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Computing the Rearrangement Distance of Natural Genomes Read chapter Read first chapter

2020 | OriginalPaper | Chapter

PaccMannRL: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning

Authors : Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, María Rodríguez Martínez

Published in: Research in Computational Molecular Biology

Publisher: Springer International Publishing

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Abstract

The pharmaceutical industry has experienced a significant productivity decline: Less than 0.01% of drug candidates obtain market approval, with an estimated 10–15 years until market release and costs that range between one [2] to three billion dollars per drug [3].

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previous chapter Bayesian Non-parametric Clustering of Single-Cell Mutation Profiles
next chapter CluStrat: A Structure Informed Clustering Strategy for Population Stratification
Literature
1.
Manica, M., et al.: Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders. Molecular Pharmaceutics (2019). ACS Publications
2.
Scannell, J.W., Blanckley, A., Boldon, H., Warrington, B.: Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discovery 11(3), 191 (2012)CrossRef
3.
Schneider, G.: Mind and machine in drug design. Nat. Mach. Intell. 1, 128–130 (2019)CrossRef
Metadata
Title
PaccMannRL: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning
Authors
Jannis Born
Matteo Manica
Ali Oskooei
Joris Cadow
María Rodríguez Martínez
Copyright Year
2020
Book
Research in Computational Molecular Biology

Print ISBN: 978-3-030-45256-8

Electronic ISBN: 978-3-030-45257-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-45257-5_18

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