Artificial Intelligence in Drug Discovery-Editor: Nathan Brown
https://doi.org/10.1039/9781788016841
Print publication date
12 Nov 2020
Copyright year
2021
Print ISBN
978-1-78801-547-9
PDF eISBN
978-1-78801-684-1
ePub eISBN
978-1-83916-054-7
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.
Pages 1 - 6
Pages 7 - 8
Pages 9 - 17
Pages 1 - 6
Pages 7 - 14
Nadine Schneider, Nikolas Fechner, Nikolaus Stiefl and Gregory A. Landrum
Pages 15 - 44
Colin Batchelor, Peter Corbett, Aileen Day, Jeff White and John Boyle
Pages 45 - 62
Isidro Cortés-Ciriano and Andreas Bender
Pages 63 - 101
Pages 102 - 118
José Jiménez-Luna and Gianni De Fabritiis
Pages 119 - 150
Fergus Imrie, Anthony R. Bradley and Charlotte M. Deane
Pages 151 - 183
Shuzhe Wang and Sereina Riniker
Pages 184 - 214
Pages 215 - 227
Wengong Jin, Regina Barzilay and Tommi Jaakkola
Pages 228 - 249
Ed Griffen, Alexander Dossetter and Andrew G. Leach
Pages 250 - 271
Ola Engkvist, Josep Arús-Pous, Esben Jannik Bjerrum and Hongming Chen
Pages 272 - 300
Pages 301 - 326
Pages 327 - 348
Loïc M. Roch, Florian Häse and Alán Aspuru-Guzik
Pages 349 - 388
Pages 389 - 393
Pages 394 - 405
