1. Artificial Neural Network
Computational Systems Biology
Young-Mook Kang

2. Neuron

3. Linear Learning W(t+1) is a better decision vector than W(t)
Because W(t+1) classifies only two objects falsely, compared with five by W(t).
The perfect decision vector W should separate all objects of category C1(full circles) from those of category C2(empty circles)

4. Bias
Introducing the bias, δ, to the decision function separating D from the points A, B and C
The family of straight line function y=ax can not separate the class of points A, B and C from the class represented by point D.

5. Bias 1

6. Transfer Functions 1 Hard-limiter Threshold logic Sigmoidal Function
Output = TF(Net)

7. Hard-limiter
A binary hard limiter
A bipolar hard-limiter

8. Threshold logic

9. Sigmoidal Function
The most widely used transfer function in various neural network applications is the so called sigmoidal function, sf.

10. Architecture Layer Learning method One Layer Networks
Hopfield Network, Kohonen network(Self organization map), …
Multi Layer Network
Counter-Propagation, Back-Propagation of Errors …
Learning method
Unsupervised Competitive learning
Kohonen network(Self organization map), …
Supervised Competitive learning
Back-Propagation of Errors …

11. Architecture
One Layer Networks
Multi Layer Network

12. Architecture
Unsupervised learning
Supervised Competitive learning

13. Ex1 ) Application of QSAR
Used Learning method
Back-propagation of Error
Used Compounds and Property
Training Set : 28 , Prediction Set : 55 compounds.
Property : AIT (Auto-Ignition Temperature )
Used Descriptors

14. Ex1 ) Application of QSAR
Back-propagation of Error

15. Learning Algorithm of BP
만약 라면
최종적 Weight 변화는 :

16. Selected Architecture
Training 회수 400r 과 21 개의 Neurons 을 가진 Hidden Layer

17. Result AEP = 3.93 AEP = 24.53 R2 = 0.9692 (R = 0.9845)
Training Set
Predict Set
RMS = 5.37
AEP = 3.93
R2 = (R = )
RMS = 29.15
AEP = 24.53
R2 = (R = )

18. Ex 2) Application of QSAR
Used Compounds
S = aqueous solubility of organic compounds,
Used Unit : ( log S )
Training Set : 888 (range : ~ 1.58 )
Validation Set : 514 ( ~ 1.56 )
Test Set : 368 ( ~ 1.22)

19. Genetic Functional Approximation
Selected Descriptor name
selected account
2D-VSA hydrophobic
1000
ASlogP value
2D-VSA polar
506
Delta Chi 0
503
Atomic polarizabilities
499
I_adj_equ
489
2D-VSA negative charged groups
478
No. positive charged groups
384
Formal charge
123
No. negative charged groups 43
No. H-bond acceptors 27
Delta Chi 4 path/cluster 25
Delta Chi 3 cluster 21
Graph vertex complexity 17
Valence bound charge index 5
Chi 4 path/cluster
Wiener index 15
2-MTI prime 13
Kier shape 1
No. Rotatable bonds 10
Population Size : 1,000
Generations : 100,000
Equation Length : 11
Original Descriptors : 125
Term Creation Types : 5 (625)
Ex) Square, SQRT, Log2, exp, …
Total Descriptors : 750
Generation Equation number
R^2
Equation 1
Equation 2
Equation 3
Equation 4
Equation 5
Equation 6 …

20. Scaling Rule

21. Optimization Structure of ANN
Neurons of Hidden Layer = 30

22. Training set (eqn14)
RMSE :
AME :
R^2 :

23. Prediction Set (eqn14)
RMSE :
AME :
R^2 :

24. Test set (eqn14)
RMSE :
AME :
R^2 :

25. Comparsion of logS with commercial prediction program
Name
Reference/Method
Calculated
Compd. R2
Mean
Absolute
Error
Range a
(%)
ASlogS
PreADME
/ atom-additive method
(2D structure)
1403
0.9100
0.480
90.02
logS prediction
Accelrys Cerius2 4.6
C2-ADME
/ genetic partial least squere regression (using AlogP98 and FPSA) - 2D structure
1387
0.8039
0.688
75.77
QikProp
QikProp 1.6
/Monte Carlo simulation
& Multiple linear regresstion
(3D structure)
1402
0.7894
0.725
75.96