A typical drug discovery project involves identifying active compounds with significant
binding potential for selected disease-specific targets. Experimental high-throughput …

Drug discovery is a cost and time-intensive process that is often assisted by computational
methods, such as virtual screening, to speed up and guide the design of new compounds …

Current drug development is still costly and slow given tremendous technological
advancements in drug discovery and medicinal chemistry. Using machine learning (ML) to …

Comparative evaluation of virtual screening methods requires a rigorous benchmarking
procedure on diverse, realistic, and unbiased data sets. Recent investigations from …

Highlights•We review machine learning methods/tools relevant to ligand-based virtual
screening.•Machine learning methods classify compounds and predict new active …

With the recent explosion in the size of libraries available for screening, virtual screening is
positioned to assume a more prominent role in early drug discovery's search for active …

Larger training datasets have been shown to improve the accuracy of machine learning (ML)-
based scoring functions (SFs) for structure-based virtual screening (SBVS). In addition …

Importance of the field: In research relating to the development of new drugs, hit
identification and validations are critical for successful optimization of candidates. To …

Structure-based virtual screening is now an established technology for supporting hit finding
and lead optimisation in drug discovery. Recent validation studies have highlighted the poor …

Abstract Structure-based virtual screening (SBVS) has been widely applied in early-stage
drug discovery. From a problem-centric perspective, we reviewed the recent advances and …