Abstract
The world is transforming using Artificial Intelligence. Pharmaceutical companies are currently using traditional medicine discovery, which is a lengthy and costly process, and most discovered drugs cannot obtain relevant regulatory approval for patient use due to their unsuitability. A Virtual screening (VS) approach is a new method to improve the research and development lifecycle. The VS process reduces the initial level of research required to discover potential drug components for a disease. Deep Learning (DL) algorithms play a vital role with VS. DL can understand the patterns within an existing dataset. The drug discovery process applies algorithms to understand the pattern of different chemical compounds with diseases. Now drug discovery is evolving using the capabilities of AI. Deepchem is one of the leading platforms that enable the construction of AI models for different diseases to identify potential drug components. This research paper discusses the Tyrosine-protein kinase cancer disease. In this research, the VS process is used to identify possible drug components of this disease. Results demonstrate that the AI-enabled VS method can predict with 85% accuracy potential drugs (ligands) that could be used on a small data set of cancer disease
| Original language | English |
|---|---|
| Title of host publication | 2021 32nd Irish Signals and Systems Conference, ISSC 2021 |
| Publisher | IEEE Xplore |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-6654-3429-4 |
| ISBN (Print) | 978-1-6654-3430-0 |
| DOIs | |
| Publication status | Published online - 1 Jul 2021 |
| Event | 2021 32nd Irish Signals and Systems Conference (ISSC) - Athlone, Ireland Duration: 10 Jun 2021 → 11 Jun 2021 |
Conference
| Conference | 2021 32nd Irish Signals and Systems Conference (ISSC) |
|---|---|
| Period | 10/06/21 → 11/06/21 |
Keywords
- machine learning
- deep learning
- Ligand
- artificial intelligence
- virtual screening
- moleular weight
