Spring forward to the ACS Conference

March 15th, 2010 by Accelrys Team

Join us at the ACS Spring 2010 Conference, held at Moscone Center in San Francisco on March 21-25, 2010.  At booth #1008, Accelrys will be showcasing the latest developments in Pipeline Pilot, Discovery Studio and Materials Studio.

We have a variety of talks, workshops and posters planned for the conference, including:

TALKS
* High-throughout quantum chemistry and virtual screening for materials solutions
* CAESAR II: The combination of direct geometry method and CAESAR algorithm for super fast conformational search
* Fast and accurate computational approach to protein ionization: Combining the generalized Born model with an iterative mobile cluster method

WORKSHOPS                                                                                                                                                                                                                                                                                                                                                                   * High-Throughput Computational Methods for Materials Discovery and Optimization
* Staying Ahead Of Your Medicinal Chemistry Project Data

POSTER                                                                                                                                                                                                                                                                                                                                                                              * Electronic structure calculations of diamond-like semiconductors

For more information, and to register for the workshops, go to http://accelrys.com/events/conferences/conference-pages/acs-spring-2010.html

If you’d like to learn about the ACS conference on Twitter and see Accelrys’ live tweets, follow #ACS_SF.

 We look forward to seeing you in San Francisco!

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Discover New Materials for Batteries Through Modeling

March 12th, 2010 by Accelrys Team

In the 21st century, materials and energy are more topical than ever before. Insights at the atomistic and quantum level help us to design cleaner energy sources, and find less wasteful ways of using energy. Join us on March 16th as Dr. George Fitzgerald presents “High-throughput Quantum Chemistry and Virtual Screening for Lithium Ion Battery Electrolyte Materials.”

Register to learn:

  • How modeling can support the discovery of components to enhance the performance of lithium ion battery formulations
  • How to use Materials Studio components in Pipeline Pilot to analyze and screen a materials structure library for Li-Ion battery additives
  • Results from a collaboration with Mitsubishi Chemical Inc which was also published in The Journal of Power Sources

This presentation is part of our ongoing webinar series that showcases how Accelrys products and services are transforming materials research. You can download related archived presentations in this series or register for future webinars.

We look forward to sharing our insights with you throughout this webinar series.

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Nanotechnology, Alternate Energy and Virtual Screening…oh my!

March 9th, 2010 by Lalitha Subramanian, PhD

It was indeed very pleasant to visit the Stanford campus last week; I had a chance to see familiar faces, as well as new ones, amongst the attendees at the workshop, “Bridging the Gap Between Theory and Experiment: Which Theoretical Approaches Are Best Suited To Solve Real Problems In Nanotechnology and Biology”

There were several invited talks on semiconductors and catalyst nano particles, apart from my talk on alternate energy.  Many of the speakers discussed the suitability of a particular simulation approach for the study of specific applications, while others discussed the most recent state-of-the-art theoretical advances to tackle real problems at several timescales.  It is particularly challenging when simulations are to be used not just for gaining insights into a system but to be a predictive tool as well as for virtual screening.  While virtual screening is a well-studied art in the world of small molecule drug discovery, this is only now gaining traction in the materials world.

For further inight into virtual screening in materials, check out George Fitzgerald’s webinar on High-throughput Quantum Chemistry and Virtual Screening for Lithium Ion Battery Electrolyte Materials, next Wednesday, March 16.

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A Man with 5 Rules

August 12th, 2009 by Nancy Miller Latimer, M.S.

Chatting with Christopher Lipinski at Drug Discovery & Development Week

Some ten years ago, I first “met” the Lipinski rules in a software project.  That was my last direct “hands-on” encounter with chemistry.  At Accelrys I am the senior product manager for the Biosciences and Analytics Collections for Pipeline Pilot.  Think genomics, proteomics, sequencing, and ontologies and not chemistry!  This week I was at the DDDW show in Boston – don’t think “booth babe”.

The conference was not as busy this year as it had been in the past and it was the afternoon of the last day.  A distinguished gentleman walked up to our booth wearing a name tag of “Christopher A. Lipinski,” happy to see a fellow booth dweller.   Half in jest I asked if he might be the man with 5 rules.  Turns out he was and, boy, I was in for an intellectual treat.   That Lipinski filter came to life in a new way over the next hour or so.  I was spell bound by Dr. Lipinski’s breadth of knowledge, passion for science, and his out of the box thinking.  What I didn’t anticipate were his insights into the importance of chemistry for the biomarker and translational research space.

He was saying some really awesome things so I started writing them down.    It was hard to focus on note taking because Dr. Lipinski is an excellent speaker and very animated.  Below are a few items that I am willing to share in no particular order:

  • Translational research must have good chemistry married to good biology.
  • Your company (Accelrys) combines chemistry and biology in one software application.  If biologists are using your software to look at high throughput screening (assay) data that has associated chemical structures, they could better filter out results for poor compounds.
  • When faced with people problems (like chemistry—biology conflicts) versus technical problems—the people problems are always much more difficult to solve.
  • The people side is the most important.
  • NIH is making good strides in the dialog between chemists and biologists.
  • As soon as the biologist has an assay for a small molecule they should probe/stress test the assay with compounds known historically to cause assay problems.
  • In software for the (bench) biologist – it needs to be dead easy.  Too many peer-reviewed publications have great biology but rotten chemistry.
  • Biologically active compounds are tightly clustered in chemical space.  It is always best to look for new activity in areas of chemical space where you previously found activity.
  • It takes 10 years to “mature” a medicinal chemist.  He then becomes an expert in pattern recognition even if he can’t articulate why certain structures look better than others
  • Areas of interest
    • Stem cell (non-embryonic source)  derived screening application
    • Many previously proprietary databases are now in the public domain  (See PMID:  17897036).  These provide a great starting point for the discovery of drugs for rare diseases.

Dr Lipinski’s long and prestigious career in medicinal chemistry, assay development, computational chemistry, and now in consulting, lecturing, and as an expert witness does not look anything like retirement.  That is good news for me.

Dr. Lipinski is shown here with his rapt audience.

Dr. Lipinski is shown here with his rapt audience.

Note: Lipinski’s total number of rules actually equals 4.  His rules are known as the “Rule of Five” because each of them incorporates the number 5 in some way.  For all you literalists out there, “5 Rules” should be interpreted in this way.

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Overlaps and Crossovers at the European User Group Meeting

August 11th, 2009 by Gerhard Goldbeck-Wood, PhD

We announced the European User Group meeting a few days ago. Check out the UGM webpages, and especially the themes.  I am excited that we’ll have users from all product and application areas together, including:

• Materials Studio

• Discovery Studio and Platform

• Training sessions and the lot.

We’re in different tracks, but I expect to see some interesting overlaps/crossovers.  For example, we’ll discuss the high throughput methods in materials to the platform, along with what we can learn from the collaborative environments and custom solutions in the Discovery Studio field for other areas such as Materials.

Also, I’ll be hosting the Annual meeting of the Nanotech Consortium on the Tuesday/Wednesday of that week, where we’ll discuss the latest, especially in the field of kinetic modelling of reactions. Take a look at my previous post on that subject.

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Biogenic Bias: A Good Kind of Bias

July 15th, 2009 by Dana Honeycutt, Ph.D.

In statistical analysis, we usually try to avoid bias. But in high-throughput screening (HTS), bias may be a good thing. In fact, it may be the reason that HTS works at all.

In his In the Pipeline blog, Derek Lowe discusses a new paper from Shoichet’s group at UCSF, entitled “Quantifying biogenic bias in screening libraries.” The question is this: Given that the number of possible organic compounds of reasonable size approaches the number of atoms in the universe (give or take a few orders of magnitude), and that an HTS run screens “only” a million or so compounds at a time, why does HTS ever yield any leads? The short answer, as the authors show, is that HTS libraries have a strong biogenic bias. In other words, the compounds in these libraries are much more similar to metabolites and natural products than are compounds randomly selected from chemical space.

The authors used Pipeline Pilot for much of their analysis, including ECFP_4 molecular fingerprints for the similarity calculations. See the paper and Derek Lowe’s blog entry for more.

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Informatics lessons from the MRS

May 19th, 2009 by George Fitzgerald, PhD

The Materials Research Society (MRS) “encourage communication and technical information exchange across the various fields of science affecting materials.” It sponsors a spring meeting held in San Francisco and a fall meeting in Boston. This year’s spring meeting covered topics ranging from amorphous materials to methods for environmental stability to multiple topics in nanotechnology. (See all symposium titles here.)

Most interesting to me was Symposium Z: “Computational Nano science — How to Exploit Synergy between Predictive Simulations and Experiment, which fits with the comments I made in my previous posting, and shows just how much active interest there is in this topic. Prof. Krishna Rajan, who heads the Combinatorial Sciences and Materials Informatics Collaboratory, demonstrated how he uses data mining as a tool to understand the formation of apatites (minerals of the form A10­(BO4) 8X2) based on data mining and statistical analysis. How do you get your head around and N-dimensional space? How do you grasp trends when there are dozens of variables to consider? Use methods like recursive partitioning and Principal Component Analysis (PCA). 

Simpler than the modeling approaches I mentioned in my earlier posting, these require only a statistical analysis of the data (some experimental results, some modeling output). The results reduce N-dimensional datasets to 2 or 3 dimensions that are “grasp-able” by mere humans. Applying these approaches to the apatite data clearly shows how the choices of cation and anion influence the stability of the crystal.

Just think how many other research problems we could understand if we had the tools to look at the data in the right way.

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High Throughput – What’s a Researcher to do?

May 12th, 2009 by George Fitzgerald, PhD

High-throughput experimentation has been a mainstay in pharmaceutical discovery since the mid-1990’s. In a 1999 C&E News article (C&EN, vol. 77, pp 33-48 March 8, 1999) this approach was hailed as the next great thing. Unfortunately, we chemists soon realized that quantity is no replacement for quality; a notable article in the WSJ “Drug Industry’s Big Push into Technology Falls Short,” was critical of this approach.

 

At the time, I was working on a DOE-funded project (DE-FC26-02NT41218) for high-throughput catalyst discovery for NOx catalysis in lean diesel engines, together with GM and Engelhard (now BASF). In practice, our method was not to generate 1000’s of samples and hope for the best but to screen fewer carefully selected samples quickly, and subject the “winners” to more sophisticated testing.

 

The approach employed in our NOx project was based on analysis of experimental data, design of experiment, and fitting response surfaces – and it worked. As pointed out in a recent BIOIT World article, however, experimental data alone are usually too noisy to build reliable statistical models. What’s a researcher to do? Molecular modeling, of course – hey I’m a modeller: you knew I was going to suggest that.

 

The key for success, it seems, is to employ a plurality of methods, both experimental and computational. Given even a modest amount of experimental data, you’ll need a database with decent search & query tools and basic statistical approaches like principle component analysis. But atomistic modeling is also important. Work by a number of research groups has shown that you can generate good predictive models from quantum mechanical methods (QM) for lots of different kinds of materials. (Keep in mind that these examples barely scratch the surface of the available literature).

 

But how do get to the point that anybody can make use of QM-based results? Doing these calculations typically takes a log time.

 

QSAR (Quantitative Structure Activity Relationship) is a terrific way to leverage QM results for complex research topics. These research groups followed the same basic procedure:

  • Start with some experimental data
  • Generate a statistical model
  • Grind through a lot of calculations
  • Forward the “winners” for experimental testing

 

You can see in the examples above that the approach can actually work. But how do you figure out what QM calculations to perform, and how do you create good statistical models? Well, that’s a story for next month.

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