Recent Computational Techniques in De Novo Drug Design

Erscheinungsjahr 2026
ISBN: 978-3-032-20427-1
Verlag: Springer

Buch, Englisch, 343 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 697 g

Reihe: Bio-IT and AI

 “ ” gives a thorough overview of modern computational methods used to discover new chemical compounds. The book looks at how fragment-based design, evolutionary algorithms, free-energy-guided optimization, and deep generative models have helped advance molecular discovery. It also discusses important challenges such as synthetic accessibility and ADME/Tox issues. The book explains how structural bioinformatics, cheminformatics, and machine learning work together to speed up hit generation and lead optimization in both academic and industry settings.

The chapters start by introducing the basics of de novo drug design and explain how it differs from virtual screening and QSAR methods. The book describes the shift from rule-based techniques to those driven by artificial intelligence that use a wide range of molecular data. The content is organized into sections on structure-based and ligand-based methods, MD and QM approaches, deep learning applications, and case studies from different therapeutic areas.

Zielgruppe

Research

Autoren/Hrsg.

Parikesit, Arli Aditya

Ansori, Arif Nur Muhammad

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Naturwissenschaften Chemie Physikalische Chemie Quantenchemie, Theoretische Chemie

Inhaltsverzeichnis

Chapter 1. Fundamentals of Generative AI in Drug Development.- Chapter 2. Computational Design of mRNA-based Cancer Immunotherapies.- Chapter 3. Protein and Peptide Design with Generative AI.- Chapter 4. Exploring Chemical Space with Generative Models.- Chapter 5. Prediction of Drug Likeness and Synthetic Accessibility Using AI: A Case Study on Potential Therapeutic Compounds from Plants for SARS-CoV-2 Treatment.- Chapter 6. Challenges in AI-Generated Drug Candidates.- Chapter 7. AI-Driven Network Pharmacology in Natural Product-Based Drug Design: A Systems Bioinformatics Perspective.- Chapter 8. Applications of Generative AI in Small Molecule Drug Discovery.- Chapter 9. Generative AI for Antimicrobial and Antiviral Drug Design.- Chapter 10. Integration of AI-Driven Drug Design in Industry and Academia.- Chapter 11. Trends of Open-Source Tools and Frameworks for AI Drug Design.- Chapter 12. Future Horizons: Collaboration of Advanced Pharmaceutical Labs and Computational Sciences.

Über Autor(innen)

Prof. Dr.rer.nat. Arli Aditya Parikesit is a Bioinformatics Streaming Professor at i3L University (formerly Indonesia International Institute for Life Sciences). He earned his bachelor's and master's degrees in chemistry from the University of Indonesia with Biotechnology specialization. Awarded by the German Academic Exchange Service (DAAD), he pursued his doctorate in bioinformatics at the University of Leipzig, Germany, with a focus on protein domain annotation across the three domains of life. Prof. Parikesit specializes in immunoinformatics, structural bioinformatics, in silico drug design, and transcriptomics. He developed pipelines for COVID-19 drug and vaccine design and is currently advancing LAMP-CRISPR diagnostics for infectious diseases, combining cutting-edge bioinformatics with practical applications in global health.

Dr. Arif Nur Muhammad Ansori has extensive experience in virology, molecular biology, and bioinformatics. He earned his doctoral degree from Universitas Airlangga, Indonesia. Currently, he serves as a researcher at the Postgraduate School, Universitas Airlangga, and at the Division of Genomics and Transcriptomics, Joint Research Center for Human Retrovirus Infection, Kumamoto University, Japan. He is also an Adjunct Faculty Member at the Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, India, and the Director of the Virtual Research Center for Bioinformatics and Biotechnology (VRCBB), Indonesia. Dr. Ansori contributes as a board member for esteemed journals, including Scientific Reports (Springer Nature), Frontiers in Virology (Frontiers Media SA), BMC Microbiology (BioMed Central), Journal of Translational Medicine (BioMed Central), Canadian Journal of Infectious Diseases and Medical Microbiology (John Wiley & Sons), and Frontiers in Drug Discovery (Frontiers Media SA). In addition, he is also an Advisory Board Member of Clinical Nutrition ESPEN (Elsevier). Dr. Ansori is also an active member of multiple scientific societies, such as the European Virus Bioinformatics Center (EVBC), the Indonesian Young Academy of Science (ALMI), the American Society for Virology (ASV), the American Society of Microbiology (ASM), the World Society for Virology (WSV), and the Council of Asian Science Editors (CASE). Recognized for his contributions to public science communication, Dr. Ansori was honored with the TCID Author Award in 2023, selected by The Conversation Indonesia.