Zusammenfassung
Die naturinspirierten Rechenverfahren (NIC) wurden effektiv zur Erforschung pharmazeutischer Komponenten und Verbindungen angewendet. NIC beinhaltet Problemlösungsmethoden, die auf Abstraktionen natürlicher Prozesse basieren und neue Wege bieten, natürliche Komplexität zu verstehen, zu modellieren und zu analysieren. Diese Algorithmen imitieren biologische Systeme, um neue Rechenparadigmen zu schaffen, wie Schwarmintelligenz, neuronale Netzwerke und evolutionäres Rechnen. Heutzutage werden die NIC-Algorithmen immer beliebter bei der Lösung komplexer Optimierungen in den meisten akademischen und industriellen Bereichen, einschließlich Arzneimitteldesign, Entwicklung, Therapeutika, molekulare Modellierung und Peptiddesign. Diese Algorithmen arbeiten mit einem kombinatorischen Ansatz für kleine Moleküle und Verbindungsentwürfe, die sich auf die pharmakologischen Eigenschaften neuer Arzneimittelkandidaten stützen. Im letzten Jahrzehnt wurden NIIC-Techniken erfolgreich in jeder Phase des Arzneimittelentdeckungs- und Entwicklungsprozesses angewendet, um das Hindernis komplexer und großer Daten aus Genomik, Proteomik, Microarray-Daten und klinischen Studien zu überwinden. Dieses Kapitel fasst die jüngsten Anwendungen von NIC-Methoden in der Therapie und computergestützten Arzneimittelentwicklung zusammen.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Literatur
Aarthy M et al (2017) Advantages of structure-based drug design approaches in neurological disorders. Curr Neuropharmacol 15(8):1136–1155
Ahmad S et al (2021) Molecular dynamics simulation and docking studies reveal NF-κB as a promising therapeutic drug target for COVID-19
Appel S et al (2019) Application of nature-inspired optimization algorithms and machine learning for heavy-ion synchrotrons. Int J Mod Phys A 34(36):1942019
Baell J et al (2013) Ask the experts: past, present and future of the rule of five. Future Med Chem 5(7):745–752
Bergdorf M et al (2021) Desmond/GPU Performance as of April 2021. DE Shaw Research, Tech Rep DESRES/TR–2021–01
Boers EJ, Kuiper H (1992) Biological metaphors and the design of modular artificial neural networks
Buchoux S (2017) FATSLiM: a fast and robust software to analyze MD simulations of membranes. Bioinformatics 33(1):133–134
Choudhary N et al (2014) Quantum chemical calculations of conformation, vibrational spectroscopic, electronic, NBO and thermodynamic properties of 2, 2-dichloro-N-(2, 3-dichlorophenyl) acetamide and 2, 2-dichloro-N-(2, 3-dichlorophenyl) acetamide. Comput Theor Chem 1032:27–41
Christen M et al (2005) The GROMOS software for biomolecular simulation: GROMOS05. J Comput Chem 26(16):1719–1751
Emambocus BAS et al (2021) Dragonfly algorithm and its hybrids: a survey on performance, objectives and applications. Sensors 21(22):7542
Fan X et al (2020) Review and classification of bio-inspired algorithms and their applications. J Bionic Eng 17(3):611–631
Fiorin G, Klein ML, Hénin J (2013) Using collective variables to drive molecular dynamics simulations. Mol Phys 111(22–23):3345–3362
Frye L et al (2021) From computer-aided drug discovery to computer-driven drug discovery. Drug Discov Today Technol 39:111–117
Gautam R, Kaur P, Sharma M (2019) A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings. Prog Artif Intell 8(4):401–424
Gupta N (2013) Artificial neural network. Netw Complex Syst 3(1):24–28
Halgren TA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750–1759
Himabindu K, Jyothi S (2017) Nature inspired computation techniques and its applications in soft computing: survey. Int J Res Appl Sci Eng Technol 5(7):1906–1916
Huey R, Morris GM, Forli S (2012) Using AutoDock 4 and AutoDock vina with AutoDockTools: a tutorial. Scripps Res Inst Mol Graph Lab 10550:92037
Keerthi S, Ashwini K, Vijaykumar M (2015) Survey paper on swarm intelligence. Int J Comput Appl 115(5)
Llorach-Pares L et al (2017) Computer-aided drug design applied to marine drug discovery: Meridianins as Alzheimer’s disease therapeutic agents. Mar Drugs 15(12):366
Losos JB, Ricklefs RE, MacArthur RH (2010) The theory of island biogeography revisited. Princeton University Press, Princeton
McInnes G et al (2021) Opportunities and challenges for the computational interpretation of rare variation in clinically important genes. Am J Hum Genet 108(4):535–548
Mikkilä-Erdmann M (2001) Improving conceptual change concerning photosynthesis through text design. Learn Instr 11(3):241–257
Mishra BB, Tiwari VK (2011) Natural products: an evolving role in future drug discovery. Eur J Med Chem 46(10):4769–4807
Najjar A et al (2019) Fragment-based drug design of nature-inspired compounds. Phys Sci Rev 4(9)
Nayak J et al (2020) Firefly algorithm in biomedical and health care: advances, issues and challenges. SN Comput Sci 1(6):1–36
Nedjah N, de Macedo Mourelle L (2006) Swarm intelligent systems, vol 26. Springer
Newton I (1990) Mathematical principles of natural philosophy. Encyclopaedia Britannica
Onawole AT et al (2020) COVID-19: CADD to the rescue. Virus Res 285:198022
Overgaard M, Mogensen J (2011) A framework for the study of multiple realizations: the importance of levels of analysis. Front Psychol 2:79
Pathak N et al (2021) Phytochemical analysis and antifungal activity of weed extracts against Rhizoctonia root rot in Buckwheat (Fagopyrum tataricum)
Pijper A (1939) The microscope in biology. S Afr J Sci 36(12):58–72
Repasky MP, Shelley M, Friesner RA (2007) Flexible ligand docking with Glide. Curr Protoc Bioinform 18(1):8.12.1–8.12.36
Sachan AK et al (2014) Molecular structure, vibrational and electronic properties of 4-Phenyl-3H-1, 3-thiazol-2-ol using density functional theory and comparison of drug efficacy of keto and enol forms by QSAR analysis. Spectrochim Acta Part A Mol Biomol Spectrosc 132:568–581
Salehahmadi Z, Manafi A (2014) How can bee colony algorithm serve medicine? World J Plast Surg 3(2):87
Seth D et al (2011) Nature-inspired novel drug design paradigm using nanosilver: efficacy on multi-drug-resistant clinical isolates of tuberculosis. Curr Microbiol 62(3):715–726
Siddique N, Adeli H (2015) Nature inspired computing: an overview and some future directions. Cogn Comput 7(6):706–714
Song CM, Lim SJ, Tong JC (2009) Recent advances in computer-aided drug design. Brief Bioinform 10(5):579–591
Timmis J, Andrews P, Hart E (2010) On artificial immune systems and swarm intelligence. Swarm Intell 4(4):247–273
Tong JC (2017) Applications of computer-aided drug design. Drug design: principles and applications. Springer, pp 1–7
Verdonk ML et al (2003) Improved protein–ligand docking using GOLD. Proteins Struct Funct Bioinform 52(4):609–623
Yadav MK et al (2021) Predictive modeling and therapeutic repurposing of natural compounds against the receptor-binding domain of SARS-CoV-2. J Biomol Struct Dyn 1–13
Zekri M, Obreza TA (2003) Plant nutrients for citrus trees. University of Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences
Zhang S (2011) Computer-aided drug discovery and development. Drug Des Discov 23–38
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Ahmad, S. et al. (2024). Beispielhafte Implikationen von naturinspirierten Berechnungsmethoden auf Therapeutika und computergestützte Arzneimittelentwicklung. In: Raza, K. (eds) Von der Natur inspirierte intelligente Datenverarbeitungstechniken in der Bioinformatik. Springer, Singapore. https://doi.org/10.1007/978-981-99-7808-3_15
Download citation
DOI: https://doi.org/10.1007/978-981-99-7808-3_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7807-6
Online ISBN: 978-981-99-7808-3
eBook Packages: Computer Science and Engineering (German Language)