QSAR approaches, including recent advances in 3D-QSAR, are advantageous during the lead optimization phase of drug discovery and complementary with bioinformatics and growing data accessibility. Hints for future QSAR practitioners are also offered

A Doweyko

A Golbraikh

B Wendt

BR Kowalski

C Hansch

D Abernethy

DM Hawkins

GM Maggiora

GR Maggiora

JC Dearden

R Czerminski

RD Brown

RD Clark

RD Cramer

Richard D. Cramer

S Wold

SH Unger

SM Free

SM Paul

SR Johnson

T Scior

YC Martin

English

PubMed

Springer - Publisher Connector

PERSPECTIVE
The inevitable QSAR renaissance
Richard D. Cramer
Received: 7 November 2011 / Accepted: 11 November 2011 / Published online: 30 November 2011
 The Author(s) 2011. This article is published with open access at Springerlink.com
Abstract QSAR approaches, including recent advances
in 3D-QSAR, are advantageous during the lead optimiza-
tion phase of drug discovery and complementary with
bioinformatics and growing data accessibility. Hints for
future QSAR practitioners are also offered.
Keywords QSAR  CoMFA  Lead optimization
Considering the many recent publications deploring vari-
ous inadequacies of current QSAR practices1 and outcomes
[1–8], as well as QSAR’s relative antiquity, the reader is
forgiven for any skeptical reaction to the title. Yet several
CADD groups at prestigious major and medium pharmas
whom I have recently encountered began discussions by
stating their intent to increase their proportion of QSAR
activities (while decreasing structure-based design). What
is going on?
The major cause of this sudden renewal of interest in QSAR
seems to be a major yet poorly met need—practical CADD
guidance for lead optimization. As the longest, most expen-
sive, and success-determining phase in most drug discovery
projects [9],2 one might suppose lead optimization to have
special need for guidance from CADD. Yet current CADD
methodology development activities tend much more to
address the earlier hit discovery and hit-to-lead phases. One
reason may be the much higher IP-based barrier between
methodology development and usage experience during lead
optimization. The data sets needed to validate any new meth-
odology’s value in practical LO situationsare far less available.
But probably a greater reason is that lead optimization
also poses a couple of demanding challenges to CADD
methodologies. The first is the nature of the candidate
structures. Earlier in drug discovery, the candidate structures
are rather dissimilar from one another, typically exhibiting at
least three orders of magnitude of differences among
experimental potencies. However, during lead optimization,
the candidates are much more similar, usually varying by
only one or two R-groups attached to a shared core.
Accordingly the variation in measured potencies is much
less, seldom much more than an order of magnitude. The
second challenge is the much greater number and variety of
the biological observables during lead optimization, all of
which need to be considered if a ranking of candidates is to
be meaningful. At the same time the pace of compound
synthesis is much greater. A synthetic chemist whose career
is currently dedicated to a lead optimization project does not
idly await lengthy computations.
Furthermore, opportunities for usefully applying QSAR
approaches across all phases of drug discovery are also
R. D. Cramer (&)
Tripos, Inc., 1699 South Hanley Road, St. Louis,
MO 63144, USA
e-mail: cramer@tripos.com
1 What is meant here by ‘‘QSAR’’? In agreement with Martin [34,
p. 1] the distinctive characteristic of QSAR is its emphasis on
biological observations as the basis for CADD activities, in contrast
with the emphasis that (receptor) structure-based CADD places on
physics-based models. (Of course neither approach altogether ignores
the other’s focus!) Is ligand similarity then a branch of QSAR (or vice
versa)? Both approaches do emphasize biological observations for
making potency predictions, although they differently seek either
sufficiency or improvement in those potencies. (Pharmacophore
approaches, being ligand-based but structurally focused, seem a third
class of CADD methodologies).
2 Lead optimization costs, per new drug introduction, are the highest
of all, exceeding those of Phase II and III development because, being
earlier, they generate more dead-ends and tie up capital for longer.
More specifically, lead optimization accounts for 17% of total R&D
cost and around 50% of discovery cost, and may be the 3rd largest
opportunity area for overall R&D cost reduction.
123
J Comput Aided Mol Des (2012) 26:35–38
DOI 10.1007/s10822-011-9495-0
mushrooming, thanks to the plummeting costs of acquiring
and recording data points in multiplexed experiments, our
rapidly expanding appreciation of biological complexity,
and the inherent methodological symbioses between QSAR
and bioinformatics. To cite a current example of this
symbiosis, the most advanced approaches to mission-crit-
ical ‘‘off-target predictions’’ [10] routinely consider simi-
larities in both ligand and biological pathway properties.
Will we start regarding QSAR simply as that branch of
bioinformatics that addresses chemical structure selection,
in particular among ligands?
Promising methodological advances may also be a factor
in this renewed QSAR interest. While the pA50 [11] pre-
dictions of structurally ‘‘local’’ 3D-QSAR models seem
relatively reliable [2, 12],3 sufficient to help guide lead
optimization, the tedious and unavoidably subjective aspects
of aligning each structure to be predicted has severely lim-
ited 3D-QSAR’s practical applicability. However, the new
method of topomer CoMFA (whereby rule-generated
alignments are applied individually to fragments rather than
complete structures) [12] is surely among the simplest,
quickest, and most objective tools in today’s CADD kit. And
fortunately, the almost unprecedentedly accurate predictions
so far reported in lead optimization projects using ‘‘topomer
CoMFA’’, specifically a standard deviation of 0.6 between
predicted and found pA50’s, over 144 made-and-tested
compounds from four different organizations [13–16], if
continued, should further encourage its widespread appli-
cation. The exceptional speed and objectivity of the topomer
CoMFA protocol is also inspiring new methodological
opportunities, such as virtual screening for R-groups (see
footnote 3), with hits being accompanied by potency pre-
dictions, and ‘‘QSEA’’ [15, 17], which simplifies exploration
of a so far oft-neglected issue, how a specific QSAR varies
with its training set composition. Also emergent at this
writing is ‘‘template CoMFA’’ [18], which provides the
CADD expert with control over the conformation(s) used to
generate a 3D-QSAR, for example in the form of a receptor-
bound conformation, while retaining the desirable attributes
of topomer CoMFA.
Yet another massive development that favors usage of
QSAR, with its need for experimental results to drive its
hypothesis generation, is the growing capabilities of most
drug discovery organizations for making experimental
data, both public [19] and private, completely and readily
available, and in as many comparative formats as possible.
But what about those publications deploring QSAR
deficiencies? For the most part it is agreed that these dis-
appointments are caused more by faulty practice or unre-
alistic expectations than by fundamental deficiencies in the
QSAR approach. However it would be much harder to
reach a consensus on which practices are faulty, and the
recurring empirical dilemma—is my latest QSAR predic-
tion trustworthy enough to guide a critical project deci-
sion?—remains problematic. With these caveats, here are
some hints for the future QSAR practitioner, based on over
40 years of experience.
• A QSAR is simply a coherent structurally-based
summary of a particular set of biological observations,
hopefully revealing a pattern which helps to success-
fully guide a discovery team to a therapeutic goal. Yet
the still barely understood complexities of biological
processes, at molecular, cellular, and organism levels,
surely set unknowable boundaries—aka ‘‘activity
cliffs’’ [20]—on the extrapolability of any QSAR.
(And the highly multidimensional character of most
QSARs dictates that almost any worthwhile prediction
is an extrapolation [21].)
• Nevertheless, it is an empirical fact, as indicated above,
that QSAR predictions are often accurate enough to
benefit discovery. I believe this tendency to be evidence
for undiscovered regularities in biological phenomena
that physical and systems biology modeling will
eventually reveal. Yet meanwhile—if no drugs are
found, the entire discovery process is jeopardized. If
QSAR may help and is conveniently available,
shouldn’t it be tried?
• Each discovery team member is already considering the
same observations to seek the same goal, often
generating intuitive SARs similar to the current QSAR.
However, the now overwhelming quantity of such
observations suggests that a QSAR exercise may also
be increasingly useful in calling attention to outliers
and/or activity cliffs, to be scrutinized with the hope of
exploitation. Having available such a single and
relatively formal QSAR expression of the team’s
intuitive and varied SAR models makes the recognition
of an outlier more likely.
• Any particular QSAR is probably only one of many
statistically acceptable and perhaps equally plausible
alternative QSARs, considering for example the
thousands of possibly explanatory structural descriptors
that are available. Rapid detection and inclusion of an
‘‘activity cliff’’ observation, though perhaps immedi-
ately disappointing, may highlight a more productive
QSAR hypothesis.
• Statistical parameters have marginal relevance when
assessing the soundness of a QSAR prediction. For
example, choosing as ‘‘the model’’ simply the one
having the greatest q2 (or r2) value is little better than a
coin flip, considering the other uncertainties discussed
in previous hints.
3 ‘‘pA50’’ is used as an abbreviation for the log10 of any biological
potency measurement.
36 J Comput Aided Mol Des (2012) 26:35–38
123
• Also underappreciated is the strong dependence of q2/r2 on
the spread in the underlying biological potencies. More
informative is the entirely model –dependent standard
error in the potency predictions/fits which accompanies a
q2/r2. This consideration further implies that for QSAR
derivation a wide spread among the biological observa-
tions, though desirable, is far from mandatory.
• Furthermore, excessive dependence on q2 can seriously
impede the discovery of useful new hypotheses,
particularly when first encountering an ‘‘activity cliff’’.
The q2 ‘‘leave-out-and-predict’’ philosophy necessarily
suppresses any new hypothesis that is supported by
only one newly tested structure. And it would seem, the
higher a cliff, the more likely its q2 rejection.
• ‘‘Leave-one-out’’ cross-validation is also a misnomer in
most QSAR derivations, because most structural series
contain multiple instances of the more successful
R-groups, whose influence therefore cannot be erased
by omitting and predicting individual structures (see
footnote 3).
• Discovery projects seek better structures. Identifying a
structure which is then found to be superior, even if its
potency prediction is numerically relatively inaccurate,
is far more valuable than many accurate pA50 predic-
tions of potencies already achieved (see footnote 3).
Therefore judicious extrapolation of a QSAR seems
desirable rather than ‘‘dangerous’’.
• In general classical statistics is far too optimistic when
validating a QSAR, because its underlying assumptions
about data distributions are contradicted by the extraor-
dinarily heterogeneous nature of chemical structures
and mechanisms of biological response. Restricting the
structural scope of a QSAR should help, but the
distribution of ‘‘local’’ structural variations, within a
series undergoing lead optimization, is also unlikely to
be uniform.
• One tactic to consider for prediction validation is to
seek multiple QSAR models encompassing different
‘‘radii’’ of structural variation, with the goal of
detecting as many activity cliffs as possible. A predic-
tion that is reproduced by such a varying scope of
QSARs is more likely to be robust.
• More sophisticated means of ‘‘data mining’’—neural
nets, pattern recognition, support vector machines, even
mere non-linear regression—have been repeatedly tried
[22, 23] and abandoned, as too costly and/or unreliable.
The major exceptions, PLS [24], cross-validation/
bootstrapping [25], and recursive partitioning [26], are
relatively minor extensions of QSAR’s original multi-
ple linear regression [27–29]. It seems that biological
data are too fuzzy and alternative explanatory hypoth-
eses too numerous for QSAR to benefit from increased
model complexity.
• When selecting candidate explanatory structural
descriptors for use in a QSAR:
• Physics-based descriptors [30], for example 3D-
QSAR’s fields, are likely to produce the most robust
and useful models, for example capable of actively
generating and selecting among quite varied struc-
tural ideas. Other classes of descriptor are more or
less limited to passive discrimination among less
structurally varied ideas, generated by some exter-
nal process.
• Substructural descriptors, for example ‘‘2D finger-
prints’’, would seem the least reliable, because
biological receptors are affected only by the fields
and mechanical behaviors presented by a candidate
ligand, not by the underlying atomic connectivities
[31]. Yet, substructural descriptor similarity has
been a better predictor of biological similarity than
many 3D similarity metrics [32].
• Whenever the underlying biological observables
may depend on transport as well as receptor fit, such
as passive membrane penetration, ‘‘1D’’ descriptors,
such as log P, polar surface area, and pKa, should be
considered. Perhaps obvious, yet for example 3D-
QSAR models almost universally omit them [33].
Unfortunately QSAR’s relatively long history also sug-
gests that many of its future practitioners will be variously
hampered in learning from past experiences. The motiva-
tions to ‘‘turn off the brain and turn on the computer’’ will
not disappear and indeed are probably increasing. Thus it is
also my somewhat biased suggestion that in such hampered
situations topomer CoMFA may nevertheless be of some
benefit to a discovery process.
Open Access This article is distributed under the terms of the
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The inevitable QSAR renaissance

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Journal of Computer-Aided Molecular Design

Clin Pharmacol Ther 90:203–214

Quantitative drug design: a critical introduction.

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The inevitable QSAR renaissance

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