Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities
Shri Vile Parle Kelavani Mandal · National University of Malaysia · +3 more institutions
Abstract
There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that…
Citation impact
- FWCI
- 46.26
- Percentile
- 100%
- References
- 198
Authors
7Topics & keywords
- Drug discovery
- Computer science
- Generative grammar
- Virtual screening
- Artificial intelligence
- Context (archaeology)
- Data science
- Drug repositioning
- Partnerships for the goals