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Quantitative Structure-Activity Relationship

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Applying machine learning techniques for ADME-Tox prediction: a review.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, eff...

AI-Driven Design and Development of Nontoxic DYRK1A Inhibitors.

Journal of medicinal chemistry
Dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) is implicated in several human diseases, including DYRK1A syndrome, cancer, and neurodegenerative disorders such as Alzheimer's disease, making it a relevant therapeutic target. I...

Discovery of New HER2 Inhibitors via Computational Docking, Pharmacophore Modeling, and Machine Learning.

Molecular informatics
The human epidermal growth factor receptor 2 (HER2) is a critical oncogene implicated in the development of various aggressive cancers, particularly breast cancer. Discovering novel HER2 inhibitors is crucial for expanding therapeutic options for HER...

Combining Machine Learning and Electrophysiology for Insect Odorant Receptor Studies.

Methods in molecular biology (Clifton, N.J.)
Insects rely on olfaction in many aspects of their life, and odorant receptors are key proteins in this process. Whereas a plethora of insect odorant receptor sequences is available, most of them are still orphan or uncompletely characterized, since ...

Applicability Domain for Trustable Predictions.

Methods in molecular biology (Clifton, N.J.)
In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), understanding and correctly applying the concept of the applicability domain (AD) has emerged as an essential part. This chapter begins with an introduction ...

Automated Workflows for Data Curation and Machine Learning to Develop Quantitative Structure-Activity Relationships.

Methods in molecular biology (Clifton, N.J.)
The recent advancements in machine learning and the new availability of large chemical datasets made the development of tools and protocols for computational chemistry a topic of high interest. In this chapter a standard procedure to develop Quantita...

Development of machine learning-based quantitative structure-activity relationship models for predicting plasma half-lives of drugs in six common food animal species.

Toxicological sciences : an official journal of the Society of Toxicology
Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products derived from animals. This study focuses on developing a quantitative structure-activity relationshi...

Application of Deep Learning for Studying NMDA Receptors.

Methods in molecular biology (Clifton, N.J.)
Artificial intelligence underwent remarkable advancement in the past decade, revolutionizing our way of thinking and unlocking unprecedented opportunities across various fields, including drug development. The emergence of large pretrained models, su...

Predicting Antitumor Activity of Anthrapyrazole Derivatives using Advanced Machine Learning Techniques.

Current computer-aided drug design
BACKGROUND: Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors.

Profiling mechanisms that drive acute oral toxicity in mammals and its prediction via machine learning.

Toxicological sciences : an official journal of the Society of Toxicology
We present a mechanistic machine-learning quantitative structure-activity relationship (QSAR) model to predict mammalian acute oral toxicity. We trained our model using a rat acute toxicity database compiled by the US National Toxicology Program. We ...