Large Language Models as Tools for Molecular Toxicity Prediction: AI Insights into Cardiotoxicity

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

The importance of drug toxicity assessment lies in ensuring the safety and efficacy pharmaceutical compounds. Predicting is crucial development risk assessment. This study compares performance GPT-4 GPT-4o with traditional deep-learning machine-learning models, WeaveGNN, MorganFP-MLP, SVC, KNN, predicting molecular toxicity, focusing on bone, neuro, reproductive toxicity. results indicate that comparable to models certain areas. We utilized combined docking techniques cardiotoxicity three specific targets, examining Chinese medicinal materials listed as both food medicine. approach aimed explore potential mechanisms action. found components Black Sesame, Ginger, Perilla, Sichuan Pagoda Tree Fruit, Galangal, Turmeric, Licorice, Yam, Amla, Nutmeg exhibit toxic effects cardiac target Cav1.2. indicated significant binding affinities, supporting hypothesis cardiotoxic effects.This research highlights ChatGPT properties its significance chemistry, demonstrating facilitation a new paradigm: data set, high-accuracy learning can be generated without requiring computational knowledge or coding skills, making it accessible easy use.

Language: Английский

Artificial intelligence for life sciences: A comprehensive guide and future trends

The Innovation Life, Journal Year: 2024, Volume and Issue: unknown, P. 100105 - 100105

Published: Jan. 1, 2024

<p>Artificial intelligence has had a profound impact on life sciences. This review discusses the application, challenges, and future development directions of artificial in various branches sciences, including zoology, plant science, microbiology, biochemistry, molecular biology, cell developmental genetics, neuroscience, psychology, pharmacology, clinical medicine, biomaterials, ecology, environmental science. It elaborates important roles aspects such as behavior monitoring, population dynamic prediction, microorganism identification, disease detection. At same time, it points out challenges faced by application data quality, black-box problems, ethical concerns. The are prospected from technological innovation interdisciplinary cooperation. integration Bio-Technologies (BT) Information-Technologies (IT) will transform biomedical research into AI for Science paradigm.</p>

Language: Английский

Memol: Mixture of Experts for Multimodal Learning Through Multi-Head Attention to Predict Drug Toxicity

Published: Jan. 1, 2025

Language: Английский