AI in drug development: a multidisciplinary perspective
Molecular Diversity, 2021
Abstract
This review discusses the application of artificial intelligence tools, particularly Bayesian approaches, in streamlining the lengthy and costly process of new drug development.
By aiding in tasks like identifying molecular targets and predicting targets such as ADME-Tox, Machine Learning models are utilized in all stages from basic research to post-marketing.
This review aims to bridge the gap between chemists and mathematicians, focusing on molecular modeling and decision-support applications often overlooked in similar research.
Streamlining Drug Development Process using Bayesian Approaches in Artificial Intelligence
Where does it apply?
The use of Bayesian approaches is applicable across the entire drug development process, from basic research for drug discovery, the pre-clinical phase, the clinical phase, and even the post-marketing phase.
It can be used to identify molecular targets, search for hit and lead compounds, synthesize drug-like compounds, predict ADME-Tox, and support decision-making processes.
Why does it matters?
Machine Learning can significantly expedite and reduce the cost of the process which traditionally is lengthy and costly. Specifically, the use of Bayesian approaches aids in better decision-making and molecular modeling, thus potentially leading to more effective and safer drugs.
Furthermore, a better understanding between chemists and mathematicians can yield improved collaboration and integration of AI into the drug development process.
AI in drug development: a multidisciplinary perspective
Molecular Diversity, 2021
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