Drug Target Review Screening ebook 2022
7 December 2022
In this ebook are articles on how artificial intelligence can be used to identify drug candidates, data-lead screening models and autonomous strategies for molecular discovery.
Here, experts explore how to enhance screening strategies to find drug candidates more efficiently.
In the first article on screening, Dr Ozlem Ozmen Garibay and Aida Tayebi, University of Central Florida, outline how a natural language-inspired technique was used to develop a generalisable drug target interaction model to identify drug candidates. They spotlight their interpretable and generalisable drug target interaction model that achieves 97 percent accuracy in identifying drug candidates for a broad variety of target proteins.
Today’s drug screening methods use one or two types of data. However, disease biology is not replicable by simple screening models because diseases are complex and heterogenous. In the second piece, Dr Aaron Daugherty, Aria Pharmaceuticals, examines advanced screening methods that can process dozens of data sources, the use of which could lead to breakthrough therapies.
In the third article, Niamh Morris, Rosalind Franklin Institute, explains why activity-directed synthesis (ADS) could enable the exploration of a far broader chemical space than is possible with conventional approaches. She explains ADS enables the parallel exploration of multiple molecular series and discovery of associated synthetic routes, making it an efficient way to screen against biological targets.
In this ebook:
- Artificial intelligence-aided screening could boost speed of new drug discovery
Dr Ozlem Ozmen Garibay and Aida Tayebi, University of Central Florida
- How many drug discovery breakthroughs have we missed?
Dr Aaron Daugherty, Aria Pharmaceuticals
- Activity-directed synthesis: a structure-blind, function-driven molecular discovery approach.
Niamh Morris, Rosalind Franklin Institute