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Celebrating the 2024 Nobel Prize in Chemistry: Advancements in Artificial...
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Celebrating the 2024 Nobel Prize in Chemistry: Advancements in Artificial Intelligence and Machine Learning for Cancer Therapeutics

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1425

Special Issue Editors

Prof. Dr. Athanasios G. Papavassiliou

E-Mail Website
Guest Editor
Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece
Interests: molecular medicine; cancer mechanobiology; regulation of gene expression; transcription factors in health and disease; epigenetics; signal transduction; mechanotransduction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Le Zhang

E-Mail Website
Guest Editor
Department of Computer Science, Sichuan University, Chengdu, China
Interests: bioinformatics; numerical analysis; high-performance computing and data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to announce a Special Issue focusing on the groundbreaking advancements in artificial intelligence (AI) and machine learning (ML) in the field of cancer therapeutics, inspired by the 2024 Nobel Prize in Chemistry awarded to David Baker, Demis Hassabis, and John M. Jumper. Their pioneering work in computational protein design and protein structure prediction has opened new avenues for developing innovative therapies for cancer.

This Special Issue aims to explore the transformative potential of AI and ML in the discovery and development of anti-cancer drugs. We invite contributions that encompass a wide range of topics, including the following:

  1. AI-Driven Drug Discovery: Investigating how AI technologies can streamline the identification of novel anti-cancer compounds and optimize drug design processes.
  2. Protein Structure Prediction: Highlighting advancements in predicting protein structures using ML models, particularly in relation to cancer targets.
  3. Personalized Medicine: Exploring the integration of AI in tailoring cancer treatments based on individual patient profiles and tumor characteristics.
  4. Novel Therapeutic Strategies: Discussing the development of innovative therapies derived from AI-driven insights into cancer biology and treatment mechanisms.
  5. Clinical Applications of AI: Presenting real-world applications of AI and ML in clinical settings, demonstrating their impact on patient outcomes and treatment efficacy.

By consolidating the latest research and clinical findings, this Special Issue aims to enhance understanding of the role of AI and ML in cancer therapeutics and to foster collaborations among researchers, clinicians, and industry experts.

We look forward to your valuable contributions that celebrate this significant milestone in cancer research and therapeutics. Thank you for participating in this important dialogue, and we are eager to receive your submissions.

Prof. Dr. Athanasios G. Papavassiliou
Prof. Dr. Le Zhang
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 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. Cancers is an international peer-reviewed open access semimonthly 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 2900 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

  • AI in cancer therapeutics
  • machine learning for drug discovery
  • protein structure prediction
  • personalized cancer treatment
  • clinical AI applications

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Editorial

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3 pages, 163 KiB  
Editorial
Fusing Artificial Intelligence and Machine Learning for Anti-Cancer Drug Discovery
by Christos Adamopoulos, Kostas A. Papavassiliou and Athanasios G. Papavassiliou
Cancers 2024, 16(20), 3522; https://doi.org/10.3390/cancers16203522 - 17 Oct 2024
Viewed by 860
Abstract
The integration of artificial intelligence (AI) and machine learning (ML) in modern oncology is rapidly transforming cancer drug discovery and development [...] Full article
(This article belongs to the Special Issue Celebrating the 2024 Nobel Prize in Chemistry: Advancements in Artificial Intelligence and Machine Learning for Cancer Therapeutics)

Research

Jump to: Editorial

26 pages, 4858 KiB  
Article
In Silico Design of Peptide Inhibitors Targeting HER2 for Lung Cancer Therapy
by Heba Ahmed Alkhatabi and Hisham N. Alatyb
Cancers 2024, 16(23), 3979; https://doi.org/10.3390/cancers16233979 - 27 Nov 2024
Viewed by 320
Abstract
Background/Objectives: Human epidermal growth factor receptor 2 (HER2) is overexpressed in several malignancies, such as breast, gastric, ovarian, and lung cancers, where it promotes aggressive tumor proliferation and unfavorable prognosis. Targeting HER2 has thus emerged as a crucial therapeutic strategy, particularly for HER2-positive [...] Read more.
Background/Objectives: Human epidermal growth factor receptor 2 (HER2) is overexpressed in several malignancies, such as breast, gastric, ovarian, and lung cancers, where it promotes aggressive tumor proliferation and unfavorable prognosis. Targeting HER2 has thus emerged as a crucial therapeutic strategy, particularly for HER2-positive malignancies. The present study focusses on the design and optimization of peptide inhibitors targeting HER2, utilizing machine learning to identify and enhance peptide candidates with elevated binding affinities. The aim is to provide novel therapeutic options for malignancies linked to HER2 overexpression. Methods: This study started with the extraction and structural examination of the HER2 protein, succeeded by designing the peptide sequences derived from essential interaction residues. A machine learning technique (XGBRegressor model) was employed to predict binding affinities, identifying the top 20 peptide possibilities. The candidates underwent further screening via the FreeSASA methodology and binding free energy calculations, resulting in the selection of four primary candidates (pep-17, pep-7, pep-2, and pep-15). Density functional theory (DFT) calculations were utilized to evaluate molecular and reactivity characteristics, while molecular dynamics simulations were performed to investigate inhibitory mechanisms and selectivity effects. Advanced computational methods, such as QM/MM simulations, offered more understanding of peptide–protein interactions. Results: Among the four principal peptides, pep-7 exhibited the most elevated DFT values (−3386.93 kcal/mol) and the maximum dipole moment (10,761.58 Debye), whereas pep-17 had the lowest DFT value (−5788.49 kcal/mol) and the minimal dipole moment (2654.25 Debye). Molecular dynamics simulations indicated that pep-7 had a steady binding free energy of −12.88 kcal/mol and consistently bound inside the HER2 pocket during a 300 ns simulation. The QM/MM simulations showed that the overall total energy of the system, which combines both QM and MM contributions, remained around −79,000 ± 400 kcal/mol, suggesting that the entire protein–peptide complex was in a stable state, with pep-7 maintaining a strong, well-integrated binding. Conclusions: Pep-7 emerged as the most promising therapeutic peptide, displaying strong binding stability, favorable binding free energy, and molecular stability in HER2-overexpressing cancer models. These findings suggest pep-7 as a viable therapeutic candidate for HER2-positive cancers, offering a potential novel treatment strategy against HER2-driven malignancies. Full article
(This article belongs to the Special Issue Celebrating the 2024 Nobel Prize in Chemistry: Advancements in Artificial Intelligence and Machine Learning for Cancer Therapeutics)
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