Machine-Learning in Drug Discovery

Machine-Learning in Drug Discovery Virtual high-throughput screening (vHTS) can be used in drug discovery to replace experimental HTS or to preselect a subset of compounds for screening to reduce costs and time. The two main approaches to vHTS are structure-based or ligand-based screening. Structure-based…Read more ›
SkelGen – A Newly Available Tool for Computational Drug Design With the rapidly growing body of biostructural information, structure-based drug design has increased in importance and a variety of computational methods have found a place in the drug discovery toolkit. The de novo design program, SkelGen, was developed by De…Read more ›
Targeting Orphan Receptors for Multiple Sclerosis Scientists at the Scripps Research Institute have reported on compounds that are able to suppress severity and disease progression in animal models of multiple sclerosis. The compounds, exemplified by SR1001, act by selectively suppressing a subset of T-helper cells characterised…Read more ›
Exploiting Bioenergetic Differences to Stop GVHD Bz-423, a mitochondrial F1F0-ATP synthase inhibitor, that has previously shown promise for the treatment of autoimmune disorders such as lupus, arthritis and psoriasis has now been shown to halt the progression of established graft-versus-host disease (GVHD) in mouse models of…Read more ›
The Right Tool for the Job? Tool compounds are used to explore the role of a specific protein in a biological context and – it goes without saying – that to obtain meaningful results in a complex situation, the tool compound should have appropriate potency and…Read more ›