Combining DELs and machine learning for toxicology prediction

Blay, Vincent; Li, Xiaoyü; Gerlach, Jacob; Urbina, Fabio; Ekins, Sean

doi:10.1016/j.drudis.2022.103351

Cited by 8 publications

(2 citation statements)

References 55 publications

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“…We could also use such models to score very large collections of molecules (such as DNA-encoded libraries) that would be impossible to test in vivo. 76 This approach could be used to prioritize compounds for testing and integrating into chemical safety websites and other tools. Such machine learning models could also be further integrated into chemical detection hardware to identify the potential threat posed by novel molecules in the environment when combined with a sensitive analytical detection system.…”

Section: ■ Discussionmentioning

confidence: 99%

Comparing LD₅₀/LC₅₀ Machine Learning Models for Multiple Species

Lane

Harris

Urbina

et al. 2023

ACS Chem. Health Saf.

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The lethal dose or concentration which kills 50% of the animals (LD50 or LC50) is an important parameter for scientists to understand the toxicity of chemicals in different scenarios that can be used to make go-no-go decisions, and ultimately assist in the choice of the right personal protective equipment needed for containment. The LD50 assessment process has also required the use of many animals although modern methods have reduced the number of rats needed. Since a compound is usually considered highly toxic when the LD50 is lower than 25 mg/kg, such a classification provides potentially valuable safety information to synthetic chemists and other safety assessment scientists. The need for finding alternative approaches such as computational methods is important to ultimately reduce animal use for this testing further still. We now summarize our efforts to use public data for building in vivo LD50 or LC50 classification and regression machine learning models for various species (rat, mouse, fish, and daphnia) and their fivefold cross-validation statistics with different machine learning algorithms as well as an external curated test set for mouse LD50. These datasets consist of different molecule classes, may cover different activity ranges, and also have a range of dataset sizes. The challenges of using such computational models are that their applicability domain will also need to be understood so that they can be used to make reliable predictions for novel molecules. These machine learning models will also need to be backed up with experimental validation. However, such models could also be used for efforts to bridge gaps in individual toxicity datasets. Making such models available also opens them up to potential misuse or dual use. We will summarize these efforts and propose that they could be used for scoring the millions of commercially available molecules, most of which likely do not have a known LD50 or for that matter any data in vitro or in vivo for toxicity.

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Section: ■ Discussionmentioning

confidence: 99%

Comparing LD₅₀/LC₅₀ Machine Learning Models for Multiple Species

Lane

Harris

Urbina

et al. 2023

ACS Chem. Health Saf.

Self Cite

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“…These new modeling architectures are likely rapidly overtaking traditional approaches for performing a variety of cheminformatics analyses. Recurrent neural networks (RNN), and long short-term memory (LSTM) networks 33 , 34 have been found to be very useful in a variety of prediction and optimization tasks 35 . More recently, simplified molecular line entry system (SMILES) 36 strings have been used as input for Sequence-To-Sequence (Seq2Seq) and Transformer models 37 .…”

Section: Introductionmentioning

confidence: 99%

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications

Snyder,

Vignaux,

Ozalp

et al. 2024

Commun Chem

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Recent advances in machine learning (ML) have led to newer model architectures including transformers (large language models, LLMs) showing state of the art results in text generation and image analysis as well as few-shot learning (FSLC) models which offer predictive power with extremely small datasets. These new architectures may offer promise, yet the ‘no-free lunch’ theorem suggests that no single model algorithm can outperform at all possible tasks. Here, we explore the capabilities of classical (SVR), FSLC, and transformer models (MolBART) over a range of dataset tasks and show a ‘goldilocks zone’ for each model type, in which dataset size and feature distribution (i.e. dataset “diversity”) determines the optimal algorithm strategy. When datasets are small ( < 50 molecules), FSLC tend to outperform both classical ML and transformers. When datasets are small-to-medium sized (50-240 molecules) and diverse, transformers outperform both classical models and few-shot learning. Finally, when datasets are of larger and of sufficient size, classical models then perform the best, suggesting that the optimal model to choose likely depends on the dataset available, its size and diversity. These findings may help to answer the perennial question of which ML algorithm is to be used when faced with a new dataset.

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Reliability and Applicability Assessment for Machine Learning Models

Urbina,

Ekins

2024

Computational Drug Discovery

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Combining DELs and machine learning for toxicology prediction

Cited by 8 publications

References 55 publications

Comparing LD₅₀/LC₅₀ Machine Learning Models for Multiple Species

Comparing LD₅₀/LC₅₀ Machine Learning Models for Multiple Species

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications

Reliability and Applicability Assessment for Machine Learning Models

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Combining DELs and machine learning for toxicology prediction

Cited by 8 publications

References 55 publications

Comparing LD50/LC50 Machine Learning Models for Multiple Species

Comparing LD50/LC50 Machine Learning Models for Multiple Species

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications

Reliability and Applicability Assessment for Machine Learning Models

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Product

Resources

About

Comparing LD₅₀/LC₅₀ Machine Learning Models for Multiple Species

Comparing LD₅₀/LC₅₀ Machine Learning Models for Multiple Species