TY - JOUR
T1 - Recent Trends in Computer-aided Drug Design for Anti-cancer Drug Discovery
AU - Tur Razia, Iashia
AU - Kanwal, Ayesha
AU - Riaz, Hafiza Fatima
AU - Malik, Abbeha
AU - Ahsan, Muhammad
AU - Saleem Khan, Muhammad
AU - Raza, Ali
AU - Sabir, Sumera
AU - Sajid, Zureesha
AU - Fardeen Khan, Muhammad
AU - Tahir, Rana Adnan
AU - Arslan Sehgal, Sheikh
N1 - Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
PY - 2023
Y1 - 2023
N2 - Cancer is considered one of the deadliest diseases globally, and continuous research is being carried out to find novel potential therapies for myriad cancer types that affect the human body. Researchers are hunting for innovative remedies to minimize the toxic effects of conventional therapies being driven by cancer, which is emerging as pivotal causes of mortality worldwide. Cancer progression steers the formation of heterogeneous behavior, including self-sustaining proliferation, malignancy, and evasion of apoptosis, tissue invasion, and metastasis of cells inside the tumor with distinct molecular features. The complexity of cancer therapeutics demands advanced approaches to comprehend the underlying mechanisms and potential therapies. Precision medicine and cancer therapies both rely on drug discovery. In vitro drug screening and in vivo animal trials are the mainstays of traditional approaches for drug development; however, both techniques are laborious and expensive. Omics data explosion in the last decade has made it possible to discover efficient anti-cancer drugs via computational drug discovery approaches. Computational techniques such as computer-aided drug design have become an essential drug discovery tool and a keystone for novel drug development methods. In this review, we seek to provide an overview of computational drug discovery procedures comprising the target sites prediction, drug discovery based on structure and ligand-based design, quantitative structure-activity relationship (QSAR), molecular docking calculations, and molecular dynamics simulations with a focus on cancer therapeutics. The applications of artificial intelligence, databases, and computational tools in drug discovery procedures, as well as successfully computationally designed drugs, have been discussed to highlight the significance and recent trends in drug discovery against cancer. The current review describes the advanced computer-aided drug design methods that would be helpful in the designing of novel cancer therapies.
AB - Cancer is considered one of the deadliest diseases globally, and continuous research is being carried out to find novel potential therapies for myriad cancer types that affect the human body. Researchers are hunting for innovative remedies to minimize the toxic effects of conventional therapies being driven by cancer, which is emerging as pivotal causes of mortality worldwide. Cancer progression steers the formation of heterogeneous behavior, including self-sustaining proliferation, malignancy, and evasion of apoptosis, tissue invasion, and metastasis of cells inside the tumor with distinct molecular features. The complexity of cancer therapeutics demands advanced approaches to comprehend the underlying mechanisms and potential therapies. Precision medicine and cancer therapies both rely on drug discovery. In vitro drug screening and in vivo animal trials are the mainstays of traditional approaches for drug development; however, both techniques are laborious and expensive. Omics data explosion in the last decade has made it possible to discover efficient anti-cancer drugs via computational drug discovery approaches. Computational techniques such as computer-aided drug design have become an essential drug discovery tool and a keystone for novel drug development methods. In this review, we seek to provide an overview of computational drug discovery procedures comprising the target sites prediction, drug discovery based on structure and ligand-based design, quantitative structure-activity relationship (QSAR), molecular docking calculations, and molecular dynamics simulations with a focus on cancer therapeutics. The applications of artificial intelligence, databases, and computational tools in drug discovery procedures, as well as successfully computationally designed drugs, have been discussed to highlight the significance and recent trends in drug discovery against cancer. The current review describes the advanced computer-aided drug design methods that would be helpful in the designing of novel cancer therapies.
KW - Anti-cancer drugs
KW - Bioinformatics
KW - Cancer
KW - Computer Aided Drug Design (CADD)
KW - Drug Discovery
KW - Molecular Docking
KW - Structure-based drug design
UR - http://www.scopus.com/inward/record.url?scp=85179335124&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179335124&partnerID=8YFLogxK
U2 - 10.2174/0115680266258467231107102643
DO - 10.2174/0115680266258467231107102643
M3 - Review article
C2 - 38031798
SN - 1568-0266
VL - 23
SP - 2844
EP - 2862
JO - Current Topics in Medicinal Chemistry
JF - Current Topics in Medicinal Chemistry
IS - 30
ER -