Before committing resources to in vivo and in vitro studies, it's vital to know you're working with the best molecules. StarDrop is built to help you predict a wide range of ADME and physiochemical properties in silico, allowing you to explore all of your options and giving you confidence in your decisions.

This module gives novice and expert users alike access to the tools needed to produce validated, predictive models. Output from the Auto-Modeller includes Glowing Molecule results that help to visualise the structure-activity relationships captured from your chemistry and data.

Generate and prioritise new compounds ideas to stimulate the search for compound optimisation strategies for your project:

• Rigorously explore the chemistry around hits
• Discover lead-hopping opportunities
• Identify substitutions likely to improve target activity
• Fully enumerate new library designs
• Find patent-busting possibilities

An optional extension to the Nova modules, providing over 27,000 structure modifications and replacement techniques that have been used in analogue design:

• Identify transformations previously applied to compounds similar to yours
• Literature references allow quick follow-up of chemistry and biological data
• Explore diverse compound optimisation strategies
• Quickly identify patent protection/busting strategies

Using StarDrop's P450 metabolism models you can quickly identify the regions of your compounds that are most vulnerable to metabolism by the major drug metabolising isoforms of cytochrome P450 and predict the metabolites formed.

StarDrop's Derek Nexus module provides Lhasa Limited's world-leading technology for knowledge-based prediction of key toxicities, helping to guide the design of safe and efficacious drugs in hit-to-lead and lead-optimisation.

StarDrop's torch3D module, using Cresset's molecular Field technology, provides a unique, 3-dimensional (3D) insight into the biological activity, properties and interactions of your compounds, helping to guide the design of novel, potent compounds with a high chance of success.

SeeSAR, developed in collaboration with BioSolveIT, is a suite of three modules which seamlessly interact to enable you to easily visualise ligand and protein structures, identify key interactions driving binding affinity and link the design of new compounds in two and three dimensions.

This module provides novel, patented methods to guide 'strategic' decisions for your discovery projects.

• Find multi-parameter scoring profiles, based on historic data, to optimally select successful compounds
• Identify the most important data with which to distinguish successful compounds and focus experimental resources
• Test the robustness of your decisions to the selection criteria you have chosen