Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Shavalieva, Gulnara; Papadokonstantakis, Stavros; Peters, Gregory

doi:10.1021/acs.jcim.1c01079

Cited by 8 publications

(6 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modern high-throughput techniques for the measurement of proxy points (e.g. : LogD), along with increasingly powerful automated text mining and data extraction technologies (Han et al, 2010;Tarasova et al, 2019;NextMove Software, 2022;Shavalieva et al, 2022) have enhanced the acquisition of Frontiers in Chemistry frontiersin.org physicochemical and biological data through internal laboratories (Zhang et al, 2018), CROs, and large-scale data mining projects, sometimes resulting in the publication of FAIR-compliant data (Wilkinson et al, 2016). However, many data-related issues still impede the development of accurate "Ag-adapted" models.…”

Section: High-quality Datasetsmentioning

confidence: 99%

Cheminformatics and artificial intelligence for accelerating agrochemical discovery

Djoumbou-Feunang,

Wilmot,

Kinney

et al. 2023

Front. Chem.

View full text Add to dashboard Cite

The global cost-benefit analysis of pesticide use during the last 30 years has been characterized by a significant increase during the period from 1990 to 2007 followed by a decline. This observation can be attributed to several factors including, but not limited to, pest resistance, lack of novelty with respect to modes of action or classes of chemistry, and regulatory action. Due to current and projected increases of the global population, it is evident that the demand for food, and consequently, the usage of pesticides to improve yields will increase. Addressing these challenges and needs while promoting new crop protection agents through an increasingly stringent regulatory landscape requires the development and integration of infrastructures for innovative, cost- and time-effective discovery and development of novel and sustainable molecules. Significant advances in artificial intelligence (AI) and cheminformatics over the last two decades have improved the decision-making power of research scientists in the discovery of bioactive molecules. AI- and cheminformatics-driven molecule discovery offers the opportunity of moving experiments from the greenhouse to a virtual environment where thousands to billions of molecules can be investigated at a rapid pace, providing unbiased hypothesis for lead generation, optimization, and effective suggestions for compound synthesis and testing. To date, this is illustrated to a far lesser extent in the publicly available agrochemical research literature compared to drug discovery. In this review, we provide an overview of the crop protection discovery pipeline and how traditional, cheminformatics, and AI technologies can help to address the needs and challenges of agrochemical discovery towards rapidly developing novel and more sustainable products.

show abstract

Section: High-quality Datasetsmentioning

confidence: 99%

Cheminformatics and artificial intelligence for accelerating agrochemical discovery

Djoumbou-Feunang,

Wilmot,

Kinney

et al. 2023

Front. Chem.

View full text Add to dashboard Cite

show abstract

“…With the rapid advancements in computer science, deep learning (DL) algorithms have significantly enhanced the prediction accuracy of molecular property modeling tasks by effectively capturing complex relationships between chemical structures and diverse properties. − The molecular graph representation, compared to descriptors, simplified molecular input line entry system (SMILES) formulas, and other molecule representations, has shown great potential. , Indeed, graph-based DL models often compete with or surpass traditional descriptor-based models in various property prediction tasks, including lipophilicity and ToxCast. , In recent years, graph-based frameworks like graph convolutional networks (GCNs), graph attention networks (GATs), message passing neural networks (MPNNs), and Attentive FP have demonstrated exceptional performance in molecular prediction tasks. − However, these algorithms have rarely been applied to liver microsomal stability prediction. Moreover, the focus of recent studies has primarily been on comparative analysis of model results, often overlooking the interpretability of graph-based algorithms themselves. ,− , …”

Section: Introductionmentioning

confidence: 99%

Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence

Long,

Jiang,

Shi

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.

show abstract

“…Computational toxicology is rapidly evolving with the proliferation of commercial databases and advances in modern computational tools for the analysis of large data sets, − since it can integrate a variety of information and data to complement and extend traditional experimental toxicology research methods and encompasses multiple disciplines. , The computational toxicology tools can be used to prioritize chemical tests and further provide some mechanistic insights. , …”

Section: Introductionmentioning

confidence: 99%

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity

Zhao

Sun

Zhang

et al. 2022

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Neurotoxicity can be resulted from many diverse clinical drugs, which has been a cause of concern to human populations across the world. The detection of drug-induced neurotoxicity (DINeurot) potential with biological experimental methods always required a lot of budget and time. In addition, few studies have addressed the structural characteristics of neurotoxic chemicals. In this study, we focused on the computational modeling for drug-induced neurotoxicity with machine learning methods and the insights into the structural characteristics of neurotoxic chemicals. Based on the clinical drug data with neurotoxicity effects, we developed 35 different classifiers by combining five different machine learning methods and seven fingerprint packages. The bestperforming model achieved good results on both 5-fold cross-validation (balanced accuracy of 76.51%, AUC value of 0.83, and MCC value of 0.52) and external validation (balanced accuracy of 83.63%, AUC value of 0.87, and MCC value of 0.67). The model can be freely accessed on the web server DINeuroTpredictor (http://dineurot.sapredictor.cn/). We also analyzed the distribution of several key molecular properties between neurotoxic and non-neurotoxic structures. The results indicated that several physicochemical properties were significantly different between the neurotoxic and non-neurotoxic compounds, including molecular polar surface area (MPSA), AlogP, the number of hydrogen bond acceptors (nHAcc) and donors (nHDon), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). In addition, 18 structural alerts responsible for chemical neurotoxicity were identified. The structural alerts have been integrated with our web server SApredictor (http://www.sapredictor.cn). The results of this study could provide useful information for the understanding of the structural characteristics and computational prediction for chemical neurotoxicity.

show abstract

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Cited by 8 publications

References 102 publications

Cheminformatics and artificial intelligence for accelerating agrochemical discovery

Cheminformatics and artificial intelligence for accelerating agrochemical discovery

Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity

Contact Info

Product

Resources

About