140 Data Analytics for Process Prediction


Description:
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.

Sponsor:
Pharmaceutical Discovery, Development and Manufacturing Forum
Co-Sponsor(s):
Systems and Process Design (10A), Pharmaceuticals (15B)


Chair:
Shekhar Viswanath
Senior Engineering Advisor
Eli Lilly and Company
Product Research and Development
Indianapolis, IN
46285
Phone Number: 3172928784
Email: VISWANATH_SHEKHAR@LILLY.COM

Co-Chair:
Jacob Albrecht
Senior Research Investigator
Bristol-Myers Squibb
One Squibb Drive
New Brunswick, NJ
08903
Email: jacob.albrecht@bms.com


Papers:
140a
A Reaction Database for Small Molecule Pharmaceutical Processes Integrated with Process Information: Future Developments
Emmanouil Papadakis, Novo Nordisk A/S; Anjan Kumar Tula, Auburn University; Rafiqul Gani, PSE for SPEED
140b
Learning to Design and Validate Small-Molecule Synthetic Routes from Historical Reaction Data
Connor W. Coley, Massachusetts Institute of Technology; Pieter Plehiers, Massachusetts Institute of Technology; Wengong Jin, Massachusetts Institute of Technology; Hanyu Gao, Massachusetts Institute of Technology; Regina Barzilay, Massachusetts Institute of Technology; Tommi S. Jaakkola, Massachusetts Institute of Technology; William H. Green, Massachusetts Institute of Technology; Klavs F. Jensen, Massachusetts Institute of Technology
140c
Using Machine Learning to Recommend Reaction Conditions and Quantifying Similarity of Catalysts, Solvents, and Reagents
Hanyu Gao, Massachusetts Institute of Technology; Thomas Struble, MIT; Connor W. Coley, Massachusetts Institute of Technology; William H. Green, Massachusetts Institute of Technology; Klavs F. Jensen, Massachusetts Institute of Technology
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, Amgen Inc.; Seth Huggins, Amgen Inc.
140e
Optimizing Drying Profile of Polymeric Drug Products Using Machine Learning and First Principle Modeling
Rishi Mehan, Dr Reddys Laboratories; SVB Janardhan Garikipati, Dr Reddys Laboratories; RaviChandra Palaparthi, Dr. Reddy's Laboratories Ltd.
140f
Leveraging Deep Learning for Pharmaceutical Discovery Lead Profiling
Jacob Albrecht, Bristol-Myers Squibb; Marcello Ricottone, Bristol-Myers Squibb; Wilson Shou, Bristol-Myers Squibb; Stephen Johnson, Bristol-Myers Squibb
Abstract Submissions Closed
See more of this Group/Topical: Pharmaceutical Discovery, Development and Manufacturing Forum
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