Computational techniques to make drug discovery more efficient
Enoch Huang, Head of Computational Sciences, Pfizer R&D
Applying the right computational technique to the right problem at the right time is critical to making the complex drug discovery process more efficient. This talk will survey different computational methods commonly employed in the drug discovery setting, such as assessing target druggability, virtual screening, lead optimization, and compound safety prediction. Some of these methods are based on first-principles, while others are trained on existing data using machine learning algorithms.
Enoch S. Huang, Ph.D., received an AB in Molecular Biology from Princeton University and a PhD in Structural Biology from Stanford University, where he was a National Science Foundation Pre-doctoral Fellow in the laboratory of Prof. Michael Levitt (2013 Nobel Prize in Chemistry). He was appointed a Jane Coffin Childs Fellow at Washington University School of Medicine (St. Louis), where he developed methods for protein structure prediction with Prof. Jay Ponder. In 1999, Enoch joined Cereon Genomics as a Computational Biologist. The following year, he accepted a position at Pfizer R&D in Cambridge as a Senior Research Scientist. In 2001, he became department head of the newly formed Molecular Informatics group and joined the site management team. In 2007 he accepted a global role as Head of the Computational Sciences Center of Emphasis.
External to Pfizer, Enoch has been an Adjunct Assistant Professor of Bioinformatics at Boston University since 2001. He currently serves on the Editorial Advisory Board for Drug Discovery Today, the Bioinformatics Professional Advisory Committee at Brandeis University, and the Industry Advisory Board of the International Society for Computational Biology. He has also served on the external advisory board of the Bioinformatics Program at the Rochester Institute of Technology, the program committee of the Systems Biology discussion group at the New York Academy of Sciences, the Steering Group for the Life Sciences Informatics Committee of the Massachusetts Biotechnology Council, and on Special Emphasis Panels of study sections for the National Institutes of Health. He is the author of over 30 research articles, scientific reviews, and book chapters and released the Open Source software package PFAAT.