2018 AIChE Annual Meeting

Session: Data Analytics for Process Prediction

Data Science and Analytics is a rapidly emerging field with multiple applications arising from the combined analysis of structured (relational & ontological databases) and unstructured (text, voice, image, video) data from multiple internal and external source systems. The fruits of recent progress in this field have transformed how organizations can gain insights from data, and have fully computerized some tasks once thought to be beyond automation. This session will focus on the application of data analytics to deliver similar benefits for pharmaceutical development engineers.

Chair

Shekhar Viswanath, Eli Lilly and Company

Co-Chair

Jacob Albrecht, Bristol-Myers Squibb

Presentations

12:30 PM

(140f) Leveraging Deep Learning for Pharmaceutical Discovery Lead Profiling
Jacob Albrecht, Marcello Ricottone, Wilson Shou, Stephen Johnson

12:55 PM

(140b) Learning to Design and Validate Small-Molecule Synthetic Routes from Historical Reaction Data
Connor Coley, Pieter Plehiers, Wengong Jin, Hanyu Gao, Regina Barzilay, Tommi S. Jaakkola, William Green, Klavs F. Jensen

01:20 PM

(140c) Using Machine Learning to Recommend Reaction Conditions and Quantifying Similarity of Catalysts, Solvents, and Reagents
Hanyu Gao, Thomas Struble, Connor Coley, William Green, Klavs F. Jensen

01:45 PM

(140d) Applying Data Science Techniques to Solubility Data for Synthetic Compounds: An Expedited End-to-End Workflow from Data Collection to Crystallization Process Design
Michael Lovette, Seth Huggins

02:10 PM

(140e) Optimizing Drying Profile of Polymeric Drug Products Using Machine Learning and First Principle Modeling
Rishi Mehan, SVB Janardhan Garikipati, RaviChandra Palaparthi, Arnesh Palanisamy

02:35 PM

(140a) A Reaction Database for Small Molecule Pharmaceutical Processes Integrated with Process Information: Future Developments
Emmanouil Papadakis, Anjan Kumar Tula, Rafiqul Gani