Quantitative structure-activity relationship (QSAR) methods are important for prediction of
biological effect of chemical compounds based on mathematical and statistical relations …

This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies
using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) …

Background: Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic
lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar …

This study focuses on the development of a machine learning ensemble approach for the
classification of Beta-Secretase 1 (BACE1) inhibitors in Quantitative Structure-Activity …

Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer
treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity …

This comprehensive review explores the pivotal role of ensemble machine learning
techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug …

Nitroaromatic compounds (NACs) represent a significant source of organic pollutants in the
environment. In this study, a well-rounded dataset containing 371 NACs with rat oral median …

Alzheimer's disease (AD) is one of the most common forms of dementia and is associated
with a decline in cognitive function and language ability. The deficiency of the cholinergic …

With the progressive and ever-increasing antibacterial resistance pathway, the need for
novel antibiotic design becomes critical. Sulfonamides are one of the more effective …

Nitroaromatic compounds (NACs) are an important type of environmental organic pollutants.
However, it is lack of sufficient information relating to their potential adverse effects on …