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1. Title: MUSK "Clean2" database
2. Sources:
(a) Creators: AI Group at Arris Pharmaceutical Corporation
contact: David Chapman or Ajay Jain
Arris Pharmaceutical Corporation
385 Oyster Point Blvd.
South San Francisco, CA 94080
415-737-8600
zvona@arris.com, jain@arris.com
(b) Donor: Tom Dietterich
Department of Computer Science
Oregon State University
Corvallis, OR 97331
503-737-5559
tgd@cs.orst.edu
(c) Date received: September 12, 1994
3. Past Usage:
(a) Dietterich, T. G., Jain, A., Lathrop, R., Lozano-Perez, T. (1994).
A comparison of dynamic reposing and tangent distance for drug
activity prediction. Advances in Neural Information Processing
Systems, 6. San Mateo, CA: Morgan Kaufmann. 216--223.
The clean2 dataset included here is derived from the starting
poses employed in this paper. The paper reports the following
results:
Algorithm: 20-fold XVAL:
1-nearest neighbor (euclidean distance) 75%
neural network (standard poses) 75%
1-nearest neighbor (tangent distance) 79%
neural network (dynamic reposing) 91%
The tangent distance and dynamic reposing technique require
computation of the molecular surface, which cannot be done
using the feature vectors included in this data set.
(b) Jain, A. N., Dietterich, T. G., Lathrop, R. H.,
Chapman, D., Critchlow, R. E., Bauer, B. E., Webster, T. A.,
Lozano-Perez, T. Compass: A shape-based machine learning tool for
drug design. Accepted for publication in Computer-Aided
Molecular Design.
This paper describes the dynamic reposing technique in more
detail and reports the same result for dynamic reposing as
above. The paper also gives a complete description of each of
the 102 molecules in the data set.
(c) Dietterich, T. G., Lathrop, R. H., Lozano-Perez, T. (submitted)
Solving the multiple-instance problem with axis-parallel rectangles.
Submitted to Artificial Intelligence.
This paper describes a family of axis-parallel rectangle
algorithms and compares various approaches to the multiple
instance problem. It includes the following table:
Algorithm TP FN FP TN errs %correct [CI]
iterated-discrim APR 30 9 2 61 11 89.2 [83.2--95.2]
GFS elim-kde APR 32 7 13 50 20 80.4 [72.7--88.1]
GFS elim-count APR 31 8 17 46 25 75.5 [67.1--83.8]
all-positive APR 34 5 23 40 28 72.6 [63.9--81.2]
backpropagation 16 23 10 53 33 67.7 [58.6--76.7]
GFS all-positive APR 37 2 32 31 34 66.7 [57.5--75.8]
most frequent class 0 39 0 63 39 61.8 [52.3--71.2]
C4.5 (pruned) 32 7 35 28 42 58.8 [49.3--68.4]
key: TP = true positives
FN = false negatives
FP = false positives
TN = true negatives
errs = errors = FN+FP
%correct = 10-fold cross-validation %correct.
CI = 95% confidence interval on proportion of correct
predictions.
For explanations of the various algorithms, see the
paper.
C4.5 and backprop were applied ignoring the multiple instance
problem (see below) during training, but obeying it during
testing.
This paper also gives more details on the construction of the
data set.
4. Relevant Information:
This dataset describes a set of 102 molecules of which 39 are judged
by human experts to be musks and the remaining 63 molecules are
judged to be non-musks. The goal is to learn to predict whether
new molecules will be musks or non-musks. However, the 166 features
that describe these molecules depend upon the exact shape, or
conformation, of the molecule. Because bonds can rotate, a single
molecule can adopt many different shapes. To generate this data
set, all the low-energy conformations of the molecules were
generated to produce 6,598 conformations. Then, a feature vector
was extracted that describes each conformation.
This many-to-one relationship between feature vectors and molecules
is called the "multiple instance problem". When learning a
classifier for this data, the classifier should classify a molecule
as "musk" if ANY of its conformations is classified as a musk. A
molecule should be classified as "non-musk" if NONE of its
conformations is classified as a musk.
5. Number of Instances 6,598
6. Number of Attributes 168 plus the class.
7. For Each Attribute:
Attribute: Description:
molecule_name: Symbolic name of each molecule. Musks have names such
as MUSK-188. Non-musks have names such as
NON-MUSK-jp13.
conformation_name: Symbolic name of each conformation. These
have the format MOL_ISO+CONF, where MOL is the
molecule number, ISO is the stereoisomer
number (usually 1), and CONF is the
conformation number.
f1 through f162: These are "distance features" along rays (see
paper cited above). The distances are
measured in hundredths of Angstroms. The
distances may be negative or positive, since
they are actually measured relative to an
origin placed along each ray. The origin was
defined by a "consensus musk" surface that is
no longer used. Hence, any experiments with
the data should treat these feature values as
lying on an arbitrary continuous scale. In
particular, the algorithm should not make any
use of the zero point or the sign of each
feature value.
f163: This is the distance of the oxygen atom in the
molecule to a designated point in 3-space.
This is also called OXY-DIS.
f164: OXY-X: X-displacement from the designated
point.
f165: OXY-Y: Y-displacement from the designated
point.
f166: OXY-Z: Z-displacement from the designated
point.
class: 0 => non-musk, 1 => musk
Please note that the molecule_name and conformation_name attributes
should not be used to predict the class.
8. Missing Attribute Values: none.
9. Class Distribution:
Musks: 39
Non-musks: 63