New Drug Development Status & News from Pharmaceutical Companies ManufacturersNovember 20, 2023
Examples of implementing AI in Drug Discovery by stage
By implementing AI into the drug discovery process, we can expect to improve operational efficiency, reduce development costs, and shorten drug development timeframes. Here, we introduce some examples of successful AI drug discovery at different stages.
1. Paper analysis
In Drug Discovery, paper analysis is essential to obtain information on new compounds and targets in a timely manner. However, it faces challenges such as the inability to identify all the necessary papers and the inability to conduct objective and exhaustive searches and analyses.
To solve the challenges of paper search, we recommend the implementation of AI. Here we explain the challenges in searching and analyzing papers and how to solve them utilizing AI.
Target search (Target molecule search)
2. Target search (Target molecule search)
Drug Discovery target search (target molecule search) is the search for genes that cause diseases, factors involved in the onset and progression of diseases, and molecules that are effective in treating those diseases.
However, there are some challenges in Drug Discovery target search (target molecule search), such as (1) low accuracy in selection and (2) bias of researchers.
FRONTEO's Drug Discovery support AI will be able to solve or improve the efficiency of these challenges through efficient search.
Screening is the process of selecting substances from a compound library that are effective in treating a disease or that exhibit the highest drug efficacy. Methods such as virtual screening are being implemented to solve the challenges.
However, for further efficiency, speed, and cost compression, it is recommended to implement AI in the basic research and hypothesis generation stages. Check here for challenges and solutions in screening.
Non-clinical trials(pre-clinical trials)
4. Non-clinical trials(pre-clinical trials)
Non-clinical trials(pre-clinical trials) are experiments conducted in the preliminary stages of testing and clinical trials(clinical study) on humans. Generally, it is conducted on experimental animals or cells.
Some of the challenges of non-clinical trials are that the results of non-clinical trials do not match those of clinical trials, and that the duraion is prolonged and the costs are high.
Here summarizes the challenges and solutions in non-clinical trials(pre-clinical trials) .
5. Clinical trials
A clinical trial is a test conducted on humans to confirm the efficacy and safety of new treatments or medicines. Unlike human experiments, it is conducted in accordance with strict standards to ensure safety.
As a way to solve challenges in clinical trials, the use of AI and DX is getting underway such as automating big data processing in clinical trials with AI.
Here describes challenges and solutions in clinical trials.
6. Drug repositioning
Drug repositioning is a method of discovering new medicinal effects for existing drugs and developing them as treatments for other diseases.
Since the safety and manufacturing methods of existing drugs have already been confirmed, and a large amount of existing data can be used, drug repositioning is less risky, less costly, and faster than drug discovery from scratch.
Here are some of the challenges and solutions in drug repositioning.