Drug development is one of the most significant processes in the pharmaceutical industry.
Various computational methods have dramatically reduced the time and cost of drug …

Highlights•Six commonly used machine learning methods in QSAR models are
summarized.•Newly developed combinatorial QSAR and hybrid QSAR methods are …

In recent times, machine learning has become increasingly prominent in predictive
toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro …

Discovering and accurately locating drug targets is of great significance for the research and
development of new drugs. As a different approach to traditional drug development, the …

Introduction: Big-data-driven drug development resources and methodologies have been
evolving with ever-expanding data from large-scale biological experiments, clinical trials …

Late-stage drug development failures are usually a consequence of ineffective targets. Thus,
proper target identification is needed, which may be possible using computational …

Cotton is a valuable fiber and cash crop in Telangana, India. This study examines how crop
growth and fiber yield are affected by the uneven distribution of rainfall. Cotton phyllosphere …

The study of drug-target protein interaction is a key step in drug research. In recent years,
machine learning techniques have become attractive for research, including drug research …

Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a
massive global health burden. Despite considerable efforts, the underlying mechanisms …

Background: From a therapeutic viewpoint, understanding how drugs bind and regulate the
functions of their target proteins to protect against disease is crucial. The identification of …