Computer-aided drug discovery has been around for decades, although the past few years
have seen a tectonic shift towards embracing computational technologies in both academia …

This Perspective is a continuation of our analysis of US FDA-approved small-molecule drugs
(1938–2012) containing nitrogen heterocycles. In this study we report drug structure and …

Ethnopharmacological relevance Network pharmacology is a new discipline based on
systems biology theory, biological system network analysis, and multi-target drug molecule …

Iron dysregulation has been implicated in multiple neurodegenerative diseases, including
Parkinson's disease (PD). Iron-loaded microglia are frequently found in affected brain …

Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big
concern during drug development in the pharmaceutical industry. Failure or inhibition of …

Drug-discovery and drug-development endeavors are laborious, costly and time consuming.
These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of …

Abstract Machine learning has transformed many fields and has recently found applications
in chemistry and materials science. The small datasets commonly found in chemistry …

We present a comprehensive analysis of all ring systems (both heterocyclic and
nonheterocyclic) in clinical trial compounds and FDA-approved drugs. We show 67% of …

This review provides the feasible literature on drug discovery through ML tools and
techniques that are enforced in every phase of drug development to accelerate the research …