Interpretable machine learning models for QSAR-based prediction of anti- <i>Salmonella typhi</i> activity

Overall, rigorous ML-QSAR modeling offers a reliable and efficient framework for virtual screening and prioritization of novel anti-S. typhi agents discovery.

Authors

Institutions

Publication Details

Journal
Future Medicinal Chemistry
Published
2026-01-26
DOI
https://doi.org/10.1080/17568919.2026.2619464
Citations
1
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
22.24
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Interpretable machine learning models for QSAR-based prediction of anti- Salmonella typhi activity

Reaz Uddin, Ozair Khurram Hashmi, Saltanat Aghayeva
1 citations
Future Medicinal Chemistry
vaccines and immunoinformatics approaches
22.24
article

Interpretable machine learning models for QSAR-based prediction of anti- Salmonella typhi activity

Reaz Uddin, Ozair Khurram Hashmi, Saltanat Aghayeva
article en
1 citations

Abstract

Overall, rigorous ML-QSAR modeling offers a reliable and efficient framework for virtual screening and prioritization of novel anti-S. typhi agents discovery.

Future Medicinal ChemistryVol. 18(5)
University of Karachi (PK), Western Caspian University (AZ)
Zero hunger
Openalex Percentile: Top 2%
vaccines and immunoinformatics approaches
22.24
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Interpretable machine learning models for QSAR-based prediction of anti- <i>Salmonella typhi</i> activity — Reaz Uddin, Ozair Khurram Hashmi, et al. · Future Medicinal Chemistry (2026) | TGRS Research Map | TGRS