Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Virtual screening (VS) has emerged in drug discovery as a powerful computational
approach to screen large libraries of small molecules for new hits with desired properties …

The primary goal of rational drug discovery is the identification of selective ligands which act
on single or multiple drug targets to achieve the desired clinical outcome through the …

The accurate identification of Drug–Target Interactions (DTIs) remains a critical turning point
in drug discovery and understanding of the binding process. Despite recent advances in …

Antimicrobial resistance (AMR) has become more of a concern in recent decades,
particularly in infections associated with global public health threats. The development of …

Accurate and effective drug–target interaction (DTI) prediction can greatly shorten the drug
development lifecycle and reduce the cost of drug development. In the deep-learning-based …

Traditional methodologies for assessing chemical toxicity are expensive and time-
consuming. Computational modeling approaches have emerged as low-cost alternatives …

CNS disorders are indications with a very high unmet medical needs, relatively smaller
number of available drugs, and a subpar satisfaction level among patients and caregiver …

The proper assessment of target-specific compound selectivity is paramount in the drug
discovery context, promoting the identification of drug-target interactions (DTIs) and the …

Functional dyes that are capable of both bright fluorescence and efficient singlet oxygen
generation are crucial for theranostic techniques, which integrate fluorescence imaging and …