Drug discovery aims at finding new compounds with specific chemical properties for the
treatment of diseases. In the last years, the approach used in this search presents an …

This review is focused on several machine learning approaches used in chemoinformatics.
Machine learning approaches provide tools and algorithms to improve drug discovery. Many …

abstract Introduction: The use of computational tools in the early stages of drug development
has increased in recent decades. Machine learning (ML) approaches have been of special …

In recent decades, artificial intelligence and machine learning have played a significant role
in increasing the efficiency of processes across a wide spectrum of industries. When it …

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and
Machine Learning (ML) with cheminformatics has proven to be a powerful combination …

QSAR. Despite this long tradition, machine learning methods gained substantial momentum
recently triggered by the success of deep learning in many application areas. 1 A wide …

Abstract Machine learning (ML) is becoming capable of transforming biomolecular
interaction description and calculation, promising an impact on molecular and drug design …

The fusion of machine learning (ML) and artificial intelligence (AI) is experiencing a dramatic
transition in the field of pharmaceutical research and development. This study examines the …

Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …

Background: Drug discovery, which is the process of discovering new candidate
medications, is very important for pharmaceutical industries. At its current stage, discovering …