BACKGROUND In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementin...

Virtual screening using three-dimensional arrangements of chemical features (3D pharmacophores) has become an important method in computer-aided drug design. Although frequently used, considerable differences exist in the interpretation of these chemical features and their corresponding 3D overlay algorithms. We have recently developed an efficient and accurate 3D alignment algorithm based on a...

Effective treatments extend lives in the world, and significant efforts are in place to expand the use of life-saving medications in the developing world. This paper gives an overview of drug discovery process and emphasises in the area of virtual screening. Because machine learning is fast becoming a popular mechanism to support activity recognition in drug discovery process and other real-wor...

A combination of a literature survey, structure-based virtual screening and synthesis of a small library was performed to identify hits to the potential antimycobacterial drug target, glutamine synthetase. The best inhibitor identified from the literature survey was (2S,5R)-2,6-diamino-5-hydroxyhexanoic acid (4, IC(50) of 610+/-15microM). In the virtual screening 46,400 compounds were docked an...

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of...

We have performed virtual screening using some very simple features, by employing the number of atoms per element as molecular descriptors but without regard to any structural information whatsoever. Surprisingly, these atom counts are able to outperform virtual-affinity-based fingerprints and Unity fingerprints in some activity classes. Although molecular weight and other biases were known in ...

A standard practice for lead identification in drug discovery is ligand virtual screening, which utilizes computing technologies to detect small compounds that likely bind to target proteins prior to experimental screens. A high accuracy is often achieved when the target protein has a resolved crystal structure; however, using protein models still renders significant challenges. Towards this go...

Virtual screening methods are now widely used in early stages of drug discovery, aiming to rank potential inhibitors. However, any practical ligand set (of active or inactive compounds) chosen for deriving new virtual screening approaches cannot fully represent all relevant chemical space for potential new compounds. In this study, we have taken a retrospective approach to evaluate virtual scre...

We developed a novel approach called SHAFTS (SHApe-FeaTure Similarity) for 3D molecular similarity calculation and ligand-based virtual screening. SHAFTS adopts a hybrid similarity metric combined with molecular shape and colored (labeled) chemistry groups annotated by pharmacophore features for 3D similarity calculation and ranking, which is designed to integrate the strength of pharmacophore ...

BACKGROUND The rapid increase in the emergence of novel chemical substances presents a substantial demands for more sophisticated computational methodologies for drug discovery. In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). Applicable of identifying compounds on novel targets when there is no...