Collaborative Drug Discovery, Inc.

Virtual Approaches to New Chemistries - Project Summary/abstract Two New Virtual Chemistry Technologies Will Be Added to the Ncats Aspire Project as Separate Modules. the First Module Will Enable New Chemistries to Be Modelled and Selected From Cutting Edge (DEEP) Machine Learning Technology Using the Latest Structure/activity Data Taken Directly From Instruments. the Second Module Will Be a Novel Informatics System for Capturing Chemistry-rich Data in a Semantic Template as Machine-readable Reactions Which Will Increase the Utility of Chemical Reactions in Electronic Lab Notebooks and Allow More Precise Interrogation and Automation of Reaction Analyses (and Their Corresponding Reaction Products). the Deep Learning Technology in Module 1 Is Based on Our New Chemically Rich Vector (CRV) Methodology, Which Is Able to Compress Information About Chemical Structures Into a Vector of 64 Numbers With an Efficiency That Allows the Encoding Process to Be Reversed: Not Only Can a CRV Be Converted Back Into Its Original Structure With High Success (>90% Exact Match), But a Modified CRV Can Be Converted Into a Structure That Is Representative of That Point in Chemical Space. CRVS Make Excellent Descriptors for Sar/qsar Iteration Because They Contain Much More Chemical Information in a Small Space, Allowing the Automation of Structure-activity Models to Be More Streamlined, Relative to Conventional Descriptors. the Resulting Models Will Explore the Multi-dimensional Space via an Interactive Visual Interface (human-directed) or a Back-end Algorithm to Constantly Search for New and Better Structures (machine-directed). Both Interactive and Automated Processes Will Be Connected Back Into the Aspire Automation Cycle So That They Can Be Synthesized and Measured (hypothesis Evaluation and Iterative Optimization). the Second Module, Machine-readable Reactions, Draws From Our Extensive Experience Developing the Bioharmony Annotator (formerly: Bioassay Express) Which Uses Natural Language Models to Assign Semantic Ontology Terms to Biological Assay Protocols, Turning Them From Unstructured Text Into Machine-readable Data. Extracting the Full Content of Reactions From Protocols and Chemical Structure Diagrams Is Remarkably Difficult Given the Unstructured Nature of Text, Abbreviations, Shortcuts and Assumptions That Go Into Diagrams. It Is Further Complicated by the Need to Connect the Materials in the Scheme With the Reaction Text Description (e.g. Reagents, Solvents, the Sequences Involved in the Recipe, Reaction Workup, and Product Characterization). as an Alternative, We Will Modularize the CDD Stoichiometric Sketcher, Which Will Allow Us to Extract This Data. We Will Work With Ncats to Identify Important Fields to Capture, Creating a Machine Readable Chemical Reaction Template.