The discovery and advances of medicines may be considered as the ultimate relevant
translational science effort that adds to human invulnerability and happiness. But advancing …

The COVID-19 pandemic has emphasized the need for novel drug discovery process.
However, the journey from conceptualizing a drug to its eventual implementation in clinical …

Large language models (LLMs) have shown strong performance in tasks across domains
but struggle with chemistry-related problems. These models also lack access to external …

Over the last decades, excellent computational chemistry tools have been developed.
Integrating them into a single platform with enhanced accessibility could help reaching their …

The deployment of large language models (LLMs) within the healthcare sector has sparked
both enthusiasm and apprehension. These models exhibit the remarkable ability to provide …

Conventional wet laboratory testing, validations, and synthetic procedures are costly and
time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques …

Artificial intelligence (AI) has been widely applied in drug discovery with a major task as
molecular property prediction. Despite booming techniques in molecular representation …

The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Drug development is expensive, time-consuming, and has a high failure rate. In recent
years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery …

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 …