Transforming discovery

The journey from data to discovery starts with analysis. You need to make sense of the data you have, visualise and interpret results, and use this information to understand the structure-activity relationships (SAR) in your chemical series.

You can easily find informative patterns in your data by generating interactive charts, chemical spaces and dashboards. Methods such as R-group analysis, clustering, matched molecular pairs and activity landscapes provide greater understanding of your compounds and data, complemented by unique visualisations such as StarDrop’s Glowing Molecule™ and Card View®.

For even deeper insights, Optibrium’s artificial intelligence methods can reveal hidden relationships within your complex data, to guide optimisation, synthesis decisions, and experimental prioritisation.

To explore ideas and prioritise suitable compounds for synthesis and testing, you need relevant data to support your decisions. Enhance your knowledge with predictive modelling, using robust QSAR models, cutting-edge quantum mechanical simulations and machine learning methods to enable informed decision-making.

Whether you want to identify toxicity and improve the safety profile of your compounds, avoid liabilities from Phase I or II metabolism, or determine key ADME or physicochemical properties such as pKa or solubility, early-stage prediction can provide the data you need for successful compound optimisation.

To achieve your goals, you need to find or design compounds with the right balance of properties. Using Optibrium’s unique approach to multi-parameter optimisation (MPO), you can build tailored scoring profiles for your project. These enable easy analysis of your complex data and help you to target the compounds most likely to be successful in your projects, while accounting for errors and predictive uncertainty to ensure you don’t miss valuable opportunities.

With limitless chemical space to explore, it can be difficult to pinpoint the compounds that provide the best activity, physicochemical and ADME properties for your objectives.

Using automated de novo compound suggestions or matched series analysis you can build on your own experience to expand the range of ideas. You can also use structure-based or reaction-based enumeration to construct libraries from commercially-available or proprietary building blocks.

Molecules aren’t two dimensional, and their 3D structures will significantly influence key properties such as binding affinity. From small molecules to large, complex macrocycles, understanding the conformational preferences of your compounds and their 3D structure-activity relationships enables fast, successful compound optimisation.

Improve the potency of your compounds by applying a range of methods across ligand- and structure-based drug design, from fast template-free conformer generation, to top-tier screening enrichment and accurate binding affinity prediction.

Drug discovery data are complex. With many possible endpoints to measure and vast chemical space to explore, the data that we have available are typically very sparse and limited, and experimental resources can be difficult to prioritise. The measurements we have are subject to experimental error, and inaccuracies are inevitable. Complex underlying relationships such as in vitro-in vivo correlations make it hard to determine the right compounds to progress.

By harnessing powerful AI, you can go from drug concept to candidate faster, pinpointing the compounds most likely to be successful in your projects and the best experiments to prioritise. Reveal transformative insights hidden by missing, inaccurate or uncertain data. Using early-stage data to predict the results of expensive downstream experiments, you can determine mechanisms of action and identify adverse outcome pathways enabling you to make informed decisions by harnessing data from current, past or parallel projects.