Development of an advanced, interpretable, and validated, machine learning platform, for the discovery of novel acetylcholinesterase inhibitors

Authors: nirajan  bhattarai
marvin k schulte
Venue: IEEE_org_ICIST_2024_Conference
Published: 2024-08-16 06:55:13

Abstract: The development of advanced algorithms, comprehensive exploration of molecular descriptor/feature spaces, model interpretability, and cross-species validation collectively facilitate rapid, accurate, and informed artificial intelligence-driven drug discovery. In this study, we specifically applied these approaches to the identification of novel acetylcholinesterase Inhibitors (AChEIs).  Rapid identification of acetylcholinesterase inhibition can aid in the identification of new therapeutic agents as well as the identification of potential toxic effects of drug molecules.   This study used four major Machine Learning (ML) variants—aggregated (ensemble + classical), AutoML (tpot), graph-based, and fine-tuned BERT large language model (LLM) ChemBERTA.  All models were trained using a curated ChEMBL dataset of AChEIs, employing an IC50 activity cutoff of 1 uM for classification. Models underwent extensive cross-validation, training across 11 different feature sets, consistent hyperparameter optimization, and evaluation using six different species datasets including independent human dataset. Interpretation methods included identifying important features with LIME, fragment/atomic contributions for a prediction, and determining optimal thresholds/hyperparameters. Experimental evaluation of selected compounds will be carried out using modified Ellman’s method. The study identified top performing models, including ensemble extra tree classifier with rdkit features (AUC ROC=0.92) among aggregrate, AutoML (tpot) extra tree classifier with rdkit features (AUC ROC=0.92), graph convolution network (AUC ROC=0.93) among graph based model, and fine-tuned ChemBERTA (ROC AUC=0.91). Extensive validation and hyperparameter optimization demonstrated the superiority of aggregate and AutoML (tpot) models in predicting AChEIs selective for human compared to other species. Promising hits were obtained through screening FDA-approved drugs, natural products from COCONUT, and ZINC's purchasable 250k compounds, followed by docking studies. The study showed a prediction threshold of 0.8 for optimal prediction of positives (>95% accuracy for positives) and pinpointed important features and key structural predictors. In conclusion, the study identified robust machine learning models for rapid detection of acetylcholinesterase inhibition, particularly rdkit feature-based aggregated and AutoML models, which could effectively discriminate human AChE inhibitors from other species

Main paper: 
  
 Appendix:  



Reviews: review: title: title: Good paper
main_review: main_review: This paper has developed an advanced, interpretable, and validated machine-learning platform for the discovery of novel acetylcholinesterase inhibitors. The paper is interesting and well-written. However, some questions need to be answered:
1.How did the integration of advanced algorithms and comprehensive exploration of molecular descriptor/feature spaces contribute to the success of the drug discovery process in identifying novel acetylcholinesterase inhibitors?

2.What are the key advantages of using ensemble and AutoML models over traditional machine learning approaches in predicting acetylcholinesterase inhibition, particularly in distinguishing human AChE inhibitors from those of other species?

3.How do the experimental validation and docking studies support the predictions made by the machine learning models, and what insights do they provide into the potential therapeutic and toxicological effects of the identified AChE inhibitors?
title: title: Accept
main_review: main_review: This study has carried out a comprehensive and in-depth exploration of advanced machine learning techniques in the rapid and accurate identification of acetylcholinesterase inhibitors ( AChEIs ). By using a variety of cutting-edge methods, including ensemble methods, AutoML, graph-based models and fine-tuned large language models ( LLMs ), this study demonstrates the ability of AI-driven drug discovery. The robustness and reliability of the results were highlighted by rigorous cross-validation using the rigorously reviewed ChEMBL dataset and different species datasets. The reviewer has the following questions:

1. How well do the identified models generalize to other classes of inhibitors beyond acetylcholinesterase? Could the same approaches be adapted for different therapeutic targets with similar efficacy?

2. While the study emphasizes model interpretability using LIME and fragment contributions, how practical are these interpretability methods in a real-world clinical setting where quick and actionable insights are crucial?

3. The research shows a strong ability to predict human-specific AChEIs, but how might the models perform when faced with less well-characterized species or strains? Could this approach be extended to predict species-specific inhibitors for a broader range of organisms?
title: title: This paper develops and validates an advanced, interpretable machine learning platform for discovering novel acetylcholinesterase inhibitors. However, the following suggestions need to be considered in the revised manuscript to further improve the quality of this paper.
main_review: main_review: 1. How does this paper demonstrate the superiority of ensemble and AutoML (tpot) models in predicting AChE inhibitors?
2. What methods were used to ensure consistent optimization across different feature sets?
3. How robust are these models when applied to new compounds in practical settings? Are there specific challenges or limitations encountered?