Machine Learning for Chemoinformatics An introduction
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1 Machine Learning for Chemoinformatics An introduction Francesca Grisoni University of Milano-Bicocca, Dept. of Earth and Environmental Sciences, Milan, Italy ETH Zurich, Dept. of Chemistry and Applied Biosciences, Zurich, Switzerland F. Grisoni, BigChem online course
2 Presentation Outline Introduction Definition Elements of Machine Learning Additional Considerations The NFL theorem Validation Applicability Machine Learning approaches: some examples Local methods Tree-like approaches Neural Networks F. Grisoni, BigChem online course
3 Introduction Machine learning in chemoinformatics Biological activity prediction Toxicity Physico-chemical properties P = f ( ) Multi-objective optimization Rational drug design F. Grisoni, BigChem online course
4 Introduction Machine Learning (ML) Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed. 1 (1) Data (2) Task A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. 2 (3) Performance 1 Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of research and development, 3(3), Mitchell, T. M. (1997). Machine learning Burr Ridge, IL: McGraw Hill, 45(37), F. Grisoni, BigChem online course
5 Machine Learning Elements (1) The data and the G-I-G-O principle X (n x p) n samples p variables X f ( ) f ( 0.1, 1, 0, 3, 3.5, 2, ) F. Grisoni, BigChem online course
6 Machine Learning Elements (1) The data and the G-I-G-O principle X (n x p) Y (n x 1) p variables p' P = f ( ) n samples X n samples Y P = f ( 0.1, 1, 0, 3, 3.5, 2, ) F. Grisoni, BigChem online course
7 Machine Learning Elements (1) The data and the G-I-G-O principle X (n x p) p variables Y (n x 1) p' Garbage In = Garbage Out n samples X n samples Y Structures Experimental Responses F. Grisoni, BigChem online course
8 Machine Learning Elements (2) Machine Learning Tasks Unsupervised Learning X (n x p) x 2 p variables n samples X x 1 F. Grisoni, BigChem online course
9 Machine Learning Elements (2) Machine Learning Tasks Unsupervised Learning X (n x p) x 2 p variables n samples X x 1 F. Grisoni, BigChem online course
10 Machine Learning Elements (2) Machine Learning Tasks Supervised Learning x 2 X (n x p) p variables n samples X x 1 F. Grisoni, BigChem online course
11 Machine Learning Elements (2) Machine Learning Tasks x 2 Supervised Learning X (n x p) + p variables Y (n x 1) p' n samples X n samples Y x 1 F. Grisoni, BigChem online course
12 Machine Learning Elements (2) Machine Learning Tasks x 2 Supervised Learning X (n x p) + p variables Y (n x 1) p' n samples X n samples Y x 1 F. Grisoni, BigChem online course
13 Machine Learning Elements (2) Machine Learning Tasks F. Grisoni, BigChem online course
14 Machine Learning Elements (2) Machine Learning Tasks Classification Regression F. Grisoni, BigChem online course
15 Machine Learning Elements (3) Performance Classification N P Single class N TN FP Sensitivity or True Positive rate (TPR) P FN TP Specificity or True Negative rate (TNR) Precision F. Grisoni, BigChem online course
16 Machine Learning Elements (3) Performance Classification N P Global Performance N TN FP Non-Error Rate or Balanced-Accuracy ϵ [0,1] P FN TP Matthews Correlation Coefficient (MCC) ϵ [-1,1] Accuracy ϵ [0,1] F. Grisoni, BigChem online course
17 Machine Learning Elements (3) Performance Classification N P Global Performance N TN FP Non-Error Rate or Balanced-Accuracy ϵ [0,1] P FN TP N = 990; P = 10 TN = 990 (100%) TP = 0 (0%) Sn p = 0/10 = 0% Sn p = 990/990 = 100% Matthews Correlation Coefficient (MCC) ϵ [-1,1] Accuracy ϵ [0,1] NER = 50% Acc = 99% F. Grisoni, BigChem online course
18 Machine Learning Elements (3) Performance Classification N P Global Performance N TN FP Non-Error Rate or Balanced-Accuracy ϵ [0,1] P FN TP N = 990; P = 10 TN = 990 (100%) TP = 0 (0%) Sn P = 0/10 = 0% Sn N = 990/990 = 100% Matthews Correlation Coefficient (MCC) ϵ [-1,1] Accuracy ϵ [0,1] NER = 50% Acc = 99% F. Grisoni, BigChem online course
19 Machine Learning Elements (3) Performance Classification N P Global Performance N TN FP Non-Error Rate or Balanced-Accuracy ϵ [0,1] P FN TP N = 990; P = 10 TN = 990 (100%) TP = 0 (0%) Sn P = 0/10 = 0% Sn N = 990/990 = 100% Matthews Correlation Coefficient (MCC) ϵ [-1,1] Accuracy ϵ [0,1] NER = 50% Acc = 99% F. Grisoni, BigChem online course
20 Machine Learning Elements (3) Performance Classification N P Global Performance N TN FP Non-Error Rate or Balanced-Accuracy ϵ [0,1] P FN TP N = 990; P = 10 TN = 990 (100%) TP = 0 (0%) Sn P = 0/10 = 0% Sn N = 990/990 = 100% Matthews Correlation Coefficient (MCC) ϵ [-1,1] Accuracy ϵ [0,1] NER = 50% Acc = 99% F. Grisoni, BigChem online course
21 Machine Learning Elements (3) Performance Regression Real Pred. Root Mean Squared Error in Prediction (RMSEP) y y# F. Grisoni, BigChem online course
22 Considerations on ML Additional Considerations F. Grisoni, BigChem online course
23 Considerations on ML Additional Considerations 1. Choice of the learner No Free Lunch Theorem: For every learner, there exists a task on which it fails, even though that task can be successfully learned by another learner. 1 1 Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press. F. Grisoni, BigChem online course
24 Considerations on ML Additional Considerations 2. Bias-Variance Trade-off Error Bias à generalization (underfitting) Variance à descriptive ability (overfitting) Complexity F. Grisoni, BigChem online course
25 Considerations on ML Additional Considerations 2. Bias-Variance Trade-off Error Bias à generalization (underfitting) Variance à descriptive ability (overfitting) Complexity F. Grisoni, BigChem online course
26 Considerations on ML Additional Considerations 3. Validation group 1 group 2 Initial dataset group 4 F. Grisoni, BigChem online course
27 Considerations on ML Additional Considerations 3. Validation group 1 group 2 Initial dataset Training set group 4 Test set F. Grisoni, BigChem online course
28 Considerations on ML Additional Considerations 3. Validation group 1 group 2 Initial dataset Training set group 4 Test set Training set Validation set Test set F. Grisoni, BigChem online course
29 Considerations on ML Additional Considerations 3. Validation group 1 group 2 group 3 group 4 group 5 Validation set Training set F. Grisoni, BigChem online course
30 Considerations on ML Additional Considerations 4. Applicability The No Free Dessert (either!) theorem Machine learning models à Reductionist Types of chemical structures Physicochemical properties Mechanisms of action considered Applicability Domain: Chemical space where the property can be reliably predicted F. Grisoni, BigChem online course
31 Considerations on ML Additional Considerations 4. Applicability min( x ), max( x ) 1 1 min( x ), max( x ) 2 2 H=X (X X) T -1 X p Man xy = å j - j j= 1 D x y F. Grisoni, BigChem online course
32 Standard Machine Learning workflow (in chemoinformatics) Considerations on ML F. Grisoni, BigChem online course
33 Standard Machine Learning workflow (in chemoinformatics) Considerations on ML Information extraction F. Grisoni, BigChem online course
34 Standard Machine Learning workflow (in chemoinformatics) Considerations on ML Information extraction Applicability & predictivity F. Grisoni, BigChem online course
35 Standard Machine Learning workflow (in chemoinformatics) Considerations on ML Information extraction Applicability & predictivity Application & new knowledge F. Grisoni, BigChem online course
36 Machine Learning methods (overview) 1. Decision Tree-based learning Decision Trees Random Forest 2. Local Methods k-means algorithm k-nn algorithm 3. Artificial Neural Networks Feed-Forward NN Kohonen Maps F. Grisoni, BigChem online course
37 (1) Decision Tree Learning Root node Decision node(s) Leaves F. Grisoni, BigChem online course
38 (1) Decision Tree Learning Root node 1. Easy to interpret 2. No data pretreatment 3. Numerical/categorical variables 4. Classification and regression 5. Non parametric 6. Automatic variable selection Decision node(s) Leaves F. Grisoni, BigChem online course
39 (1) Decision Tree Learning Random Forest Bagging (Bootstrap Aggregating) = the power of the crowd F. Grisoni, BigChem online course
40 (2) Local approaches k-means clustering x 2 x 1 F. Grisoni, BigChem online course
41 (2) Local approaches k-means clustering x 2 1. Select a k (3) x 1 F. Grisoni, BigChem online course
42 (2) Local approaches k-means clustering x 2 1. Select a k (3) 2. Random assignment x 1 F. Grisoni, BigChem online course
43 (2) Local approaches k-means clustering x 2 1. Select a k (3) 2. Random assignment 3. Centroid calculation x 1 F. Grisoni, BigChem online course
44 (2) Local approaches k-means clustering x 2 1. Select a k (3) 2. Random assignment 3. Centroid calculation 4. Closest centroid x 1 F. Grisoni, BigChem online course
45 (2) Local approaches k-means clustering x 2 1. Select a k (3) 2. Random assignment 3. Centroid calculation 4. Closest centroid x 1 F. Grisoni, BigChem online course
46 (2) Local approaches k-means clustering x 2 1. Select a k (3) 2. Random assignment 3. Centroid calculation 4. Closest centroid x 1 F. Grisoni, BigChem online course
47 (2) Local approaches k-means clustering x 2 1. Select a k (3) 2. Random assignment 3. Centroid calculation 4. Closest centroid x 1 F. Grisoni, BigChem online course
48 (2) Local approaches k-means clustering x 2 1. Select a k (3) 2. Random assignment 3. Centroid calculation 4. Closest centroid 5. End x 1 F. Grisoni, BigChem online course
49 (2) Local approaches k-nearest Neighbor (knn)? F. Grisoni, BigChem online course
50 (2) Local approaches k-nearest Neighbor (knn) 1. Calculate a distance (n train times)? F. Grisoni, BigChem online course
51 (2) Local approaches k-nearest Neighbor (knn) 1. Calculate a distance (n train times) 2. Select a number of neighbors (k) to predict the response? F. Grisoni, BigChem online course
52 (2) Local approaches k-nearest Neighbor (knn) 1. Calculate a distance (n train times) k = 1 2. Select a number of neighbors (k) to predict the response? F. Grisoni, BigChem online course
53 (2) Local approaches k-nearest Neighbor (knn) 1. Calculate a distance (n train times) k = 2 2. Select a number of neighbors (k) to predict the response? F. Grisoni, BigChem online course
54 (2) Local approaches k-nearest Neighbor (knn) 1. Calculate a distance (n train times) k = 3 2. Select a number of neighbors (k) to predict the response? F. Grisoni, BigChem online course
55 (2) Local approaches k-nearest Neighbor (knn) 1. Calculate a distance (n train times) k = 4 2. Select a number of neighbors (k) to predict the response? F. Grisoni, BigChem online course
56 (2) Local approaches k-nearest Neighbor (knn) 1. Good for large training set with localized differences 2. Difficult to be interpreted 3. Which k? 4. Which distance measure? 5. Curse of dimensionality à Variable selection F. Grisoni, BigChem online course
57 (3) Neural Networks Artificial Neurons Inputs x 2 x 1 Output x 3 x 4 f (x) y x p Activation Function F. Grisoni, BigChem online course
58 (3) Neural Networks Artificial Neurons Inputs x 2 x 1 Output x 3 x 4 f (x) y x p Activation Function Neural networks. Comprehensive Chemometrics: Chemical and Biochemical Data Analysis Vol, 3. F. Grisoni, BigChem online course
59 (3) Neural Networks Feed-Forward NN Output Layer 1. Untrained Network 2. Compute the outcome Hidden Layer(s) 3. Compute the error 4. Back propagation learning 5. Repeat until stop criterion Input Layer F. Grisoni, BigChem online course
60 (3) Neural Networks Feed-Forward NN Error Training set Epochs F. Grisoni, BigChem online course
61 (3) Neural Networks Feed-Forward NN Error Validation set Training set Epochs F. Grisoni, BigChem online course
62 (3) Neural Networks Feed-Forward NN Error Validation set Training set Epochs F. Grisoni, BigChem online course
63 (3) Neural Networks Kohonen Maps p dimensional Unsupervised non-linear mapping Topology preserving map 2 dimensional F. Grisoni, BigChem online course
64 (3) Neural Networks Kohonen Maps Input Neurons Kohonen Layer 1. Competitive Learning Similarity to each neuron Winner takes all 2. Collaborative Learning Winning neuron update Update of close neurons Weights F. Grisoni, BigChem online course
65 (3) Neural Networks Kohonen Maps Top map (compounds) F. Grisoni, BigChem online course
66 (3) Neural Networks Kohonen Maps Top map (compounds) Weight maps (p) F. Grisoni, BigChem online course
67 (3) Neural Networks Kohonen Maps Top map (compounds) Weight maps (p) F. Grisoni, BigChem online course
68 Which ML algorithm? Purpose (clustering, regression, classification) Performance vs interpretability Covered chemical space (e.g., AD) Types of included variables F. Grisoni, BigChem online course
69 Summary Machines can learn from our data No ML algorithm always outperforms the others Validation and Applicability Domain assessment are crucial Pay attention to what the performance metric is telling you! F. Grisoni, BigChem online course
70 Supplementary reading Theory and Algorithms Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press. Marini, F. (2009). Neural networks. In: Comprehensive Chemometrics: Chemical and Biochemical Data Analysis - Vol, 3. Online resources [Coursera] Ng, A. Machine Learning, Stanford University. [Online Book] Neural Networks and Deep Learning. F. Grisoni, BigChem online course
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