Artificial Intelligence in Drug Design

ISBN-13: 9781071617861 / Angielski / Twarda / 2021 / 570 str.

Alexander Heifetz

Spis treści

1. Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges

            Morgan Thomas, Andrew Boardman, Miguel Garcia-Ortegon, Hongbin Yang, Chris de Graaf, and Andreas Bender

 

2. Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints

            Andreas H. Göller, Lara Kuhnke, Antonius ter Laak, Katharina Meier, and Alexander Hillisch

 

3. Fighting COVID-19 with Artificial Intelligence

            Stefania Monteleone, Tahsin F. Kellici, Michelle Southey, Michael J. Bodkin, and Alexander Heifetz

 

4. Application of Artificial Intelligence and Machine Learning in Drug Discovery

            Rishi R. Gupta

 

5. Deep Learning and Computational Chemistry

            Tim James and Dimitar Hristozov

 

6. Has Drug Design Augmented by Artificial Intelligence Become a Reality?

            Atanas Patronov, Kostas Papadopoulos, and Ola Engkvist

 

7. Network Driven Drug Discovery

            Jonny Wray and Alan Whitmore

 

8. Predicting Residence Time of GPCR Ligands with Machine Learning

            Andrew Potterton, Alexander Heifetz, and Andrea Townsend-Nicholson

 

9. De Novo Molecular Design with Chemical Language Models

            Francesca Grisoni and Gisbert Schneider

 

10. Deep Neural Networks for QSAR

            Yuting Xu

 

11. Deep Learning in Structure-Based Drug Design

            Andrew Anighoro

 

12. Deep Learning Applied to Ligand-Based De Novo Drug Design

            Ferruccio Palazzesi and Alfonso Pozzan

 

13. Ultra-High Throughput Protein-Ligand Docking with Deep Learning

            Austin Clyde

 

14. Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors

            Tânia Cova, Carla Vitorino, Márcio Ferreira, Sandra Nunes, Paola Rondon-Villarreal, and Alberto Pais

 

15. Artificial Intelligence in Compound Design

            Christoph Grebner, Hans Matter, and Gerhard Hessler

 

16. Artificial Intelligence, Machine Learning, and Deep Learning in Real Life Drug Design Cases

            Christophe Muller, Obdulia Rabal Gracia, and Constantino Diaz Gonzalez

 

17. Artificial Intelligence-Enabled De Novo Design of Novel Compounds that are Synthesizable

            Govinda Bhisetti and Cheng Fang

 

18. Machine Learning from Omics Data

            René Rex

 

19. Deep Learning in Therapeutic Antibody Development

            Jeremy M. Shaver, Joshua Smith, and Tileli Amimeur

 

20. Machine Learning for In Silico ADMET Prediction

            Lei Jia and Hua Gao

 

21. Opportunities and Considerations in the Application of Artificial Intelligence to Pharmacokinetic Prediction

            Matthew R. Wright

 

22. Artificial Intelligence in Drug Safety and Metabolism

            Graham F. Smith

 

23. Molecule Ideation Using Matched Molecular Pairs

            Sandeep Pal, Peter Pogány, and James Andrew Lumley

Blurb

This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as:  structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. 

Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.