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AI and Machine Learning (AI/ML) methods offer an alluring way to expedite the drug discovery process by delivering a large number of possible new candidates for any given therapeutic area. However, insight from medicinal chemists is still necessary to triage and prioritise the most promising molecules for synthesis, and progress these through the Design-Make-Test-Analyse (DMTA) cycle. A smooth interface between data and humans drives down DMTA cycle times and shortens the path to clinical candidates. 
In this webinar, we demonstrate the power of combining Torx® with CAS SciFinder-n to ensure the correct decisions are made quickly to deliver new drugs faster. Torx is a web-based platform that drives collaboration and productivity in drug discovery by connecting teams and data across the entire DMTA cycle. It enables chemists to seamlessly access and refine compounds (whether obtained from AI/ML predictions or more traditional approaches such as collaborations with computational chemistry teams) using desirable physico-chemical properties, 3D-pose information and prior project knowledge in a single environment. 
By connecting to CAS SciFinder-n directly from the Torx GUI, we show how to uncover key insights in synthetic feasibility and IP position at the click of a button, by mining extensive substances, reagent and reaction schemes, before communicating synthesis priorities and assignments to team members and CRO partners.

An audience Q&A session follows the webinar presentation.

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