Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals

Hong, Huixiao; Liu, Jie; Ge, Weigong; Sakkiah, Sugunadevi; Guo, Wenjing; Yavaş, Gökhan; Zhang, Chaoyang; Gong, Ping; Tong, Weida; Patterson, Tucker A.

doi:10.1007/978-3-031-20730-3_12

Cited by 3 publications

(1 citation statement)

References 103 publications

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“…This approach significantly reduces the need for time-consuming and costly experimental assays. Central to QSAR/QSPR analysis is the concept of molecular similarity, which is usually measured based on various molecular descriptors and fingerprints [7,8]. Molecular descriptors are numerical descriptions of the structural features of a chemical and are widely used in the development of predictive models of predicting biological activity and chemical properties [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Li,

Huang,

Xia

et al. 2024

Biomolecules

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Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

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Section: Introductionmentioning

confidence: 99%

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Li,

Huang,

Xia

et al. 2024

Biomolecules

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Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study

Liu,

Khan,

Guo

et al. 2024

Expert Opinion on Drug Metabolism & Toxicology

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Development of a comprehensive open access “molecules with androgenic activity resource (MAAR)” to facilitate risk assessment of chemicals

Dong,

Hardy,

Liu

et al. 2024

Exp. Biol. Med.

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The increasing prevalence of endocrine-disrupting chemicals (EDCs) and their potential adverse effects on human health underscore the necessity for robust tools to assess and manage associated risks. The androgen receptor (AR) is a critical component of the endocrine system, playing a pivotal role in mediating the biological effects of androgens, which are male sex hormones. Exposure to androgen-disrupting chemicals during critical periods of development, such as fetal development or puberty, may result in adverse effects on reproductive health, including altered sexual differentiation, impaired fertility, and an increased risk of reproductive disorders. Therefore, androgenic activity data is critical for chemical risk assessment. A large amount of androgenic data has been generated using various experimental protocols. Moreover, the data are reported in different formats and in diverse sources. To facilitate utilization of androgenic activity data in chemical risk assessment, the Molecules with Androgenic Activity Resource (MAAR) was developed. MAAR is the first open-access platform designed to streamline and enhance the risk assessment of chemicals with androgenic activity. MAAR’s development involved the integration of diverse data sources, including data from public databases and mining literature, to establish a reliable and versatile repository. The platform employs a user-friendly interface, enabling efficient navigation and extraction of pertinent information. MAAR is poised to advance chemical risk assessment by offering unprecedented access to information crucial for evaluating the androgenic potential of a wide array of chemicals. The open-access nature of MAAR promotes transparency and collaboration, fostering a collective effort to address the challenges posed by androgenic EDCs.

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Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals

Cited by 3 publications

References 103 publications

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study

Development of a comprehensive open access “molecules with androgenic activity resource (MAAR)” to facilitate risk assessment of chemicals

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