These interpretations enabled the rational design of six new drug-like molecules, which were predicted as active against multiple colorectal cancer cell lines by both our PTML model and a CLC-Pred 2.0 webserver, with the latter being a well-established virtual screening tool for early anticancer discovery. This work confirms the potential of the joint use of PTML and FBTD as a unified computational methodology for early phenotypic anticancer drug discovery. Full article
(This article belongs to the Special Issue In Silico Approaches to Drug Design and Discovery)
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21 pages, 3661 KB  
Article
Virtual Screening of Cathelicidin-Derived Anticancer Peptides and Validation of Their Production in the Probiotic Limosilactobacillus fermentum KUB-D18 Using Genome-Scale Metabolic Modeling and Experimental Approaches
by Vichugorn Wattayagorn, Taratorn Mansuwan, Krittapas Angkanawin, Chakkapan Sapkaew, Chomdao Sinthuvanich, Nisit Watthanasakphuban and Pramote Chumnanpuen
Int. J. Mol. Sci. 2025, 26(20), 10077; https://doi.org/10.3390/ijms262010077 - 16 Oct 2025
Viewed by 717
Abstract
The development of anticancer peptides (ACPs) has emerged as a promising strategy in targeted cancer therapy due to their high specificity and therapeutic potential. Cathelicidin-derived antimicrobial peptides represent a particularly attractive class of ACPs, yet systematic evaluation of their anticancer activity remains limited. [...] Read more.
The development of anticancer peptides (ACPs) has emerged as a promising strategy in targeted cancer therapy due to their high specificity and therapeutic potential. Cathelicidin-derived antimicrobial peptides represent a particularly attractive class of ACPs, yet systematic evaluation of their anticancer activity remains limited. In this study, we conducted virtual screening of eight cathelicidin-derived peptides (AL-38, LL-37, RK-31, KS-30, KR-20, FK-16, FK-13, and KR-12) to assess their potential against colon cancer. Among these, LL-37 and FK-16 were identified as the most promising candidates, with LL-37 exhibiting the strongest inhibitory effects on both non-metastatic (HT-29) and metastatic (SW-620) colon cancer cell lines in vitro. To overcome challenges associated with peptide stability and delivery, we employed the probiotic lactic acid bacterium Limosilactobacillus fermentum KUB-D18 as both a biosynthetic platform and delivery vehicle. A genome-scale metabolic model (GEM), iTM505, was reconstructed to predict the strain’s biosynthetic capacity for ACP production. Model simulations identified trehalose, sucrose, maltose, and cellobiose as optimal carbon sources supporting both high peptide yield and biomass accumulation, which was subsequently confirmed experimentally. Notably, L. fermentum expressing LL-37 achieved a growth rate of 2.16 gDW/L, closely matching the model prediction of 1.93 gDW/L (accuracy 89.69%), while the measured LL-37 concentration (26.96 ± 0.08 µM) aligned with predictions at 90.65% accuracy. The strong concordance between in silico predictions and experimental outcomes underscore the utility of GEM-guided metabolic engineering for optimizing peptide biosynthesis. This integrative approach—combining virtual screening, genome-scale modeling, and experimental validation—provides a robust framework for accelerating ACP discovery. Moreover, our findings highlight the potential of probiotic-based systems as effective delivery platforms for anticancer peptides, offering new avenues for the rational design and production of peptide therapeutics. Full article
(This article belongs to the Special Issue In Silico Approaches to Drug Design and Discovery)
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18 pages, 1768 KB  
Article
In Silico Assessment of Potential Geroprotectors: From Separate Endpoints to Complex Pharmacotherapeutic Effects
by Leonid Stolbov, Anastasia Rudik, Alexey Lagunin, Dmitry Druzhilovskiy, Dmitry Filimonov and Vladimir Poroikov
Int. J. Mol. Sci. 2025, 26(18), 8858; https://doi.org/10.3390/ijms26188858 - 11 Sep 2025
Cited by 1 | Viewed by 1507
Abstract
This study presents an approach for the in silico assessment of potential geroprotectors that target the multifaceted mechanisms of aging, implemented in the PASS GERO web application. This work is timely given the societal impact of aging—the primary risk factor for major chronic [...] Read more.
This study presents an approach for the in silico assessment of potential geroprotectors that target the multifaceted mechanisms of aging, implemented in the PASS GERO web application. This work is timely given the societal impact of aging—the primary risk factor for major chronic diseases. The urgent need to extend healthspan—the period of life spent in good health—motivates the search for compounds that modulate fundamental aging mechanisms. The model estimates the probabilities of 117 aging-related biological activities with high predictive accuracy, achieving an average Invariant Accuracy of Prediction (IAP) of 0.967 under cross-validation. Validation using known geroprotectors (rapamycin, metformin, and resveratrol) demonstrated strong concordance between predicted activities and documented molecular mechanisms of action. For instance, the model correctly predicted rapamycin’s inhibition of mTOR and metformin’s activation of AMPK. The PASS GERO web application provides a systematic strategy to prioritize novel compound candidates for experimental evaluation in anti-aging research. We discuss challenges including the chemical diversity of the training data, the need for validated biomarkers, and the limitations of translating computational predictions into clinical outcomes, positioning the tool as robust application for activity profiling in discovery workflows. Full article
(This article belongs to the Special Issue In Silico Approaches to Drug Design and Discovery)
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