AI-driven pharmatech company Exscientia announced today that it has commenced an oncology drug discovery and development collaboration with the University of Texas MD Anderson Cancer Center to identify novel anti-cancer, cell-intrinsic small-molecule compounds based on jointly identified therapeutic targets. The researchers at MD Anderson’s Therapeutics Discovery division will use Exscientia’s expertise in building machine learning models to help identify druggable targets and in what it terms a “data-agnostic” approach to drug design.

Scientists at the Johns Hopkins Kimmel Comprehensive Cancer Center and its Bloomberg~Kimmel Institute for Cancer Immunotherapy report that they successfully trained a machine learning algorithm to predict, in hindsight, which patients with melanoma would respond to treatment and which would not respond.

Artificial intelligence models can help predict cardiotoxicity risk among patients with renal cell carcinoma treated with VEGF receptor inhibitors, according to study results. Integration of artificial intelligence (AI) models into electronic medical records can help oncologists and other members of the clinical care team identify those who may benefit from cardio-oncology monitoring and treatment, findings presented at International Kidney Cancer Symposium: North America showed.

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La plataforma, conocida como ‘el Google de la Oncología’, ha sido destacada como proyecto innovador y se ha hecho con el galardón en la categoría de ‘Big Data e Inteligencia Artificial”

Los premios, que han celebrado su II edición, reconocen la labor de iniciativas digitales en salud que contribuyen a la sostenibilidad del sistema sanitario y mejoran la calidad de vida de los pacientes

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Immune checkpoint inhibitors (ICIs) are standard-of-care as first-line (1L) therapy for advanced non-small cell lung cancer (aNSCLC) without actionable oncogenic driver mutations. We applied machine learning (ML)-based survival models to a real-world cohort of patients with aNSCLC who received 1L ICI therapy extracted from a US-based electronic health record database.

This is the most advanced machine learning approach for analysing mutational signatures. We know that because we’ve extensively compared it with everything else that exists.

A study published last month in Nature Metabolism shows that a machine learning (ML)-based computational platform can identify specific metabolic targets in ovarian cancer, which could be used in personalized treatment therapies.