John Van Drie's research in Computer-Aided Drug Discovery

Computer-aided drug design, including structure-based design, pharmacophore discovery, and 3D database searching

CADD - the next twenty years

This was the title of a talk I gave at the Mar, 2007 ACS meeting. It was recorded, and both the Powerpoint and the audio are available at the CINF web site.

This has also appeared in print in JCAMD.

Structure-based design

My current interest is in understanding the non-additivity often seen in structure-activity relationships. The most striking example is seen in the SAR of HCV protease inhibitors, in which, for example, the replacement of a P3 isopropyl by a cyclohexyl can sometimes yield a 16-fold boost in potency, while sometimes it has no effect. This was seen at Vertex (see RB Perni et al publications) on scaffolds that differed very slightly, in the P1' region.

My initial attempt to understand that was to extend the lattice model of Miller and Dill. At some point, that manuscript-in-preparation "Non-additivity in structure-additivity relationships and molecular recognition", will be polished and submitted. There I was guessing that binding site side-chain flexibility was the primary source of non-additivity. The lattice model suggests this is only a modest contributor to the overall non-additivity.

I have a new speculation about what may be the dominant source of non-additivity in SAR's, and am exploring that. I'm scheduled to present that work at the Boston ACS meeting in August, 2007.

Non-additivity is also interesting in that it may be the answer to something I've long been searching for. As I've commented in print a number of times, any drug designer has an intuitive sense that some SAR's are "easy", some are "difficult", with all variations in between. I've long wished to have a way to calibrate that more precisely, much in the way that white-water canoeists rate the difficulty of different rapids. I've developed a single number which characterizes the degree of non-additivity in a SAR. This metric appears to track with one's intuitive perception of how difficult an SAR is, and may be one way of accomplishing this rating. When the non-additivity is high, our modelling methods tend to perform poorly, and medicinal chemists' intuition tends to fall down.

Pharmacophore discovery

These methodologies are also called 'pharmacophore identification', 'pharmacophore perception', or 'receptor mapping'. The first commercial software for pharmacophore discovery was the Catalyst software, of which I was one of the co-authors. (DISCO is often cited as the first, but that appeared from Abbott only after they'd seen initial versions of Catalyst. DISCO did indeed appear in the literature before Catalyst). Catalyst's approach to pharmacophore discovery has numerous weaknesses, which I've addressed in my latest work on pharmacophore discovery, DANTE, which is described in the following papers.

J.H.VanDrie, "An inequality for 3D database searching and its use in evaluating the treatment of conformational flexibility", J. Comp.-Aided Mol. Design, 1996, 10, 623.

J.H.VanDrie, "Strategies for the determination of pharmacophoric 3D database queries", J. Comp.-Aided Mol. Design, 1997, 11, 39.

J.H.VanDrie "'Shrink-wrap' surfaces: A new method for incorporating shape into pharmacophoric 3D database searching", J. Chem. Inf. and Comp. Sci., 1997, 37, 38.

J.H.Van Drie and R. A. Nugent, "Addressing the challenges of combinatorial chemistry: 3D databases, pharmacophore recognition and beyond ", SAR and QSAR in Env. Res, 1998, 9, 1-21.

I recently published a review in Curr Pharm Design, 2003, 9(20):1649, and also have a book chapter reviewing pharmacophore discovery: Computational Medicinal Chemistry and Drug Discovery, Tollenaere Bultinck de Winter and Langenaeker (eds.), NY: Dekker.

Here is a script and a small C program which one can use with the commercial Catalyst software to implement the Mayer et al method, the algorithm at the heart of DANTE:

AWK script to extract and format info from Catalyst log
C program to process output of AWK script
Brief documentation on this

3D database searching

I developed the ALADDIN software, collaborating with Yvonne Martin at Abbott Labs. That pioneered a new approach to 3D database searching, and was used in the first success for virtual screening, the discovery of a novel template for D1 agonists.

3D databases on the desk of the medicinal chemist (proceedings of Johnny Gasteiger's Hochfilzen conference)
3D databases in drug discovery (Netsci)

Link to PubMed citations

Click here

Contact me

Those interested in anything related to matters above: send me an e-mail and let's initiate a dialogue.

History

My abbreviated work history is:

Independent consultant in drug discovery, Cambridge, MA, 2007 -
Director, CADD, Novartis Institutes for BioMedical Research, Cambridge, MA, 2004-2007
Sr. Scientist, Modelling, Vertex, Cambridge, MA, 2001-2004
Sr. Scientist, CADD, Pharmacia (nee Upjohn), Kalamazoo, MI, 1994-2001
Sr. Scientist, BioCAD, Mtn. View, CA, 1990-1994
Independent consultant in computational chemistry, Chicago, IL, 1985-1990

I was Chair of the first Gordon Research Conference on Computer-Aided Drug Design, in 2003.

My graduate work was in theoretical chemistry at Caltech in the 1970's; I did my undergraduate work in chemistry, math, and physics at Wabash College in Indiana.

Definition of the term pharmacophore

The material that was here for many years has now been moved to a refereed journal, in a 2007 article by me in the IEJMD, an issue dedicated to Monty Kier.

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