Machine learning-based quantitative structure–activity relationship model for antibiotic prediction and discovery
Jiratchaya Nakbang, Chonthicha Arbsuwan, Santitham Prom-on, Peerut Chienwichai, Dukyong Yoon, Dukyong Yoon, Dukyong Yoon · 2026
Read Machine learning-based quantitative structure–activity relationship model for antibiotic prediction and discovery by Jiratchaya Nakbang, Chonthicha Arbsuwan, Santitham Prom-on, Peerut Chienwichai, Dukyong Yoon, Dukyong Yoon, Dukyong Yoon (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13472409.txt.
Description
Antimicrobial resistance (AMR) is a critical global health problem that has become increasingly alarming in recent years. The discovery of new antibiotics is one approach for alleviating AMR, however, screening for novel drugs is time consuming and expensive. To accelerate antibiotic discovery, the integration of machine learning algorithms with Quantitative Structure–Activity Relationship (QSAR) calculations could provide a rapid solution.
Catalog record
| CREATOR | Jiratchaya Nakbang, Chonthicha Arbsuwan, Santitham Prom-on, Peerut Chienwichai, Dukyong Yoon, Dukyong Yoon, Dukyong Yoon |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 60 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |