In Silico Drug Discovery Conference Announces Speakers and Topics

RESEARCH TRIANGLE PARK, N.C., Sept. 17, 2014 /PRNewswire/ -- The In Silico Drug Discovery Conference today announced speakers and presentation topics that will be featured at the conference, which takes place at North Carolina Biotechnology Center's Hamner Conference Center on December 3 and 4, 2014. Researchers, students, drug developers, business development professionals, and biotechnology managers are invited to hear the following:

KEYNOTE SPEAKERS

Ruben Abagyan, PhD
UC San Diego
Skaggs School of Pharmacy
Drug Discovery with Three Dimensional Models of Everything

The number of protein structures in the Protein Data Bank exceeds 100,000 and even the most recalcitrant to crystallization membrane proteins, G-protein coupled receptors (GPCRs) and channels, are no longer out of reach. Dr. Abagyan will discuss how these structures can be converted to specific three dimensional models, or fields that help to facilitate with the following major tasks:

• Building models by homology for the proteins not yet crystallized, subtypes, hetero-oligomers, and defining details not visible in crystal structures;
• In silico docking and screening for new modulators of a specific protein;
• Screening of a single compound against a panel of models to identify poly-pharmacology, adverse effects, or, in some cases identify a target of a phenotypic screen.

The types of three dimensional models and the methods involved continue to evolve, improve and organized into knowledge bases and automated workflows. Finally, he will show how computational methods help to design crystallizeable constructs for membrane proteins and their complexes.

Frank Brown, PhD
Merck & CO.
Computational Science and Informatics in the Pharmaceutical Industry

Dr.  Brown is presently Associate VP and Head, Global Structural Chemistry for Merck. He has an impressive background in the pharmaceutical industry in the field of computational science and informatics. Dr. Brown has spent the last 20 years of his career innovating new scientific methods and applying them in commercial pharmaceutical enterprises. From 2006 to 2012, he held the position of CSO for Accelrys where he was involved in developing the strategy for the company and driving the scientific agenda. At the conference, Dr. Brown will share his insights into computational science and informatics.

Christopher Lipinski, PhD
Melior Discovery
From Computational Prediction Paper to Amusing, Wide Ranging, Slightly Unorthodox and Provocative Thoughts

Dr. Lipinski will discuss his thoughts on intellectual property and the trials and tribulations of medicinal chemistry due-diligence. The session will explore questions such as, "What really constitutes 'prior art?' Can disclosing too much data be a problem? Why do medicinal chemists tend to repeat the same motifs in their syntheses? What really is medicinal chemistry due diligence and how does it differ from what a biologist would do? How does medicinal chemistry due diligence tie in with target and ligand network maps and with evolution?

Dr. Lipinski is currently a Scientific Advisor to Melior Discovery a drug repurposing biotech and carries out his medicinal chemistry consulting. He is renowned for his "Rule of Five"; an algorithm for predicting drug compounds likely to show oral activity. His 1997 publication in "Advanced Drug Delivery Reviews," where the "Rule of Five" first appears, is the most frequently cited medicinal chemistry paper in the last decade and is one of the most cited publications in the journal's history.

Pat Walters, PhD
Vertex Pharmaceuticals
The Evolving Role of Modeling and Informatics in Drug Discovery

Over the last few years, we have seen a dramatic increase in the amount of data generated as part of drug discovery programs.  Increasing regulatory pressures and an awareness of the importance of drug-like properties have led to an effort to more rigorously characterize drug candidates.  Discovery compounds are now routinely subjected to a battery of properties assays as well as in-vitro and in-vivo ADME evaluation.  In addition to dealing with an increase in the number of assays, teams are also incorporating calculated properties, ADME models, and multiple ligand efficiency metrics as part of the optimization process. This data explosion is further compounded by large amounts of data coming from public sources through databases like ChEMBL, PubChem, RCSB, and DrugBank.  As part of this new paradigm, the role of the modeler in drug discovery is changing.  Modelers must be able to integrate this plethora of information and enable discovery teams to make effective decisions.

Dr. Walters will discuss the challenges facing the modeling community and provide a few suggestions for future directions.

PRESENTATIONS

Tim Cheeseright, PhD
Cresset Group
Multi-Dimensional Activity Cliff Analysis

Addresses how 3D similarity method can determine how structural changes, during lead optimization, can cause unexpected changes in binding activity, also known as activity cliff.  It will also present novel activity cliff visualization techniques.

David Deng
ChemAxon LLC
ChemCurator, Computer-Assisted Patent Curation and Analysis Tool
Explores a new semi-automated method of patent curation that extracts both chemical names and markush structures using the technologies of Markush Technology and ChemAxon's Document to Structure.  Although human intervention is still necessary, it can be a very useful for making patent analyzation process much easier.

Sean Ekins, PhD
Collaborations in Chemistry
Medicinal Chemistry Due Diligence: Computational Predictions of an NIH Chemical Probes
Presents a computational approach, combining an evaluation from an expert medicinal chemist and machine-learning techniques, to determine whether a set of NIH chemical probes possess desirable drug-like features.

Harold R. Garner, PhD
Lynntech + Virginia Tech
HALO/Gen: A Rapid Method for the Design and Development of Peptide Ligands as Target-Specidic Bio-Markers and Drugs
Explores the HALO/Gen system, which uses the principal of "like binds to like" to design and optimize peptide ligands as drug candidates.

Shahar Keenan, PhD
Cloud Pharmaceuticals Inc.
QM/MM and Inverse Design for Novel Therapeutics Targeting Drug-Resistant pfDHFR-TS Malaria
Shows how QM/MM calculations with the Inverse Design algorithm are used to develop mutation-resistance inhibitors, by designing ligands that are active against 3 known mutations from DHFR from Plasmodium falciparum (malaria).

Paul Kowalczyk
Syngenta Biotechnology Inc.
ADME/Tox Predictions: Using Open-Source Software and Public Data to Build and Depoly Predictive Models for Activity Against Cytochrome P450 Enzymes
Examines the use of eight different machine learning methods and public data to predict ADME/Tox parameters for Cytochrome P450 enzymes.

John Kulp, PhD
BioLeap
Ligand Engineering of PCSK9/LDL-R Protein-Protein Interaction Inhibitors
Describes a novel ligand engineering process with application to the development of small molecule inhibitors of the PCSK9/LDL-R protein-protein interaction. (PCSK9 has the indication of lowering cholesterol.)

John Overington
EMBL-EBI, Wellcome Trust Genome Campus
ChEMBL, SureChEMBL and UniChEM: Open Data for Drug Discovery
Discusses the application of databases, ChEMBL, a database that links chemical structures to molecular targets and assys, SureChEMBL, a database that mines patent literature, and UniChEM, a database that cross-references chemical structures to other databases, for in silico drug design.

Regina Politi, PhD
UNC Eshelman School of Pharmacy
Target-Specific Native/Decoy Pose Classifier to Boost Ligand's Ranking Accuracy for Virtual Screening Application
Describes the implementation of a hybrid docking and ligand pose filtering approach, based on a newly developed pose classifier to choose native-like from decoy poses, for three different targets to obtain a single pose within 1 Angstrom of the x-ray structure.

Wieslaw Swietnicki, PhD
Wroclaw Research Centre, Poland
Identification of Small Molecule Inhibitors of Type III Secretion System ATPase EscN from Enteropathogenic E. coli
Explores computational screening of small-molecule inhibitors and QSAR optimization to determine a compound and five derivatives that inhibit EscN ATPase and block the virulence factor secretion.

Lei Wang
Certara
Multi-Criteria Drug Discovery: Using CoMFA Models to Drive Target Specificity
Examines the method to combine ligand-based scoring functions and 3D QSAR models to determine selective inhibitors.

PANELS

Market Trends for In Silico Drug Discovery
Moderator: Ed Addison, CEO, Cloud Pharmaceuticals
In the past, in silico drug discovery was unsuccessful because of inaccurate and incomplete computational models (docking had low accuracy, selectivity was impossible, compute power was not there, and chemical property modeling was early). In the past 18 months, there have been significant breakthroughs with in silico drug discovery that may overcome many of these barriers. This panel will address the market dynamics for these new and better methods in the face of skepticism from the past, emerging proliferation of genomics, and increasing university involvement in therapeutics research.  The impact of the digital health revolution will be addressed as well as the high cost of ignoring this trend.

Intellectual Property Issues of In Silico Drug Discovery
Moderator: Lawrence Husick, Partner, Lipton Weinberger and Husick
The protection of tools and methods used for in silico drug discovery has been made far more uncertain by recent events. Supreme Court decisions in cases such as Mayo v. Prometheus and CLS Bank v. Alice have called into question the basis of most patents on computer-implemented methods dealing with scientific principles, while guidelines now in use at the Patent Office are causing just about every claim mentioning a computer to be rejected out of hand. This complicates the already-problematic task of claiming libraries of compounds that, in many cases, are related through their properties rather than their chemical structures. Our panelists will address several aspects of this issue that is so critical to insuring that new leads and the methods used to design them may be protected to insure that innovators may continue to fund their development efforts.

About the In Silico Drug Discovery Conference

The In Silico Drug Discovery Conference takes place December 3-4, 2014 at NCBiotech's Hamner Conference Center. The conference combines the best of cutting-edge industry approaches with recent academic research for "in silico" drug discovery. Expert speakers, panelists, researchers, and product vendors will present state-of-the-art computer-aided drug design methods s and case studies in a variety of formats. Visit www.insiliconf.org to learn more, register, and see updates to the agenda.

SOURCE In Silico Drug Discovery Conference