International Training Program (ITP)
Participation Application Form

• Applicant (Student) Name : J.B. Brown• Home Institute– Laboratory of Biological Network Information (Akutsu Laboratory)
Kyoto University Institute for Chemical Research; Kyoto, Uji, Gokasho 611-0011• Partner Institute– Macromolecular Modelling Group (Knapp Laboratory)
Free University of Berlin; Fabeckstrassen 36a, D-14195, Germany, Berlin-Dahlem• Duration of program : September, 2009 - November, 2009 (approx. 10 weeks)

Research background and objectives of exchange program
Background
In previous research, J.B. Brown has led a project for the development and testing of a computational technique for analyzing target properties in compounds. The approach was to design ameasure of similarity between compounds, called a kernel, which is then utilized by a patternanalysis algorithm such as a Support Vector Machine. More specifically, the compound similarity
technique is called a chiral graph kernel (labeled K), which measures the similarity of two compounds by transforming atoms and bonds into mathematical graphs G1 , G2 and counting (via afunction ψ) the number of pattern sub-structures (trees t ∈ T ) existing in both compounds:!
K(G1 , G2 ) =w(t)ψt (G1 )ψt (G2 ) .
t∈TAn example of this equation is shown below, where w(t) is a weight given to each tree.

Figure 1: An example of transforming a chemical structure into a feature vector.

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J.B. Brown, along with other collaborators, extended the tree patterns from previous research
to include the chirality or handedness of molecules (e.g., R-ibuprofen and S-ibuprofen). As aresult, it was shown that Support Vector Regression (SVR) with a chiral graph kernel produced
a drastic improvement in target property prediction (Brown et al., J. Chem. Inf. Modell., inre-review).
Despite the success of the kernel method proposed, it has yet to incorporate the physicochemical
properties of atoms in a drug or other molecule. As a simple example, the compounds CH4 andSiH4 will have a similarity score of K(CH4 , SiH4 ) = 0 because methane contains the pattern
tree C→H, different from silane’s pattern Si→H. However, it is known that similar valence group
substitutions are important for industry and medicine.

Research objectives
J.B. Brown has proposed two improvements to the existing chiral tree-pattern graph kernel toaccomodate for compounds that interchange atoms of the same valence group. As both of theextensions are physicochemical, it is important that J.B. Brown collaborate with a research groupwith a focus on physical chemistry. As a result, J.B. Brown contacted professor Ernst WalterKnapp of the partner institute, and J.B. Brown’s visit to Knapp’s group was agreed upon.
At the Free University of Berlin, J.B. Brown will collaborate with Knapp’s group on thefollowing projects:
1. Revision of the kernel equation given above to relax matching requirements to only valencegroup matchings. This is critical for compound toxicity analysis and virtual screening applications in pharmacology. In the previous method, trees of depth one (single atoms) were
calculated for similarity as follows:
K(u, v) = λ ∗ 1(label(u) = label(v)) ,where 1() is a function returning 1 if a true argument is given, and 0 otherwise (lambda
is a weighting of a match). The similarity of two trees is then the summation of theseatom match weightings (convolution), and the similarity of two compounds is the similarityof the trees constituting the compounds (the equation on page 1). J.B. Brown’s proposed
extension, which will be argued and refined during the ITP program, is as follows:
Ke (u, v) = 1(|δ + (u)| = |δ + (v)|) ∗ (λ ∗ k̂ −|EN(u)−EN(v)| ) ,where we introduce the electronegativity EN(u) of an atom u, and an electronegativity match
free parameter k̂ > 1. δ + (u) returns the set of outgoing neighbors in compound u’s graphrepresentation (see figure above).
2. Expansion of the kernel methods to three dimensional compounds with analysis via electrostatic potential (ESP). ESP is the Knapp group’s specialty, and therefore, the state of theart in kernel methods for biochemical QSAR analyses will be greatly enhanced as a result of
this collaboration. Knapp’s group has the computing hardware, software, and field-specificknowledge to help J.B. Brown’s QSAR research. J.B. Brown has proposed the following
ESP kernel, though it has yet to be tested:pp

KESP (C1 , C2 ) = k1 exp−k2 (Σp=(x,y,z) |ESP (C1 )−ESP (C2 )|) .
Essentially, the equation above represents the exponential decrease in similarity by calculating the convoluted difference in electrostatic potential at each point in a virtual electrostaticmesh. This means that analyses are no longer restricted to chemical compounds; biochemicalcomplexes can now be evaluated and screened by kernel methods. For systems and molecular bioinformatics, this will be a large advance. At Knapp’s group, J.B. Brown will spend
a good amount of time discussing how to set the constants k1 and k2 based on standard
physicochemical properties.

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