Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species

Abstract

Acetylcholinesterase (AChE) is an important enzyme and target for human therapeutics, environmental safety, and global food supply. Inhibitors of this enzyme are also used for pest elimination and can be misused for suicide or chemical warfare. Adverse effects of AChE pesticides on nontarget organisms, such as fish, amphibians, and humans, have also occurred as a result of biomagnifications of these toxic compounds. We have exhaustively curated the public data for AChE inhibition data and developed machine learning classification models for seven different species. Each set of models were built using up to nine different algorithms for each species and Morgan fingerprints (ECFP6) with an activity cutoff of 1 μM. The human (4075 compounds) and eel (5459 compounds) consensus models predicted AChE inhibition activity using external test sets from literature data with 81% and 82% accuracy, respectively, while the reciprocal cross (76% and 82% percent accuracy) was not species-specific. In addition, we also created machine learning regression models for human and eel AChE inhibition to return a predicted IC50 value for a queried molecule. We did observe an improved species specificity in the regression models, where a human support vector regression model of human AChE inhibition (3652 compounds) predicted the IC50s of the human test set to a better extent than the eel regression model (4930 compounds) on the same test set, based on mean absolute percentage error (MAPE = 9.73% vs 13.4%). The predictive power of these models certainly benefits from increasing the chemical diversity of the training set, as evidenced by expanding our human classification model by incorporating data from the Tox21 library of compounds. Of the 10 compounds we tested that were predicted active by this expanded model, two showed >80% inhibition at 100 μM. This machine learning approach therefore offers the ability to rapidly score massive libraries of molecules against the models for AChE inhibition that can then be selected for future in vitro testing to identify potential toxins. It also enabled us to create a public website, MegaAChE, for single-molecule predictions of AChE inhibition using these models at .

Introduction to the Special Issue: AI Meets Toxicology

“…In several typical computational toxicology approaches, machine learning and deep learning methods are employed to tackle QSAR problems related to mutagenicity, organ toxicity, and drug-induced liver injury, showcasing their potential for accurate toxicity prediction. Vigaux et al 11 developed machine learning classification and regression models for predicting AChE inhibition activity and IC50 values, respectively, in humans and eels, achieving high accuracy and species specificity, and created a public Web site, MegaAChE, for single-molecule predictions of AChE inhibition. The prediction of Ames mutagenicity is of particular concern in regulatory and pharmacological toxicology.…”

“…The methods described in this special issue cover a wide range of AI methods ranging from expert systems, ,, over similarity measures including read-across methods, − to classical machine learning such as random forests (RF), support vector machines (SVM), and artificial neural networks (ANN) − ,, to deep learning (DL) methods ,,− , including equivariant neural networks, deep generative models, and even large language models . In addition to models relying purely on the chemical structure, there is a notable trend of bringing in additional modalities to improve or inform predictive models. , In the following, we provide an overview of the AI approaches used in the publications contained in the SI.…”

“…The tasks and problems spanned by the proposed methods are also highly diverse and range from classical quantitative structure–activity/property relationship (QSAR/QSPR) problems, such as the prediction of drug-induced liver injury (DILI), , Ames mutagenicity, , or acetylcholinesterase (AChE) inhibition, to categorization of chemicals, generating transcriptomic profiles and classifying parts of regulatory documents . In their paper, “Transparency in Modeling through Careful Application of OECD’s QSAR/QSPR Principles via a Curated Water Solubility Data Set”, the authors revisited the five Organisation for Economic Co-operation and Development (OECD) principles for QSAR models.…”

“…Most of the presented methods perform prediction tasks on molecules (for exceptions, see paragraphs below) and, thus, have to use representations of molecules. While for data handling, the simplified molecular-input line-entry system (SMILES) representation is often used, the predominant representations of molecules for modeling are still extended connectivity fingerprint (ECFP) or Morgan fingerprints. − ,− Several publications use graph neural networks that operate on the molecular graph. , Aside from the chemical structure, there is a growing tendency to incorporate biological characterizations and read-outs, for example, via cell morphology , or transcriptomics . The utilization of diverse representations, ranging from molecular structures to biological features, enhances the predictive models showcased in this section and could improve the comprehensive understanding of toxicological properties.…”

Artificial intelligence in clinical pharmacology: A case study and scoping review of large language models and bioweapon potential

“…Scenarios where AI, particularly when combined with publicly available biological information, could pose security threats were discussed, ranging from espionage and health fraud to the creation of novel In addition to the articles discussed, other papers, not focused on the development of bioweapons per se, also mentioned the possibility of dual use of AI in drug development. 23,24…”