C.-A. Azencott, M. A. Kayala, and P. Baldi. Institute for Genomics and Bioinformatics Donald Bren School of Information and Computer Sciences
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1 Learning Scoring Functions for Chemical Expert Systems C.-A. Azencott, M. A. Kayala, and P. Baldi Institute for Genomics and Bioinformatics Donald Bren School of Information and Computer Sciences 237th ACS Meeting March 24, 2009 Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
2 Goals Chemical space exploration Reproduce Augment problem-solving ability of organic chemists Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
3 Reaction Simulator Input: Starting materials Conditions (ph, temperature) Output: Products Energy diagram Reaction favorability scoring function? Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
4 Energy of Activation G Measure of reaction favorability Directly related to reaction rates Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
5 Energy of Activation G Measure of reaction favorability Directly related to reaction rates How can we compute G? Direct computation not possible Laboratory experiment or QM/MM simulation Infer with Machine Learning Data Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
6 G numerical data Experimental data Quantum Mechanical / Molecular Modeling Problems: Time Consuming Expensive Very little available What can we do? Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
7 How do chemists solve problems? Without: performing experiments producing numerical G chemists can (almost) always: tell which of several possible reactions is more favorable produce relative rates Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
8 How do chemists solve problems? Without: performing experiments producing numerical G chemists can (almost) always: tell which of several possible reactions is more favorable produce relative rates How do we get this qualitative knowledge? Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
9 Reaction Explorer Jonathan H. Chen: Rule-based expert system Basic undergraduate organic chemistry Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
10 Reaction Explorer Jonathan H. Chen: Rule-based expert system Basic undergraduate organic chemistry Create pairs of elementary steps ordered by favorability Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
11 Approach 1: Priority Ordering Rules priority order elementary movements Most favorable electron movement Less favorable electron movement Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
12 Approach 2: Implicitly Unfavorable Reactions From what simple elementary movement should happen Deduce what elementary movements should not happen Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
13 Data Sets CHNO + Halides, Ionic Reactions. Data Set Number of Pairs Priority ordered 3, 457 Implicitly unfavored 475, 684 (from 16, 806 most favorable reactions) Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
14 Feature Representation, f : Elementary Step R n Electron movement between pair of source / sink orbitals Principled choice of 150 features Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
15 Neural Network Architecture I Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
16 Neural Network Architecture II Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
17 Classification Performance Cross-Validated accuracy: Data Set Architecture Accuracy Priority ordered Perceptron (no hidden nodes) 92.8% 15 hidden nodes 93.7% Implicitly unfavored Perceptron (no hidden nodes) 98.8% 15 hidden nodes 99.0% Altogether Perceptron (no hidden nodes) 98.6% 15 hidden nodes 99.0% Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
18 Performance in the Simulator I Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
19 Performance in the Simulator II Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
20 Performance in the Simulator III Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
21 Performance in the Simulator IV Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
22 Conclusion Lack of quantitative data Compensate with qualitative knowledge Applied to the prediction of G Ionic Reactions C, H, N, O + halides Qualitative trends / ranking order of reactivities necessary to solve relevant chemistry problems Future Work Expand coverage Validation on external reaction databases (e.g. SPRESI) Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
23 Thanks Matthew A. Kayala Jonathan H. Chen Reaction Simulation Expert System for Synthetic Organic Chemistry, talk Thursday, March 26th at 10:30 am (CINF General Papers) Prof. Pierre Baldi The Baldi Lab OpenEye Academic Software License ChemAxon Acadedmic Software License NIH/NLM Biomedical Informatics Training Program NSF Grants and The Dreyfus Foundation Special Grant Program in Chemical Sciences Azencott (ACS Spring 09) Learning Chem Scoring Functions March 24, / 20
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