Dear Colleagues:

Attached is the summary of responses for my query. The original = query and=20 the 5 responses follows:

Original query-------------

Dear Friends:
While several QSAR related techniques and = methodologies=20 are appearing in drug
design Journals, very few talk about the more = accurate=20 quantum chemical
methods in drug design arena.

* What are = the=20 bottlenecks for the quantum chemical methods to get into the
area of = drug=20 design.

* For small molecules QC methods plays greater role, but = for=20 handling drug-
like molecules and handling several thousands of = compounds, QC=20 methods do not
see the limelight (correct me if i am wrong). Is the=20 CPU-intensiveness alone
is the reason. Or is there any some = conceptual gap=20 in this. [ I hear someone
saying CPU-intensive is the reason and one = has to=20 wait for months to get
results ]

* Based on your = experience/insight,=20 can you think of some timeframe, say 5
years, 10 years down the = line,=20 Quantum chemical methods would play a major
role in the area of lead = identification/optimization, or would you
say "prediction of future = is=20 difficult!".

I look forward to reading your views. = Thanks.

S.=20 Parthiban
Jubilant Biosys Ltd.
http://www.jubilantbiosys.com<= BR>
Summary=20 of responses:---------------------------

From: "Dr. N. SUKUMAR" <nagams@rpi.edu>

The CPU bottleneck has certainly been a major factor, but I believe = a
second factor is the descriptors commonly employed in drug design. = If=20 all
one uses in modeling are molecular-geometry-derived descriptors,=20 atom
counts, topological descriptors and electrostatic potentials, = then=20 it
hardly seems worthwhile performing accurate quantum chemical=20 computations,
especially in view of the enormous computational = overhead. Ab=20 initio
cmputations, however, can generate a lot more information at a = fundamental
level, derived from the molecular wavefunction or = electron=20 density
distribution. There are a few research groups (ours among = them) that=20 have
investigated the use of electron-density-derived descriptors in=20 drug
design. In the Transferable Atom Equivalents (TAE) method, first = introduced
by Curt Breneman, we employ besides electrostatic = potentials,=20 electronic
kinetic energy densities, the Laplacian distribution = introduced by=20 Bader,
Fukui's function and Politzer's local average ionization = potential.=20 The
distributions of these electronic properties on the molecular van = der=20 Waals
surface (binned as histograms or encoded as wavelets) are used=20 as
descriptors. These electron-density-derived descriptors have found = success
in a number of applications, especially when used in = combination with=20 other
traditional descriptors. For small datasets of small=20 molecules,
electron-density-derived descriptors can be readily = determined=20 > from ab
initio computions, but for large pharmaceutical datasets and=20 for
macromolecules, these descriptors can still be computed from=20 an
atomic-fragment-based approach using the theory of Atoms In = Molecules.=20 This
is done in our RECON program, which employs atomic descriptors = computed=20 at
HF/6-31+G* level and is available for download from our website.=20 Typical
CPU timings for RECON on a 1.7GHz Intel Pentium under linux = are about=20 90
sec.for a set of 25 proteins and 7.5 min.for a 42,689 molecule = dataset=20 from
NCI -- comparable to times for computing topological = descriptors. So=20 I
would have to say that for such applications, CPU is no = longer a=20 limiting
factor.

Our protein chromatography studies are = published in=20 Mazza, et al,
Anal.Chem. 73, 5457-5461 (2001) and Song, et al,=20 J.Chem.Inf.Comput.Sci. 42,
1347-1357 (2002), while the drug design=20 applications are in various stages
of going to press and in = press.

Dr.=20 N. Sukumar
http://www.drugmining.com/
Ren= sselaer=20 Department of Chemistry

From: "Patrick Bultinck" <Patrick.Bultinck@rug.ac.be= >

Dear,

This is a very interesting question indeed. And as a = matter of=20 fact, there
is now in press a volume "Computational Medicinal = Chemistry and=20 Drug
Design" in press edited by two scientists working in=20 pharmaceutical
industry and one professor of Utrecht University and = myself.=20 In this volume
a number of quantum chemical techniques = (semi-empirical=20 theory, wave
function theory, DFT, QM/MM, accuracy and applicability = of=20 methods, ...)
are described in quite some detail by eminent=20 contributors.

Having some experience with computational medicinal = chemistry focussing on
quantum chemistry, I would say that there are = a number=20 of different reasons
for quantum chemistry not always breaking = through in=20 drug design at the
speed one might expect. Here is my 0.02 eurocent = worth=20 :

- CPU demands are only a part of the problem but an important=20 (and
expensive) one. This does not necessarily mean that drug = molecules are=20 too
big. With current codes and cpu power, one could handle several=20 hundred
atoms with say Hartree-Fock. Moreover, linear scaling = techniques=20 allow us
to go even further with DFT, Hartree-Fock, local MP2, ... = One must=20 also
realize that many medicinal molecules are not so very big. After = all,=20 we
would like to get them in the blood and tissues, and so usually = drugs=20 are
not so big. Also remember that quantum chemistry is most often = used=20 to
design ligands and candidate-drugs, rather than actual drugs. QC = plays=20 a
role in the development steps. The drug is the thing you buy at = your=20 local
pharmacy, but this undergoes still quite a number of steps when = leaving=20 the
QC desktop (think of optimization for synthesis, clinical=20 testing,
solubility, ...). The CPU problem will often arise when more = accurate
techniques are needed, say some correlation level = calculation is=20 needed.
But a different problem is that sometimes the QC method does = not=20 scale too
dramatically, but the amount of data is too big. Think of = some=20 virtual
screening guy walking in and asking to calculate 10^6=20 molecules...

- There is sometimes a problem with what the qc = results=20 would mean. Suppose
you work out a conformational analysis on some = nice level=20 of theory and a
good basis set. What does this mean from a medicinal = point of=20 view ? Pretty
often, one observes that a critical structure known as = the=20 bio-active
conformation is not known. This then yields (sometimes) = the=20 conceptual
problem of "what do these results mean ?". In very simple = student=20 terms, I
would compare this to a simple equation A=3DB, and you need = to solve=20 for A
but do not know what B is.

- There are some problems = related to=20 the fact that the human body is not
composed of solvent free = molecules under=20 a wealth of approximations
(harmonic oscillator, rigid rotor, ideal = gas, ...=20 to name a few). This
means one needs to have the quantum chemical = methods=20 simulate in a better
way actual "drug circumstances". One needs to = model=20 solvents and
solute-solvent interactions for example. Many methods = have been=20 developped,
and conceptually the best and simplest one is explicit = solvent=20 modelling.
One then is confronted again with cpu time making this = model often=20 useless
in practice. Other models may also give rise to extra CPU = demand, or=20 may be
too crude in their approximations.

- A fairly social = reason is=20 sometimes (!!) the reluctant attitude of
(organic) chemists with = respect to=20 quantum chemistry. Although there
clearly are chemists which are = bright in=20 organic synthesis and quantum
chemistry, often quantum chemistry is = still not=20 considered on an equal
footing as the synthesis department. If then = you have=20 had to struggle to
get two quantum chemists in your industry, you can = not=20 expect these people
to make such a progress that they can easily = convince the=20 200 other R&D
chemists that they will open the way to a new era = in drug=20 design.

- There are still some conceptual problems to be solved, = or=20 rather : there
still are a lot of conceptual problems to be solved. = As a=20 quantum chemist
we want to retrace things to wave functions/electron = density=20 as much as we
can. These are the all determining properties, and so = we would=20 like to
derive even e.g. QSAR models from these properties. Still = much work=20 is
needed in this, but there are advances all the time. The field of = QSAR=20 is
in fact a good example. QSAR is often done from known or tabulated = (and
rule or fragment based combinations of) properties of a = molecule,=20 quite
often experimental properties. Quantum chemistry has entered = this field=20 by
allowing the calculation of properties which may not be accessible = to=20 the
experimentalist. Mati Karelson (also contributor to our volume) = has=20 written
a chemical review on this topic, describing many observable=20 and
non-observable quantities of use in QSAR. Still deeper in the=20 application
of QC in QSAR is the field known as quantum-QSAR. This = field,=20 largely
developed by Carbo-Dorca (http://iqc.udg.es) derives QSAR models=20 from
purely quantum chemical ideas. Such an approach is quite = interesting,=20 but
often one needs to clear some "simple-looking" things like how to = express=20 a
thing like molecular similarity (the basis of quantum QSAR). This = is a=20 very
exciting area which will probably make it to the qc desktop in=20 relatively
near future.

- Experimental techniques are being = developed=20 that make intensive use of
QC. As an example one can consider some=20 spectroscopic techniques. Some
techniques give spectra which can = hardly be=20 interpreted "on sight", and
require some QC predicted spectra to = decide what=20 of the possible products
is actually present in the = solution.

Now here=20 is my 0.01 eurocent worth opinion on what way QC may break through
in = drug=20 design.

- With even increasing cpu power, and current = developments in=20 algorithms,
concepts etc., we will no doubt see an increase in the = use of QC.=20 My first
computer (when I was 11 years old and very chemistry = ignorant) was=20 a
Commodore 64. I am pretty sure you would have a hard time = implementing=20 even
simple models on these machines to work at a speed that would = convince=20 drug
design chemists. Now look at where we are with PC's. A good PC = can do=20 quite
some QC work nowadays. This evolution is likely to continue = still, so=20 the
problem of CPU requirements will reduce (that is: if we keep=20 considering
similar molecules and similar QC models).

- There = are=20 exciting new areas that may contribute a lot to the use of QC
in drug = design.=20 Personally I am quite fascinated by conceptual DFT. These
may yield=20 reactivity descriptors that can help you interpret/predict
reactivity = orders=20 and many more. Such areas may even be implemented
sometimes in highly = efficient ways. We have recently published a number of
contribution = on=20 Electronegativity Equalization aimed at use in drug design.
This = method may=20 yield many reactivity descriptors for about a million
molecules/hour = on a=20 Pentium III machine. Such advances are bringing QC to a
speed that = e.g. the=20 virtual screening guys can live with. Another example I
work in with=20 Carbo-Dorca is the field of molecular similarity and quantum
QSAR. = Quantum=20 QSAR models are working (not always) as good as classical (2D
or 3D) = QSAR,=20 but are based on purely electron densities.

So I think we are = facing a=20 nice future for QC in drug (ligand) design, and
it is already there. = But on=20 the other hand, it is not yet on an equal
footing as the many other = aspects=20 of drug design, and it would be naive to
think it will be next week. = I am,=20 however, sure that it will continue to
develop at an ever increasing=20 speed.

Best regards,

Patrick Bultinck
Quantum = Chemistry=20 Group
Ghent University
Belgium

From: "Dr. Andreas Klamt" <andreas.klamt@cosmologic.de>

My view of this topic is as follows: I am afraid, that realistic = modeling=20 of drug receptor interaction is still too demanding
for quantum = chemical=20 methods, because usually the enzyme is too large, and in addition = a lot of=20 conformations and many
compounds would have to be sampled. =

The=20 situation is different if you consider the ADME part: Here we can do = good=20 calculations of solubility and many kinds of
physiological = partitioning=20 properties quite efficiently using DFT methods. The advantage compared = with=20 force field models is
that you can get much better insight into the = real=20 physics using quantum chemistry. For examples see:

http://www.cosmol= ogic.de/water_solubility.html

or
"COSMO-RS:=20 a novel view to physiological solvation and partition questions", = Andreas Klamt,=20 Frank Eckert and Martin Hornig,
Journal of Computer-Aided = Molecular=20 Design 15, 355-365 (2001)
and
"Prediction of aqueous solubility of = drugs=20 and pesticides with COSMO-RS", Andreas Klamt, Frank Eckert, Martin = Hornig,=20 Michael E.
Beck and Thorsten B=FCrger, J. Comp. Chem. 23, 275-281 = (2002)

From: "Peter Gannett" <pgannett@hsc.wvu.edu>

I would say the problem is more complicated than just the amount of = time=20 required for a QC calc/compound. For example, you can optimized a = compound=20 geometry all you want but it is not necessarily the geometry adopted = when the=20 molecule is in the active site of an enzyme so there is not much = use. =20 Second, there are simple (minded) methods that clearly ignore a large = number of=20 interactions and still come out with useful information. CoMFA is = an=20 example of this. A rather simple set of compounds (training set of = say 20=20 compounds) provides you with a reasonable ability to predict how to=20 modify/improve on a drug's activity. It has proven to be a fairly = powerful=20 method though computationally, it is very inexpensive. So, it = think the=20 bottom line here that QC will not play an important role until it can be = demonstrated that there are compelling reasons to implement = it.

Pete=20 Gannett

From: "Leif Norskov" <lnl@novo.dk>

Dear S. Parthiban.

In my opinion = one simply=20 cannot calculate properties that are
directly relevant for drug = design by=20 quantum chemical methods.
Waiting 5 or 10 years wouldn't change=20 much.

However, there may be specific cases where one can find=20 (empirically)
that there is a correlation between biological activity = and
some electronic property such as a HOMO-LUMO energy = difference.
In=20 such cases QM can already today be useful.

Disclaimer: My=20 scepticism towards quantum chemistry has prevented me
from ever = trying - the=20 opinion expressed above is pure fiction.

Best = regards,

/Leif=20 Norskov
Novo Nordisk A/S
LNL@novo.dk

From: "Jeremy R. Greenwood" <jeremy@compchem.dfh.dk>

Hi Parthiban,

Interesting = questions.=20

> While several QSAR related techniques and methodologies are = appearing in drug
> design Journals, very few talk about the more = accurate quantum chemical
> methods in drug design arena.=20

Few, but some. I do it because I'm interested in = fine-tuning=20 hydrogen
bonding to substituted heteroaromatics, for which QM on = model=20 systems
helps develop SAR. And for philosophical reasons -- I look=20 towards
the future and can see potential in combining the pure = Platonic=20 rationalism
of QM with the brute force of consumer-driven = electronics.=20 :)

> * What are the bottlenecks for the quantum chemical = methods to=20 get into the
> area of drug design.

In a way = they're=20 already involved since they're used to help
paramaterise forcefields. =

Mostly I'd say it's the time and effort involved (especially if = it's=20
industry) and there's some tradition/conservatism within academia as = well
which slows the process. It takes time to introduce QM into=20 undergraduate
pharmacology courses, it takes time for a critical mass = of=20 younger
quantum-enabled chemists to leak from theoretical chemistry=20 departments
into the drug design arena and replace those for whom QM = was=20 never
an option in the past.

It seems that a lot of the best=20 computational chemists are heading more
in the direction of e.g. = inorganic=20 chem / materials science in search
of harder problems, rather than = scaling=20 up the theories which work
well for small systems composed of 1st and = 2nd row=20 elements
in order to apply them to biological systems. Scale-up is=20 less
glamourous; more engineering than pure science.

Then = there's the=20 fact that a lot of synthetic chemists are more
comfortable with = classical=20 concepts, and a lot of drug design is
ultimately done on paper by = synthetic=20 chemists. Not much gets
made without having them on board, and that = takes a=20 generation and
culture shift.

> * For small molecules QC = methods=20 plays greater role, but for handling drug-
> like molecules and = handling=20 several thousands of compounds, QC methods do not
> see the = limelight=20 (correct me if i am wrong). Is the CPU-intensiveness alone
> is = the=20 reason. Or is there any some conceptual gap in this. [ I hear someone =
>=20 saying CPU-intensive is the reason and one has to wait for months to get =
> results ]

Currently I'd say it's mostly the=20 CPU-intensiveness,
then maybe the fact that few interfaces are = designed for=20 it and
few codes are robust enough to handle e.g. input from 000's =
of=20 structures from MM from Corina.

> * Based on your = experience/insight,=20 can you think of some timeframe, say 5
> years, 10 years down the = line,=20 Quantum chemical methods would play a major
> role in the area of = lead=20 identification/optimization, or would you
> say "prediction of = future is=20 difficult!".

If Moore's Law holds for two more decades as it is = predicted=20 to do,
barring global economic meltdown, we can expect around 100 = times=20
the current computing power per square inch in 2012, 10,000 times =
the=20 computing power in 2022. Still not good enough for full ab initio =
treatments=20 of whole enzymes with current methods, let alone full QM-MD
for = binding.=20

But for e.g. a QM ligand in a MM(-MD) cavity, with linear = scaling=20 DFT,
more robust codes, new functionals -- quite do-able for a = series,
or=20 for developing excellent tailored 'scoring functions'.

And for=20 ligand-only studies, for the ligand that takes a few days
now, you = will be=20 able to do your 00's or 000's of ligands accurately
in the same=20 timeframe.

More likely to be in lead optimisation than in lead=20 identification
or scaffold hopping -- when it comes to the = millions/billions=20
of real compounds or enormous combinatorics of virtual = compounds,
there's=20 plenty of room for improvement and more to be gained by
improving MM = &=20 scoring functions than by burning cpu time on QM.

As with any new = technology, we can say what kind of power will
be available with = some=20 confidence, but not how people will
choose to use it, so we can = expect new=20 kinds of applications
of the theory and the hardware that we haven't = thought=20 of yet.
I'll perhaps go out on a limb and say I think we can expect = to=20
see a lot more high-end QM starting to creep into drug design
in = the=20 next 10-20 years, one way or another. For some kinds of
problems, it = won't=20 prove useful, and for others it will come to
dominate.

> I = look=20 forward to reading your views. Thanks.

(I look forward to hearing = what=20 the rest of the community=20 thinks).

Jeremy
-----------------------------------------------= -----------------------
Jeremy=20 Greenwood          &nbs= p;            = ;          =20 jeremy@greenwood.net
Departme= nt of=20 Medicinal=20 Chemistry          &nbs= p;          =20 bh +45 35306117
Royal Danish School of=20 Pharmacy           = ;            = =20 fx +45 35306040
Universitetsparken 2, DK-2100 Copenhagen,=20 Denmark      ah +45=20 32598030
-------------------------------------------------------------= ---------

------=_NextPart_000_0556_01C2A383.078FC0E0-- From chemistry-request@server.ccl.net Sat Dec 14 20:02:02 2002 Received: from odin.cair.du.edu ([130.253.1.2]) by server.ccl.net (8.11.6/8.11.0) with ESMTP id gBF122i12124 for ; Sat, 14 Dec 2002 20:02:02 -0500 Received: from CONVERSION-DAEMON.du.edu by du.edu (PMDF V6.1-1 #40620) id <0H7400601YVDD0@du.edu> for chemistry@CCL.net; Sat, 14 Dec 2002 18:02:01 -0700 (MST) Received: from localhost by du.edu (PMDF V6.1-1 #40620) with ESMTP id <0H7400301YVDWQ@du.edu> for chemistry@CCL.net; Sat, 14 Dec 2002 18:02:01 -0700 (MST) Date: Sat, 14 Dec 2002 18:02:01 -0700 (MST) From: Jim Stoner Subject: G98 Oniom Help X-X-Sender: To: chemistry@CCL.net Message-id: MIME-version: 1.0 Content-type: TEXT/PLAIN; charset=US-ASCII Content-transfer-encoding: 7BIT Hello everyone, I am having problems with an Oniom job using Gaussian 98, x86-Linux-G98RevA.11.1 using a dual Xeon box with 1 GB shared. It is effectively a solvation geometry minimization. Single molecule at B3LYP/6-311G* in 93 waters at AM1. The job progresses, and appears to be slowly converging, when it terminates: (Enter /usr/gaussian/g98/l103.exe) GradGradGradGradGradGradGradGradGradGradGradGradGradGradGradGradGradGrad Berny optimization. SLEqS1 Cycle: 9511 Max:0.157361E-03 RMS:0.226065E-04 Conv:0.167855E-09 Incomplete coordinate system. Try restarting with Geom=Check Guess=Read Opt=(ReadFC,NewRedundant) Incomplete coordinate system. Error termination via Lnk1e in /usr/gaussian/g98/l103.exe. Job cpu time: 0 days 23 hours 59 minutes 36.5 seconds. File lengths (MBytes): RWF= 236 Int= 0 D2E= 0 Chk= 242 Scr= 1 So any ideas to what the error, "Incomplete coordinate system", is refering to. I have restarted the job as requested, but get the same error in return. Thanks in advance... Jim =============================================== James W. Stoner Graduate Student University of Denver Eaton Electron Paramagnetics Laboratory Department of Chemistry and Biochemistry 2101 East Wesley Avenue, Lab 170 Denver, Colorado 80208 303/359-8886 ===============================================