Main description:

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.

Contents:

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. Goeller, 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

Tania Cova, Carla Vitorino, Marcio 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

Rene 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 Pogany, and James Andrew Lumley