StarDrop

StarDrop is designed to enhance the optimization of small molecules by improving target activity, physicochemical properties, ADME, and safety, ultimately delivering high-quality candidates. This process involves exploring optimization strategies to quickly identify the best ideas.

Leading with Knowledge

To make informed decisions, having the necessary data is crucial. In silico ADME prediction supports this by providing:

• QSAR Models of Key Properties: Predict key properties using robust ADME and physicochemical models at the click of a button.
• A Tailored Approach to QSAR: Build and validate models specific to your chemistry and data, with easy visualization using the Glowing Molecule feature.
• Phase I and II Metabolic Routes: Identify metabolic pathways and metabolites quickly to guide compound design and avoid metabolic liabilities.
• Prediction of Over 40 Key Toxicity Endpoints: Design safe, efficacious drugs with knowledge-based toxicity prediction from Lhasa Limited’s Derek Nexus models.
• Generate Predictions for Key Properties: Model properties such as target activities, selectivity profiles, phenotypic responses, and in vivo pharmacokinetics and efficacy.

Exploring in 3D

Gain insights into structure-activity relationships through 3D modeling, enhancing the understanding of compound interactions.

The Perfect Balancing Act

Successful drugs require a balance of activity, selectivity, and physicochemical and ADMET properties. StarDrop facilitates multi-parameter optimization (MPO) by creating a profile of desired properties and their importance, scoring each compound for success likelihood against chosen criteria. This approach considers data uncertainty, preventing the loss of potential opportunities due to inaccurate measurements or predictions.

The software includes innovative patented methods to identify the best scoring profiles for project objectives and highlights the most critical data for selecting successful compounds, optimizing experimental resources. It also allows testing the robustness of decisions based on selection criteria to ensure valuable compounds are not overlooked.