RedCircle VERIFY_TOKEN_707f3d9e-72ac-4e47-8f7c-274f79ca0204 -- Rendered At Fri, 16 Oct 2026 07:03:42 +0000 https://redcircle.com/shows/carles-summoned en-US All rights reserved. Carles C Ai generated summaries 707f3d9e-72ac-4e47-8f7c-274f79ca0204 Ai generated summaries

]]> episodic no Carles C carles.corbi@gmail.com false full Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World Carles C The provided text explores the increasing value of machine learning (ML) for predicting molecular properties and toxicity in drug discovery, emphasizing its potential to augment or replace traditional experimental processes. It highlights five crucial pillars for success in ML-driven toxicity prediction: data set selection, structural representations, model algorithms, model validation, and translation of predictions to decision-making. The sources discuss various types of chemical representations, including 2D, 3D, and graph-based methods, along with the advantages and limitations of different ML model types, such as generative models. Finally, the importance of robust model validation strategies, including retrospective and prospective approaches, is underscored, along with the critical role of understanding pharmacokinetics for human-relevant decision-making in drug safety.

]]> <p>The provided text explores the increasing value of <strong>machine learning (ML)</strong> for predicting molecular properties and <strong>toxicity</strong> in drug discovery, emphasizing its potential to augment or replace traditional experimental processes. It highlights <strong>five crucial pillars for success</strong> in ML-driven toxicity prediction: <strong>data set selection</strong>, <strong>structural representations</strong>, <strong>model algorithms</strong>, <strong>model validation</strong>, and <strong>translation of predictions to decision-making</strong>. The sources discuss various types of <strong>chemical representations</strong>, including 2D, 3D, and graph-based methods, along with the advantages and limitations of different <strong>ML model types</strong>, such as generative models. Finally, the importance of robust <strong>model validation strategies</strong>, including retrospective and prospective approaches, is underscored, along with the critical role of understanding <strong>pharmacokinetics</strong> for human-relevant decision-making in drug safety.</p> 90639db8-a984-4d8c-98ad-82922803f58b https://redcircle.com/shows/707f3d9e-72ac-4e47-8f7c-274f79ca0204/episodes/b1279f70-626d-467f-a7e9-cf383207fe42 Thu, 19 Jun 2025 02:29:38 +0000 1108 false