What file types can I upload into StarDrop and Semeta?
StarDrop and Semeta can read SD (.sdf), comma separated value (.csv), SMILES (.smi), text (.txt), mol (.mol) and mol2 (.mol2)…
StarDrop and Semeta can read SD (.sdf), comma separated value (.csv), SMILES (.smi), text (.txt), mol (.mol) and mol2 (.mol2)…
There is no hard limit to the number of compounds that can be loaded and analysed in StarDrop since it…
You can cite the latest version of StarDrop using the text below: StarDrop v. XXX, Optibrium Ltd; optibrium.com/products/stardrop/
Have advances in AI and deep learning reached a threshold whereby generative chemistry methods are redefining drug design? This webinar…
This paper describes the prediction of the regioselectivity of metabolism by AOs, FMOs and UGTs for humans and CYPs for three preclinical species.
Virtual screening presents a host of challenges, especially where little or no structural information on targets is available. So how can we best set our screening strategies up for success?
Explore ways to use the Inspyra Panel, in combination with Matched Series Analysis (MSA).
This worked example uses Inspyra™ to interactively explore optimisation strategies to achieve a selective inhibitor of DPP-4 with appropriate physicochemical properties.
In this webinar, we examine the effective use of QSAR modelling in drug discovery and discuss a variety of pain points for medicinal chemists in knowing when a model can be trusted and how to avoid common pitfalls.
This script enables you to search SpotRM to identify the structural features of your molecules that might lead to the formation of…
This short video gives an introduction to the Matched Pairs Neighbourhood tool in StarDrop’s Card View. If you are interested…
In this example, we are going to use the reaction-based library enumeration feature in StarDrop’s Nova module to generate a library of virtual compounds. This will be based on pre-defined sets of reagents that will be used to generate products using well-known reactions.
This worked example uses StarDrop’s Surflex eSim3D module to assess a small library of compounds for their similarity to known Heat Shock Protein 90 (HSP90) ligands. ideo archive.
In this example we will illustrate how knowledge-based predictions of toxicity can be used within a MPO environment to guide the selection and design of compounds with a good balance of properties and reduced risk of toxicity.
During this example we will consider three compounds from a lead series which we would like to try to evolve into a candidate. The compound has a good profile of ADME properties but insufficient inhibition of the target, the Serotonin transporter. In this example we will use StarDrop’s Nova module to generate new ideas for compounds to improve the potency while maintaining the balance of other properties.
In this example, using StarDrop’s R-group clipping tool, we will quickly transform chemical building blocks into their corresponding substituents, ready to enumerate a virtual library in StarDrop’s Nova module.
This worked example explores ways to assess and design compounds in 3D using the SeeSAR Pose module.
This worked example explores ways to assess the binding affinity of docked compounds.
In this example we will explore the feasibility of pursuing a fast-follower for Buspirone, a 5-HT1A ligand used as an anti-anxiolytic therapeutic, which has a known liability due to rapid metabolism by CYP3A4.