Researchers at Brown University found that AI chatbots routinely violate core mental health ethics standards, underscoring the need for legal standards and oversight as use of these tools increases.

On October 2, the third edition of Insilico Medicine’s Pharma.AI Quarterly Launch webinar—titled “Towards Pharmaceutical Superintelligence” —was successfully closed with around 300 registrants all over the world from universities, healthcare institutions, international pharmaceutical companies, and the innovative biotech industry. During the webinar, Insilico software team illustrated the most up-to-date abilities of the Pharma.AI platform while presenting live demos as well as case studies, and the highlights are summarized as below.

Researchers at the HUN-REN Biological Research Centre, Szeged, Hungary, have developed an artificial-intelligence-assisted technology capable of analyzing up to one hundred patient-derived cell samples simultaneously.

The new method, described in Nature Communications, could significantly accelerate drug development and advance the field of personalized medicine.

Researchers at the HUN-REN Biological Research Centre in Szeged, Hungary, have developed an artificial-intelligence-assisted technology capable of analyzing up to one hundred patient-derived cell samples simultaneously.

The new method, described in Nature Communications, could significantly accelerate drug development and advance the field of personalized medicine.

Traditional geotechnical investigations provide data only at discrete borehole locations, leaving vast areas uncharacterized. This spatial gap often leads to unforeseen ground conditions during construction, causing costly delays, design modifications, and occasionally catastrophic failures. Now, a novel integrated geophysical-machine learning approach, using k-means clustering technique, by a team of researchers from Shibaura Institute of Technology provides continuous subsurface characterization, enabling evidence-based decision-making throughout project lifecycles.

Understanding molecular diversity is fundamental to biomedical research and diagnostics, but existing analytical tools struggle to distinguish subtle variations in the structure or composition among biomolecules. A new method using solid-state nanopores — tiny tunnels that a protein or other molecule can pass through — and machine learning suggests a way to help identify and classify proteins more accurately.