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FUJiFILM LifeSciences

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Bio-IT World

Science AAAS

The Scientist


R&D Directions







Friday, June 5

7:30 am Breakfast Presentation (Opportunity Available) or Morning Coffee

(Sponsorship Available. Contact Katelin Fitzgerald at or 781.972.5458)

The Race for Chemical and Biology Space

8:20 Chairperson’s Remarks

José Duca, Ph.D., Senior Principal Scientist, 3D - Drug Design Department, Schering-Plough Research Institute



8:30 The Race for Chemical and Biological Space: A Drug Design Perspective

Tomi Sawyer, Ph.D., Chief Scientific Officer, AILERON Therapeutics; Editor-in-Chief, Chemical Biology & Drug Design

Ligand-Based Predictions
Applied to Drug Design

9:00 Predicting the Activity of Congeneric Series

W. Patrick Walters, Ph.D., Senior Research Fellow, Computational Chemistry and Molecular Modeling, Vertex Pharmaceuticals, Inc.

Over the last 20 years, a number of groups have developed scoring functions to estimate the magnitude of protein-ligand interactions and prioritize molecules for synthesis or screening. The vast majority of these scoring functions have been validated based on diverse sets of molecules spanning a large (6-10 log) range of activities. While these validations have some relevance, they do not necessarily reflect the realities of pharmaceutical drug discovery. In order to be useful on a typical lead optimization project, a scoring function should be able to rank order congeneric series where the activities span a 2-3 log range. In an effort to identify functions that would be useful for lead optimization, we have carried out a validation study using 3 large sets (400-600 members) of congeneric compounds taken from drug discovery projects.

  • We evaluated the ability of a number of widely used scoring functions to predict relative binding affinities.
  • In addition to using the scoring functions as developed by the original authors, we also used a machine learning approach to tailor the functions to a particular target.
  • In order to provide a more realistic estimation of performance, we employed a novel temporal cross validation strategy which closely models the evolution of a targeted scoring function in the course of a drug discovery project.

Sponsored by
Tripos NEW

9:30 Designing Drugs against Multiple Parameters: Scoring Functions for Multi-Parameter Ligand Based De Novo Design

James R. Damewood, Principal Scientist II, AstraZeneca Pharmaceuticals

Successful drug discovery often requires optimization against a set of biological and physical properties. We describe our work on multi-parameter approaches to ligand-based de novo design and studies that demonstrate its ability to successfully generate lead hops or scaffold hops between known classes of ligands for some example receptors. Multiple design criteria, including pharmacophoric similarity, shape similarity, structural (fingerprint) similarity can be employed alongside various selectivity or ADME related properties to guide the evolution of structures which meet multiple design criteria.

Sponsored by

10:00 SPR-Based Screening of Fragment Library and Follow-up of Hits

Girija Krishnamurthy, Ph.D., Principal Research Scientist/Group Leader, Wyeth Research

10:15 Networking Coffee Break, Poster and Exhibit Viewing

11:00 Improving the Quality of Structure-Based Virtual Screen by The Interaction-Focused Post-Docking Analysis

Suo-Bao Rong, Ph.D., Senior Principal Scientist, Medicinal Chemistry – Antibacterials and Neurosciences Computational Chemistry, Pfizer Global R&D

Co-Author: Brian S. Bronk, Ph.D., Senior Director, Medicinal Chemistry – Antibacterials and Neurosciences Computational Chemistry, Pfizer Global R&D

A semi-automatic post-docking protocol, including the key interaction identification, interaction similarity analysis, binding mode clustering, pocket occupancy comparison, and chemotype clustering, has been developed to further improve the hit rate and especially the hit-to-lead success rate for structure-based virtual screens. Key features of the protocol include its flexibility and effectiveness to explore the diversities of both ligand-protein interactions (binding modes) and ligand structures. This method makes it practical for a designer to use experimental SAR to narrow a large number of compounds by visualization as the last step in selecting a small number of representative VS hits for submission to the bioassay. In this way, it is feasible to examine multiple potential directions derived from structure-based virtual screening, thereby increasing the probability of impact on successful hit-to-lead optimization. The protocol has succeeded in several different targets to achieve ~10% hit rate and particularly identify a series of diverse novel scaffolds successful in the hit-to-lead optimization.

11:30 Fragment-Based Drug Discovery (FBDD)

Dr. Richard J. Law, Computational Chemistry Group Leader, Evotec UK Limited

Fragment-based drug discovery (FBDD) represents a change in strategy from the screening of molecules with higher molecular weights and physical properties more akin to fully drug-like compounds, to the screening of smaller, less complex molecules. This is because it has been recognised that fragment hit molecules can be efficiently grown and optimised into leads, particularly after the binding mode to the target protein has been first determined by 3D structural elucidation, E.g. by NMR or x-ray crystallography. Several studies have shown that medicinal chemistry optimisation of an already drug-like lead compound, results in a final compound with increased molecular weight compared to the starting structure. The evolution of a lower molecular weight fragment hit may represent an attractive approach to optimisation. Computational chemistry can play an important role in evolution of a drug-like molecule from a fragment hit, both with and without the available fragment-target co-complex structure.

12:00 pm Entropy, Solvation and Strain Energy – the Stumbling Blocks in Binding Free Energy Calculations

Enrico O. Purisima, Ph.D., Group Leader, Computational Chemistry & Bioinformatics, Biotechnology Research Institute, National Research Council of Canada

An accurate and robust scoring/free energy function is essential for virtual screening and structure-based drug optimization. Entropy, solvation and strain energy are three critical components of binding free energies in protein-ligand complexes. The challenge is to incorporate these terms in an energy function without introducing more noise that drowns out the signal. In this talk, we will examine the magnitude of the contribution of these components to binding free energies. We will describe the use of exhaustive docking to build up a partition function from predominant states. We will illustrate the use of continuum electrostatics and continuum van der Waals methods for solvation contributions.

12:30 Luncheon Technology Presentation (Opportunity Available) or Lunch on Your Own

(Contact Katelin Fitzgerald at or 781.972.5458)

SBDD and Computational
Chemistry Methods:
A Marriage of Innovation

1:25 Chairperson’s Remarks

Tomi Sawyer, Ph.D., Chief Scientific Officer, AILERON Therapeutics; Editor-in-Chief, Chemical Biology & Drug Design

Sponsored by
Chemical Computing

Poster Award Announcement

1:30 Methods for Structure-Based Scaffold Replacement

Paul Labute, Ph.D., President, Chemical Computing Group (CCG)

Small molecule scaffold replacement techniques are an important part of drug discovery because of the need to find rapid “follow on” compounds or alternate series. Fragment-based drug discovery techniques also benefit from scaffold replacement methods because of the need to link fragments that bind to a receptor. We present methods for structure-based scaffold replacement that combine techniques from pharmacophore discovery and ligand receptor docking. Strategies for the creation of 3D virtual fragment databases are discussed as well as the results of computational experiments. 

Sponsored by
Schrodinger logo 
2:00  Application of Free Energy Perturbation Calculations in Drug Discovery
Woody Sherman, Ph.D., Director Applications Science, Schrodinger 

 The accurate prediction of binding free energies has been a primary objective for computational methods since the inception of molecular modeling and computer-aided drug design. In this work we describe a large-scale free energy perturbation (FEP) project designed specifically to make FEP practical in drug discovery. We first describe validation of the methodology using the Desmond molecular dynamics program and the OPLS force field by computing absolute and relative solvation freeenergies of a diverse set of small molecules. We then present a large set of pharmaceutically relevant targets and compounds that are being used for the prediction of relative binding free energies. Finally, we present preliminary results for relative binding free energy predictionsand discuss the implications for FEP in drug discovery.


 2:15 Networking Refreshment Break, Poster and Exhibit Viewing

3:00 Structure-Based and Ligand-Based Computational Modeling of Pharmacological Profiles

Ajay N. Jain, Ph.D., Professor, Cancer Research Institute & Department of Lab Medicine, University of California San Francisco

The current state-of-the-art in drug discovery is largely dominated by incremental advances, with me-too drugs forming a very substantial fraction of the small molecule therapeutics market. Such drugs frequently yield no practical benefits in terms of therapy. Apart from drugs that target rapidly evolving organisms, justification for me-too design is problematic outside of narrow economic grounds. Coupled with a shifting regulatory environment that is changing the economic arguments, the need for truly novel drugs is increasingly apparent. This offers an opportunity for computational methods to have a larger impact than has been the historical norm. A central intellectual challenge is that complex issues involving human inductive bias over the course of drug design history make it difficult to demonstrate the success of computational approaches. Despite the challenges, computational approaches can yield predictive models that support design of novel therapeutics. A unified approach to addressing protein-structure based modeling (docking) as well as ligand-based modeling will be presented.

3:30 Protein-Ligand Docking against Non-Native Protein Conformers

Marcel Verdonk, Ph.D., Director, Computational Chemistry & Informatics, Astex Therapeutics, Ltd.

Docking performance is mostly measured against native protein conformers, i.e. each ligand is docked into the protein conformation from the structure that contained that ligand. In real-life applications, however, ligands are docked against non-native conformations of the protein, i.e. the apo structure or a structure of a different protein-ligand complex. We will present the construction of an extensive test set of non-native protein conformers for a range of drug targets. In addition, we will discuss the effects of docking against non-native protein conformations on docking performance, as well as the usefulness of multiple-conformer docking protocols.

  • Definitive insights into
  • Performance of rigid-protein docking against non-native protein conformations
  • Performance of multiple protein conformer docking protocols
  • Insights into the unique informatics set-up at Astex

4:00 Binding Site Detection and Druggability Index from First Principles

Xavier Barril, Ph.D., ICREA Research Professor, Fisicoquimica, Facultat de Farmacia, Universitat de Barcelona

Both X-ray crystallography and NMR experiments have demonstrated that small organic solvents can be used to identify specific binding sites on protein surfaces. This effect is reproduced using molecular dynamics with an explicit solvent mixture, which gives us direct access to interaction free energies between the protein and small organic molecules. On a set of proteins of pharmacological interest, we show that the method can detect not only typical small-molecule binding sites, but also protein-protein and low affinity binding sites. Furthermore, by adding the interaction free energy of organic solvent binding sites that cluster together, it is possible to predict the maximal affinity that a drug-like molecule could attain, thus providing a measure of druggability.

4:30 Closing Remarks

4:45 pm Close of Conference

Day 1