1. ROOT ROOT.PAT ROOT.TES (ROOT.WGT) (ROOT.FWT) (ROOT.DBD) MetaNeural ROOT.XXX ROOT.TTT ROOT.TRN (ROOT.DBD) ROOT.WGT ROOT.FWT Use Analyze root –34 for easy way (the file meta let you override defaults) Use meta root for full mode - e.g meta root - use MetaUI for input file

2.      ANALYZE = MetaNeural Alternative Code Either run meta root analyze root.pat –34 (single training and testing) analyze root.pat –3434 (LOO) analyze root.txt 34 (bootstrap mode) Results for analyze are in resultss.xxx and resultss.ttt Results from MetaNeural are in root.xxx and root.ttt MetaNeural input file is generated automatically in analyze The file name meta overrides the default input file for analyze

3. 4 => 4 layers 2 => 2 inputs 16 => # hidden neurons in layer #1 4 => # hidden neurons in layer# 2 1 => # outputs 300 => epoch length (hint:always use 1, for the entire batch) 0.01 => learning parameters by weight layer (hint: 1/# patterns or 1/# epochs) 0.01 0.5 => momentum parameters by weight layer (hint use 0.5) 0.5 10000000 => some very large number of training epochs 200 => error display refresh rate 1 =>sigmoid transfer function 1 => Temperature of sigmoid check.pat => name of file with training patterns (test patterns in root.tes) 0 => not used (legacy entry) 100 => not used (legacy entry) 0.02000 => exit training if error < 0.02 0 => initial weights from a flat random distribution 0.2 => initial random weights all fall between –2 and +2 MetaNeural Input File for the ROOT

4. EXAMPLE DATA SETS IRIS data Checkerboard data Svante wold’s QSAR data Cherkassky’s nonlinear function Albumin QSAR data

6. CHECK_DATA.BAT CHECK_NET.BAT CHECK_TEST.BAT CHECK.PAT FILES RELATED TO CHECKERBOARD EXAMPLE

7. MetaNeural INPUT FILE FOR CHECKERBOARD DATA

11. QSAR DATA SET EXAMPLE: 19 Amino Acids From Svante Wold, Michael Sjölström, Lennart Erikson, "PLS-regression: a basic tool of chemometrics," Chemometrics and Intelligent Laboratory Systems, Vol 58, pp. 109-130 (2001) RENSSELAER

13. PLS 1 latent variable

14. PLS 1 latent variable No aromatic AAs

15. 1 latent variable Gaussian Kernel PLS (sigma = 1.3) With aromatic AAs

16. Chemoinformatic Models to Predict Binding Affinities to Human Serum Albumin: G. Colmenarejo et. al., J. Med. Chem 2001, 44, pp. 4370-4378

17. Binding affinities to human serum albumin (HSA): log K’hsa Gonzalo Colmenarejo, GalaxoSmithKline J. Med. Chem. 2001, 44, 4370-4378 95 molecules, 250-1500+ descriptors Widely different compounts

18. 1 ) Surface properties are encoded on 0.002 e/au 3 surface Breneman, C.M. and Rhem, M., J. Comp. Chem., 1997,18(2), p. 182-197 2 ) Histograms or wavelet encoded of surface properties give TAE property descriptors Electron Density-Derived TAE-wavelet Descriptors PIP (Local Ionization Potential) Histograms Wavelet Coefficients

19. TAE Internal Ray Reflection - low resolution scan Isosurface (portion removed) with 750 segments PEST-Shape Descriptors: Surface Property-Encoded Ray Tracing RENSSELAER

20. Segment length and point-of-incidence value form 2D-histogram Each bin of 2D-histogram becomes a hybrid descriptor –36 descriptors per hybrid length-property PIP vs Segment Length Shape-Aware Molecular Descriptors from Property/Segment-Length Distributions RENSSELAER

21. training

22. testing

26. CHERKASSKY’S NONLINEAR BENCHMARK DATA Generate 500 datapoints (400 training; 100 testing) for: Cherkas.bat

30. Y=sin|x|/|x| Generate 500 datapoints (100 training; 500 testing) for:

31. Comparison Kernel-PLS with PLS 4 latent variables sigma = 0.08 PLS Kernel-PLS