To prioritise new compound ideas efficiently in a discovery project, compound structures and all their associated data must be brought together to guide optimisation of high quality, potent compounds. Predictions based on 2-dimensional (2D) and 3-dimensional (3D) structures are typically generated in an arsenal of modelling tools, often restricted to expert computational scientists due to their complexity. This means that computational experts are often required to run routine calculations, distracting from more scientifically challenging tasks, such as preparation and validation of protein docking models, where they can add most value. The delay in feedback to the medicinal chemists also forms a barrier to rigorous assessment of new compound ideas prior to synthesis.<br/><br/>We will present an integrated, general Pose Generation Interface which links expertly prepared docking and 3D alignment models, from a variety of applications, with a comprehensive environment for data visualisation, analysis and predictive modelling, to automate routine compound assessment in docking and alignment models. Poses and scores are automatically retrieved for visualisation and analysis, alongside predictions from models of absorption, distribution, metabolism, excretion and toxicity (ADMET), to quickly understand structure-activity relationships, identify potential liabilities and design new compounds with the highest chance of success. This enables medicinal chemists to evaluate multiple iterations of designs on-the-fly alongside other compound data. At the same time, it frees the computational scientist from running routine docking/alignment analyses, providing greater opportunity to focus on expert computational design and model building. This approach supports collaboration between computational and synthetic chemists, helping to share the results of 3D modelling studies with all decision makers.

Generic interface streamlines access to results from expertly prepared 3D models

253rd National Meeting (2017)

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