Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values

Rodríguez-Pérez, Raquel; Bajorath, Jürgen

doi:10.1021/acs.jmedchem.9b01101

Cited by 264 publications

(201 citation statements)

References 61 publications

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“…The SHapley Additive exPlanations (SHAP) approach is an extension of LIME; feature weights are represented as SHapley values from game theory. The SHAP approach has a high potential for rationalizing the predictions made by complex ML models (37). In the present study, we used the SHAP method to observe the influence of each feature on the prediction results during the prediction process applied to each sample.…”

Section: Model Interpretationmentioning

confidence: 99%

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Dai

Fan

et al. 2020

Front. Endocrinol.

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Background: Some patients with acromegaly do not reach the remission standard in the short term after surgery but achieve remission without additional postoperative treatment during long-term follow-up; this phenomenon is defined as postoperative delayed remission (DR). DR may complicate the interpretation of surgical outcomes in patients with acromegaly and interfere with decision-making regarding postoperative adjuvant therapy. Objective: We aimed to develop and validate machine learning (ML) models for predicting DR in acromegaly patients who have not achieved remission within 6 months of surgery. Methods: We enrolled 306 acromegaly patients and randomly divided them into training and test datasets. We used the recursive feature elimination (RFE) algorithm to select features and applied six ML algorithms to construct DR prediction models. The performance of these ML models was validated using receiver operating characteristics analysis. We used permutation importance, SHapley Additive exPlanations (SHAP), and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. Results: Fifty-five (17.97%) acromegaly patients met the criteria for DR, and five features (post-1w rGH, post-1w nGH, post-6m rGH, post-6m IGF-1, and post-6m nGH) were significantly associated with DR in both the training and the test datasets. After the RFE feature selection, the XGboost model, which comprised the 15 important features, had the greatest discriminatory ability (area under the curve = 0.8349, sensitivity = 0.8889, Youden's index = 0.6842). The XGboost model showed good discrimination ability and provided significantly better estimates of DR of patients with acromegaly compared with using only the Knosp grade. The results obtained from permutation importance, SHAP, and LIME algorithms showed that post-6m IGF-1 is the most important feature in XGboost algorithm prediction and showed the reliability and the clinical practicability of the XGboost model in DR prediction. Dai et al. ML Models Predict Acromegaly DR Conclusions: ML-based models can serve as an effective non-invasive approach to predicting DR and could aid in determining individual treatment and follow-up strategies for acromegaly patients who have not achieved remission within 6 months of surgery.

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Section: Model Interpretationmentioning

confidence: 99%

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Dai

Fan

et al. 2020

Front. Endocrinol.

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“…Pope et al 67 adapted gradient-based feature attribution 68,69 for the identification of relevant functional groups in adverse effect prediction 70 . Recently, SHAP 52 was used to interpret relevant features for compound potency and multitarget activity prediction 71 . Hochuli et al 72 compared several feature attribution methodologies, showing how the visualization of attributions assists in the parsing and interpretation of protein-ligand scoring with three-dimensional convolutional neural networks 73,74 .…”

Section: Relevance Of Input Featuresmentioning

confidence: 99%

Drug discovery with explainable artificial intelligence

2020

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arious concepts of 'artificial intelligence' (AI) have been successfully adopted for computer-assisted drug discovery in the past few years 1-3. This advance is mostly owed to the ability of deep learning algorithms, that is, artificial neural networks with multiple processing layers, to model complex nonlinear inputoutput relationships, and perform pattern recognition and feature extraction from low-level data representations. Certain deep learning models have been shown to match or even exceed the performance of the familiar existing machine learning and quantitative structure-activity relationship (QSAR) methods for drug discovery 4-6. Moreover, deep learning has boosted the potential and broadened the applicability of computer-assisted discovery, for example, in molecular design 7,8 , chemical synthesis planning 9,10 , protein structure prediction 11 and macromolecular target identification 12,13. The ability to capture intricate nonlinear relationships between input data (for example, chemical structure representations) and the associated output (for example, assay readout) often comes at the price of limited comprehensibility of the resulting model. While there have been efforts to explain QSARs in terms of algorithmic insights and molecular descriptor analysis 14-19 , deep neural network models notoriously elude immediate accessibility by the human mind 20. In medicinal chemistry in particular, the availability of 'rules of thumb' correlating biological effects with physicochemical properties underscores the willingness, in certain situations, to sacrifice accuracy in favour of models that better fit human intuition 21-23. Thus, blurring the lines between the 'two QSARs' 24 (that is, mechanistically interpretable versus highly accurate models) may be key to accelerated drug discovery with AI 25. Automated analysis of medical and chemical knowledge to extract and represent features in a human-intelligible format dates back to the 1990s 26,27 , but has been receiving increasing attention due to the re-emergence of neural networks in chemistry and healthcare. Given the current pace of AI in drug discovery and related fields, there will be an increased demand for methods that help us understand and interpret the underlying models. In an effort to mitigate the lack of interpretability of certain machine learning models, and to augment human reasoning and decision-making, 28 , attention has been drawn to explainable AI (XAI) approaches 29,30. Providing informative explanations alongside the mathematical models aims to (1) render the underlying decision-making process transparent ('understandable') 31 , (2) avoid correct predictions for the wrong reasons (the so-called clever Hans effect) 32 , (3) avert unfair biases or unethical discrimination 33 and (4) bridge the gap between the machine learning community and other scientific disciplines. In addition, effective XAI can help scientists navigate 'cognitive valleys' 28 , allowing them to hone their knowledge and beliefs on the investigated process 34. While the e...

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“…In the context of activity predictions, Shapley values can also be rationalized as a fair or reasonable allocation of feature importance given a particular model output [19]. Features contribute to the model's output or prediction with different magnitude and sign, which is accounted for by Shapley values.…”

Section: Shapley Valuesmentioning

confidence: 99%

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions

Rodríguez-Pérez

Bajorath

2020

J Comput Aided Mol Des

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382

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Difficulties in interpreting machine learning (ML) models and their predictions limit the practical applicability of and confidence in ML in pharmaceutical research. There is a need for agnostic approaches aiding in the interpretation of ML models regardless of their complexity that is also applicable to deep neural network (DNN) architectures and model ensembles. To these ends, the SHapley Additive exPlanations (SHAP) methodology has recently been introduced. The SHAP approach enables the identification and prioritization of features that determine compound classification and activity prediction using any ML model. Herein, we further extend the evaluation of the SHAP methodology by investigating a variant for exact calculation of Shapley values for decision tree methods and systematically compare this variant in compound activity and potency value predictions with the model-independent SHAP method. Moreover, new applications of the SHAP analysis approach are presented including interpretation of DNN models for the generation of multi-target activity profiles and ensemble regression models for potency prediction.

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Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values

Cited by 264 publications

References 61 publications

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Drug discovery with explainable artificial intelligence

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions

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