The key to extracting the best insights from your scientific data is establishing a good data management foundation. With that in mind, Dan will share:

- Enabling data driven scientific discovery by providing data access from instruments

- Enhancing the accessibility, security and accuracy of data through ELNs

- Supporting scientific decision making through intuitive data visualisation and analytics

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A holistic de novo drug design approach, developed in collaboration with Schrödinger is presented which is enabling Bayer to gain efficiencies and ultimately discover drugs faster. It has and will continue to open the door to more possibilities in drug discovery to discover novel molecules – molecules that may have otherwise never been discovered with tried-and-true experimental efforts.

This session will address:

• ML models for predictions based on experimental ADMET and chemical synthesis data
• Active learning free energy perturbation approach
• Application examples from active drug discovery projects

 

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Reaping the Benefits of an AI-Driven Automated Drug Discovery Platform

Strateos’ roboticized cloud labs shorten its clients' hit identification, H2L and lead optimization cycle times by automating in vitro cell-based/biochemical assays and design and synthesis of small molecules. Ro5 offers AI-driven solutions for target identification, hit identification, H2L, lead optimization and clinical trial analytics. Strateos and Ro5 have built a closed loop, automated design-make-test-analyze (DMTA) system that takes advantage of Strateos’s Cloud Lab Automation-as-a-Service and Ro5’s Knowledge Graph and AI Chemistry platforms. It enables fast progression from target identification to lead optimization and allows scientists to evaluate targets, efficiently identify hit compounds, and rapidly design promising drug candidates. This system has been used to identify a prospective oncology target and initiate a drug discovery program. We show that our automated AI-driven DMTA workflow can rapidly identify the majority of hits with only 10% library screened and a diverse set of scaffolds with only 1% of the library screened for subsequent lead optimization.

Key Takeaways:

• Learn how Strateos and Ro5 implemented a structure based virtual screening workflow on Strateo’s Cloud Lab-Automation-as-a-Service platform to select compounds for screening, synthesis and characterization shortening the cycle time from months to weeks. 
• Learn how AI/ML automated molecular design and custom modeling solutions create better candidate molecules faster than traditional drug discovery cycles. 

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