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

Drug discovery is a time-consuming, high-investment, and high-risk process in traditional
drug development. Drug repositioning has become a popular strategy in recent years …

Traditional drug design requires a great amount of research time and developmental
expense. Booming computational approaches, including computational biology, computer …

Microporous zeolites that can act as heterogeneous catalysts have continued to attract a
great deal of academic and industrial interest, but current progress in their synthesis and …

Computer-aided drug discovery/design methods have played a major role in the
development of therapeutically important small molecules for over three decades. These …

Mycolic acids are major and specific lipid components of the mycobacterial cell envelope
and are essential for the survival of members of the genus Mycobacterium that contains the …

This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with
the foundations of the theory and building it up, this is essential reading for any scientists …

In silico catalyst design is a grand challenge of chemistry. Traditional computational
approaches have been limited by the need to compute properties for an intractably large …

Abstract The Transporter Classification Database (TCDB; http://www. tcdb. org) serves as a
common reference point for transport protein research. The database contains more than 10 …

Background Structure elucidation of unknown small molecules by mass spectrometry is a
challenge despite advances in instrumentation. The first crucial step is to obtain correct …