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Pharmaceuticals
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Computational Methods in the Design of Anticancer Drugs
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Computational Methods in the Design of Anticancer Drugs

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 44266

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Marialuigia Fantacuzzi

E-Mail Website
Guest Editor
Department of Pharmacy, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
Interests: drug discovery; computational techniques; anticancer agents; aromatase inhibitors
Special Issues, Collections and Topics in MDPI journals
Dr. Mariangela Agamennone

E-Mail Website
Guest Editor
Department of Pharmacy, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
Interests: drug design; in silico drug design; docking; pharmacophore; chemoinformatics; virtual screening; molecular dynamics; computer-aided drug design; MMP inhibitors; anticancer agents; anti-influenza agents; protein–protein interaction inhibitors; cancer immunotherapy

Special Issue Information

Dear Colleagues,

Cancer is still a major threat to human health and one of the leading causes of death worldwide. In recent years, advances in the development of new anticancer drugs have been continuous, and several compounds (small molecules, engineered antibodies, immunomodulators, etc.) have been approved for the treatment of cancer. 

In recent decades, computational methods have become an essential tool in the drug design process as they are able to reduce research costs and accelerate the development process. The application of computational methods in the design of anticancer drugs has proved to be very effective. Given the wide variety of very different tumor forms and the multiplicity of possible pharmacological targets, this research area is very fruitful.

This Special Issue on "Computational methods in the design of anticancer drugs" aims to collect the most recent discoveries in the field of anticancer drug design with the aid of computational methods, such as structure-based drug discovery and ligand-based drug discovery classical or de novo drug design (molecular docking, virtual screening, pharmacophore mapping, similarity searching, QSAR modeling), molecular dynamics and the development of machine learning methods. These are some types of computational approaches that we would like to highlight in this Special Issue.

We are looking forward to your contribution.

Dr. Marialuigia Fantacuzzi
Dr. Mariangela Agamennone
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. Pharmaceuticals 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 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

  • cancer drug design
  • computational methods
  • computer-aided drug design
  • molecular docking
  • molecular dynamics
  • QSAR, machine learning
  • chemoinformatics
  • virtual screening
  • pharmacophore models
  • bioinformatics
  • artificial intelligence
  • DFT
  • ab initio

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Published Papers (12 papers)

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Editorial

Jump to: Research, Review

4 pages, 176 KiB  
Editorial
Computational Methods in the Design of Anticancer Drugs
by Marialuigia Fantacuzzi and Mariangela Agamennone
Pharmaceuticals 2024, 17(4), 404; https://doi.org/10.3390/ph17040404 - 22 Mar 2024
Viewed by 1090
Abstract
In recent years, continuous progress has been made in the development of new anticancer drugs, and several compounds (small molecules, engineered antibodies, immunomodulators, etc [...] Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)

Research

Jump to: Editorial, Review

24 pages, 3772 KiB  
Article
Application of Ligand- and Structure-Based Prediction Models for the Design of Alkylhydrazide-Based HDAC3 Inhibitors as Novel Anti-Cancer Compounds
by Emre F. Bülbül, Dina Robaa, Ping Sun, Fereshteh Mahmoudi, Jelena Melesina, Matthes Zessin, Mike Schutkowski and Wolfgang Sippl
Pharmaceuticals 2023, 16(7), 968; https://doi.org/10.3390/ph16070968 - 6 Jul 2023
Cited by 2 | Viewed by 2230
Abstract
Histone deacetylases (HDAC) represent promising epigenetic targets for several diseases including different cancer types. The HDAC inhibitors approved to date are pan-HDAC inhibitors and most show a poor selectivity profile, side effects, and in particular hydroxamic-acid-based inhibitors lack good pharmacokinetic profiles. Therefore, the [...] Read more.
Histone deacetylases (HDAC) represent promising epigenetic targets for several diseases including different cancer types. The HDAC inhibitors approved to date are pan-HDAC inhibitors and most show a poor selectivity profile, side effects, and in particular hydroxamic-acid-based inhibitors lack good pharmacokinetic profiles. Therefore, the development of isoform-selective non-hydroxamic acid HDAC inhibitors is a highly regarded field in medicinal chemistry. In this study, we analyzed different ligand-based and structure-based drug design techniques to predict the binding mode and inhibitory activity of recently developed alkylhydrazide HDAC inhibitors. Alkylhydrazides have recently attracted more attention as they have shown promising effects in various cancer cell lines. In this work, pharmacophore models and atom-based quantitative structure–activity relationship (QSAR) models were generated and evaluated. The binding mode of the studied compounds was determined using molecular docking as well as molecular dynamics simulations and compared with known crystal structures. Calculated free energies of binding were also considered to generate QSAR models. The created models show a good explanation of in vitro data and were used to develop novel HDAC3 inhibitors. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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Graphical abstract

30 pages, 5251 KiB  
Article
Ligand Growing Experiments Suggested 4-amino and 4-ureido pyridazin-3(2H)-one as Novel Scaffold for FABP4 Inhibition
by Letizia Crocetti, Giuseppe Floresta, Chiara Zagni, Divya Merugu, Francesca Mazzacuva, Renan Rodrigues de Oliveira Silva, Claudia Vergelli, Maria Paola Giovannoni and Agostino Cilibrizzi
Pharmaceuticals 2022, 15(11), 1335; https://doi.org/10.3390/ph15111335 - 28 Oct 2022
Cited by 5 | Viewed by 1926
Abstract
Fatty acid binding protein (FABP4) inhibitors are of synthetic and therapeutic interest and ongoing clinical studies indicate that they may be a promise for the treatment of cancer, as well as other diseases. As part of a broader research effort to develop more [...] Read more.
Fatty acid binding protein (FABP4) inhibitors are of synthetic and therapeutic interest and ongoing clinical studies indicate that they may be a promise for the treatment of cancer, as well as other diseases. As part of a broader research effort to develop more effective FABP4 inhibitors, we sought to identify new structures through a two-step computing assisted molecular design based on the established scaffold of a co-crystallized ligand. Novel and potent FABP4 inhibitors have been developed using this approach and herein we report the synthesis, biological evaluation and molecular docking of the 4-amino and 4-ureido pyridazinone-based series. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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Graphical abstract

15 pages, 5060 KiB  
Article
Design and Synthesis of Aminopyrimidinyl Pyrazole Analogs as PLK1 Inhibitors Using Hybrid 3D-QSAR and Molecular Docking
by Swapnil P. Bhujbal, Hyejin Kim, Hyunah Bae and Jung-Mi Hah
Pharmaceuticals 2022, 15(10), 1170; https://doi.org/10.3390/ph15101170 - 21 Sep 2022
Cited by 3 | Viewed by 2405
Abstract
Cancer continues to be one of the world’s most severe public health issues. Polo-like kinase 1 (PLK1) is one of the most studied members of the polo-like kinase subfamily of serine/threonine protein kinases. PLK1 is a key mitotic regulator responsible for cell cycle [...] Read more.
Cancer continues to be one of the world’s most severe public health issues. Polo-like kinase 1 (PLK1) is one of the most studied members of the polo-like kinase subfamily of serine/threonine protein kinases. PLK1 is a key mitotic regulator responsible for cell cycle processes, such as mitosis initiation, bipolar mitotic spindle formation, centrosome maturation, the metaphase to anaphase transition, and mitotic exit, whose overexpression is often associated with oncogenesis. Moreover, it is also involved in DNA damage response, autophagy, cytokine signaling, and apoptosis. Due to its fundamental role in cell cycle regulation, PLK1 has been linked to various types of cancer onset and progression, such as lung, colon, prostate, ovary, breast cancer, melanoma, and AML. Hence, PLK1 is recognized as a critical therapeutic target in the treatment of various proliferative diseases. PLK1 inhibitors developed in recent years have been researched and studied through clinical trials; however, most of them have failed because of their toxicity and poor therapeutic response. To design more potent and selective PLK1 inhibitors, we performed a receptor-based hybrid 3D-QSAR study of two datasets, possessing similar common scaffolds. The developed hybrid CoMFA (q2 = 0.628, r2 = 0.905) and CoMSIA (q2 = 0.580, r2 = 0.895) models showed admissible statistical results. Comprehensive, molecular docking of one of the most active compounds from the dataset and hybrid 3D-QSAR studies revealed important active site residues of PLK1 and requisite structural characteristics of ligand to design potent PLK1 inhibitors. Based on this information, we have proposed approximately 38 PLK1 inhibitors. The newly designed PLK1 inhibitors showed higher activity (predicted pIC50) than the most active compounds of all the derivatives selected for this study. We selected and synthesized two compounds, which were ultimately found to possess good IC50 values. Our design strategy provides insight into development of potent and selective PLK1 inhibitors. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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Graphical abstract

15 pages, 2689 KiB  
Article
Structure-Based Identification and Biological Characterization of New NAPRT Inhibitors
by Jorge Franco, Francesco Piacente, Melanie Walter, Simone Fratta, Moustafa Ghanem, Andrea Benzi, Irene Caffa, Alexander V. Kurkin, Andrea Altieri, Patrick Herr, Macarena Martínez-Bailén, Inmaculada Robina, Santina Bruzzone, Alessio Nencioni and Alberto Del Rio
Pharmaceuticals 2022, 15(7), 855; https://doi.org/10.3390/ph15070855 - 12 Jul 2022
Cited by 10 | Viewed by 2775
Abstract
NAPRT, the rate-limiting enzyme of the Preiss–Handler NAD biosynthetic pathway, has emerged as a key biomarker for the clinical success of NAMPT inhibitors in cancer treatment. Previous studies found that high protein levels of NAPRT conferred resistance to NAMPT inhibition in several tumor [...] Read more.
NAPRT, the rate-limiting enzyme of the Preiss–Handler NAD biosynthetic pathway, has emerged as a key biomarker for the clinical success of NAMPT inhibitors in cancer treatment. Previous studies found that high protein levels of NAPRT conferred resistance to NAMPT inhibition in several tumor types whereas the simultaneous blockade of NAMPT and NAPRT results in marked anti-tumor effects. While research has mainly focused on NAMPT inhibitors, the few available NAPRT inhibitors (NAPRTi) have a low affinity for the enzyme and have been scarcely characterized. In this work, a collection of diverse compounds was screened in silico against the NAPRT structure, and the selected hits were tested through cell-based assays in the NAPRT-proficient OVCAR-5 ovarian cell line and on the recombinant hNAPRT. We found different chemotypes that efficiently inhibit the enzyme in the micromolar range concentration and for which direct engagement with the target was verified by differential scanning fluorimetry. Of note, the therapeutic potential of these compounds was evidenced by a synergistic interaction between the NAMPT inhibitor FK866 and the new NAPRTi in terms of decreasing OVCAR-5 intracellular NAD levels and cell viability. For example, compound IM29 can potentiate the effect of FK866 of more than two-fold in reducing intracellular NAD levels. These results pave the way for the development of a new generation of human NAPRTi with anticancer activity. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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18 pages, 9260 KiB  
Article
Identification of NAPRT Inhibitors with Anti-Cancer Properties by In Silico Drug Discovery
by Moustafa S. Ghanem, Irene Caffa, Alberto Del Rio, Jorge Franco, Marco Daniele Parenti, Fiammetta Monacelli, Michele Cea, Amr Khalifa, Aimable Nahimana, Michel A. Duchosal, Silvia Ravera, Nadia Bertola, Santina Bruzzone, Alessio Nencioni and Francesco Piacente
Pharmaceuticals 2022, 15(7), 848; https://doi.org/10.3390/ph15070848 - 10 Jul 2022
Cited by 13 | Viewed by 3588
Abstract
Depriving cancer cells of sufficient NAD levels, mainly through interfering with their NAD-producing capacity, has been conceived as a promising anti-cancer strategy. Numerous inhibitors of the NAD-producing enzyme, nicotinamide phosphoribosyltransferase (NAMPT), have been developed over the past two decades. However, their limited anti-cancer [...] Read more.
Depriving cancer cells of sufficient NAD levels, mainly through interfering with their NAD-producing capacity, has been conceived as a promising anti-cancer strategy. Numerous inhibitors of the NAD-producing enzyme, nicotinamide phosphoribosyltransferase (NAMPT), have been developed over the past two decades. However, their limited anti-cancer activity in clinical trials raised the possibility that cancer cells may also exploit alternative NAD-producing enzymes. Recent studies show the relevance of nicotinic acid phosphoribosyltransferase (NAPRT), the rate-limiting enzyme of the Preiss–Handler NAD-production pathway for a large group of human cancers. We demonstrated that the NAPRT inhibitor 2-hydroxynicotinic acid (2-HNA) cooperates with the NAMPT inhibitor FK866 in killing NAPRT-proficient cancer cells that were otherwise insensitive to FK866 alone. Despite this emerging relevance of NAPRT as a potential target in cancer therapy, very few NAPRT inhibitors exist. Starting from a high-throughput virtual screening approach, we were able to identify and annotate two additional chemical scaffolds that function as NAPRT inhibitors. These compounds show comparable anti-cancer activity to 2-HNA and improved predicted aqueous solubility, in addition to demonstrating favorable drug-like profiles. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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17 pages, 1276 KiB  
Article
Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict PET Image Quality of Three Generations EGFR TKI in Advanced-Stage NSCLC Patients
by I. H. Bartelink, E. A. van de Stadt, A. F. Leeuwerik, V. L. J. L. Thijssen, J. R. I. Hupsel, J. F. van den Nieuwendijk, I. Bahce, M. Yaqub and N. H. Hendrikse
Pharmaceuticals 2022, 15(7), 796; https://doi.org/10.3390/ph15070796 - 27 Jun 2022
Cited by 9 | Viewed by 3320
Abstract
Introduction: Epidermal growth factor receptor (EGFR) mutated NSCLC is best treated using an EGFR tyrosine kinase inhibitor (TKI). The presence and accessibility of EGFR overexpression and mutation in NSCLC can be determined using radiolabeled EGFR TKI PET/CT. However, recent research has shown a [...] Read more.
Introduction: Epidermal growth factor receptor (EGFR) mutated NSCLC is best treated using an EGFR tyrosine kinase inhibitor (TKI). The presence and accessibility of EGFR overexpression and mutation in NSCLC can be determined using radiolabeled EGFR TKI PET/CT. However, recent research has shown a significant difference between image qualities (i.e., tumor-to-lung contrast) in three generation EGFR TKIs: 11C-erlotinib, 18F-afatinib and 11C-osimertinib. In this research we aim to develop a physiological pharmacokinetic (PBPK)-model to predict tumor-to-lung contrast and as a secondary outcome the uptake of healthy tissue of the three tracers. Methods: Relevant physicochemical and drug specific properties (e.g., pKa, lipophilicity, target binding) for each TKI were collected and applied in established base PBPK models. Key hallmarks of NSCLC include: immune tumor deprivation, unaltered tumor perfusion and an acidic tumor environment. Model accuracy was demonstrated by calculating the prediction error (PE) between predicted tissue-to-blood ratios (TBR) and measured PET-image-derived TBR. Sensitivity analysis was performed by excluding each key component and comparing the PE with the final mechanistical PBPK model predictions. Results: The developed PBPK models were able to predict tumor-to-lung contrast for all EGFR-TKIs within threefold of observed PET image ratios (PE tumor-to-lung ratio of −90%, +44% and −6.3% for erlotinib, afatinib and osimertinib, respectively). Furthermore, the models depicted agreeable whole-body distribution, showing high tissue distribution for osimertinib and afatinib and low tissue distribution at high blood concentrations for erlotinib (mean PE, of −10.5%, range −158%–+190%, for all tissues). Conclusion: The developed PBPK models adequately predicted the image quality of afatinib and osimertinib and erlotinib. Some deviations in predicted whole-body TBR lead to new hypotheses, such as increased affinity for mutated EGFR and active influx transport (erlotinib into excreting tissues) or active efflux (afatinib from brain), which is currently unaccounted for. In the future, PBPK models may be used to predict the image quality of new tracers. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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19 pages, 3125 KiB  
Article
Exploring the Natural Compounds in Flavonoids for Their Potential Inhibition of Cancer Therapeutic Target MEK1 Using Computational Methods
by Wejdan M. AlZahrani, Shareefa A. AlGhamdi, Torki A. Zughaibi and Mohd Rehan
Pharmaceuticals 2022, 15(2), 195; https://doi.org/10.3390/ph15020195 - 3 Feb 2022
Cited by 9 | Viewed by 2958
Abstract
The Mitogen-Activated Protein Kinase (MAPK) signaling pathway plays an important role in cancer cell proliferation and survival. MAPKs’ protein kinases MEK1/2 serve as important targets in drug designing against cancer. The natural compounds’ flavonoids are known for their anticancer activity. This study aims [...] Read more.
The Mitogen-Activated Protein Kinase (MAPK) signaling pathway plays an important role in cancer cell proliferation and survival. MAPKs’ protein kinases MEK1/2 serve as important targets in drug designing against cancer. The natural compounds’ flavonoids are known for their anticancer activity. This study aims to explore flavonoids for their inhibition ability, targeting MEK1 using virtual screening, molecular docking, ADMET prediction, and molecular dynamics (MD) simulations. Flavonoids (n = 1289) were virtually screened using molecular docking and have revealed possible inhibitors of MEK1. The top five scoring flavonoids based on binding affinity (highest score for MEK1 is −10.8 kcal/mol) have been selected for further protein–ligand interaction analysis. Lipinski’s rule (drug-likeness) and absorption, distribution, metabolism, excretion, and toxicity predictions were followed to find a good balance of potency. The selected flavonoids of MEK1 have been refined with 30 (ns) molecular dynamics (MD) simulation. The five selected flavonoids are strongly suggested to be promising potent inhibitors for drug development as anticancer therapeutics of the therapeutic target MEK1. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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16 pages, 5953 KiB  
Article
Repositioning of Etravirine as a Potential CK1ε Inhibitor by Virtual Screening
by Luis Córdova-Bahena, Axel A. Sánchez-Álvarez, Angel J. Ruiz-Moreno and Marco A. Velasco-Velázquez
Pharmaceuticals 2022, 15(1), 8; https://doi.org/10.3390/ph15010008 - 22 Dec 2021
Cited by 3 | Viewed by 4204
Abstract
CK1ε is a key regulator of WNT/β-catenin and other pathways that are linked to tumor progression; thus, CK1ε is considered a target for the development of antineoplastic therapies. In this study, we performed a virtual screening to search for potential CK1ε inhibitors. First, [...] Read more.
CK1ε is a key regulator of WNT/β-catenin and other pathways that are linked to tumor progression; thus, CK1ε is considered a target for the development of antineoplastic therapies. In this study, we performed a virtual screening to search for potential CK1ε inhibitors. First, we characterized the dynamic noncovalent interactions profiles for a set of reported CK1ε inhibitors to generate a pharmacophore model, which was used to identify new potential inhibitors among FDA-approved drugs. We found that etravirine and abacavir, two drugs that are approved for HIV infections, can be repurposed as CK1ε inhibitors. The interaction of these drugs with CK1ε was further examined by molecular docking and molecular dynamics. Etravirine and abacavir formed stable complexes with the target, emulating the binding behavior of known inhibitors. However, only etravirine showed high theoretical binding affinity to CK1ε. Our findings provide a new pharmacophore for targeting CK1ε and implicate etravirine as a CK1ε inhibitor and antineoplastic agent. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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Review

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28 pages, 4005 KiB  
Review
A Comprehensive Computational Insight into the PD-L1 Binding to PD-1 and Small Molecules
by Marialuigia Fantacuzzi, Roberto Paciotti and Mariangela Agamennone
Pharmaceuticals 2024, 17(3), 316; https://doi.org/10.3390/ph17030316 - 28 Feb 2024
Cited by 1 | Viewed by 2814
Abstract
Immunotherapy has marked a revolution in cancer therapy. The most extensively studied target in this field is represented by the protein–protein interaction between PD-1 and its ligand, PD-L1. The promising results obtained with the clinical use of monoclonal antibodies (mAbs) directed against both [...] Read more.
Immunotherapy has marked a revolution in cancer therapy. The most extensively studied target in this field is represented by the protein–protein interaction between PD-1 and its ligand, PD-L1. The promising results obtained with the clinical use of monoclonal antibodies (mAbs) directed against both PD-1 and PD-L1 have prompted the search for small-molecule binders capable of disrupting the protein–protein contact and overcoming the limitations presented by mAbs. The disclosure of the first X-ray complexes of PD-L1 with BMS ligands showed the protein in dimeric form, with the ligand in a symmetrical hydrophobic tunnel. These findings paved the way for the discovery of new ligands. To this end, and to understand the binding mechanism of small molecules to PD-L1 along with the dimerization process, many structure-based computational studies have been applied. In the present review, we examined the most relevant articles presenting computational analyses aimed at elucidating the binding mechanism of PD-L1 with PD-1 and small molecule ligands. Additionally, virtual screening studies that identified validated PD-L1 ligands were included. The relevance of the reported studies highlights the increasingly prominent role that these techniques can play in chemical biology and drug discovery. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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24 pages, 5688 KiB  
Review
Computer-Aided Identification of Kinase-Targeted Small Molecules for Cancer: A Review on AKT Protein
by Erika Primavera, Deborah Palazzotti, Maria Letizia Barreca and Andrea Astolfi
Pharmaceuticals 2023, 16(7), 993; https://doi.org/10.3390/ph16070993 - 11 Jul 2023
Viewed by 3004
Abstract
AKT (also known as PKB) is a serine/threonine kinase that plays a pivotal regulatory role in the PI3K/AKT/mTOR signaling pathway. Dysregulation of AKT activity, especially its hyperactivation, is closely associated with the development of various human cancers and resistance to chemotherapy. Over the [...] Read more.
AKT (also known as PKB) is a serine/threonine kinase that plays a pivotal regulatory role in the PI3K/AKT/mTOR signaling pathway. Dysregulation of AKT activity, especially its hyperactivation, is closely associated with the development of various human cancers and resistance to chemotherapy. Over the years, a wide array of AKT inhibitors has been discovered through experimental and computational approaches. In this regard, herein we present a comprehensive overview of AKT inhibitors identified using computer-assisted drug design methodologies (including docking-based and pharmacophore-based virtual screening, machine learning, and quantitative structure–activity relationships) and successfully validated small molecules endowed with anticancer activity. Thus, this review provides valuable insights to support scientists focused on AKT inhibition for cancer treatment and suggests untapped directions for future computer-aided drug discovery efforts. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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18 pages, 1612 KiB  
Review
Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade
by Liuying Wang, Yongzhen Song, Hesong Wang, Xuan Zhang, Meng Wang, Jia He, Shuang Li, Liuchao Zhang, Kang Li and Lei Cao
Pharmaceuticals 2023, 16(2), 253; https://doi.org/10.3390/ph16020253 - 7 Feb 2023
Cited by 27 | Viewed by 10104
Abstract
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug [...] Read more.
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements. Full article
(This article belongs to the Special Issue Computational Methods in the Design of Anticancer Drugs)
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