Machine learning applications in drug development

By Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez. 2019

184 citations recorded by the source corpus.

Graphical abstract

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jian Tang
2. Michael M. Bronstein
3. Arian Jamasb
4. Tom L. Blundell
5. Ben Day
6. David B. Ascher
7. Douglas E. V. Pires

In Molecular Biology

1. Jian Tang
2. Michael M. Bronstein
3. Arian Jamasb
4. Tom L. Blundell
5. Ben Day
6. Yang Zhang
7. David B. Ascher
8. Douglas E. V. Pires

Citations in the corpus

• Addressing bias in big data and AI for health care: A call for open science (2021, background): “11 AI systems could also help reduce health care costs, predict patients’ no show, or shorten hospital waiting times by searching millions of medical records.”
• Utilizing graph machine learning within drug discovery and development (2021, background): “of the drug discovery and development pipeline [6], see Figure 1.”