Insilico Medicine (“Insilico”), a clinical-stage, generative AI-driven drug discovery company, today announced its participation in NeurIPS 2025, to be held in San Diego, USA, from December 2–7, 2025. Led by Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine, and Petrina Kamya, PhD, Global Head of AI Platforms, Insilico’s experienced team will participate in the conference.

In a recent pioneering study, researchers at Insilico Medicine ("Insilico"), a generative artificial intelligence (AI)-driven clinical-stage biotechnology company, harnessed its AI-driven generative chemistry platform, Chemistry42, to design a novel, first-in-class PROTAC targeting PKMYT1, D16-M1P2, which employs a dual mechanism of action—inducing PKMYT1 degradation while directly inhibiting its kinase activity. This innovative strategy holds promise for overcoming limitations of existing inhibitors, such as poor selectivity and acquired resistance, and to effectively target both the catalytic and non-catalytic functions of PKMYT1. As a result, the PROTAC demonstrates the potential for a more selective, potent, and durable therapeutic effect.

The research project RePAIR (Reconstructing the Past: Artificial Intelligence and Robotics Meet Cultural Heritage), funded by the European Union, is coming to an end and is presenting its main results: the creation of a robotic infrastructure – built by Italian Institute of Technology -  guided by artificial intelligence and the use of algorithms to reassemble fragmented Pompeian frescoes like a “puzzle”. The entire prototype was validated through initial experimental tests conducted on-site at the Archaeological Park of Pompeii, demonstrating that robotics and artificial intelligence may, in the future, facilitate the work of archaeologists.

Big Earth Data Journal is calling for papers for a Special Issue on Addressing incomplete reference data for leveraging Artificial Intelligence in Earth Observation applications. This special issue focuses on advancing methodologies for Earth Observation application reference data challenges. It gathers pioneering research, theoretical insights and innovative case studies to boost reference data enhancement, aiming to improve the precision and trustworthiness of AI-driven insights and guide academics, industry professionals and policymakers.This special issue focuses on advancing methodologies for Earth Observation application reference data challenges. It gathers pioneering research, theoretical insights and innovative case studies to boost reference data enhancement, aiming to improve the precision and trustworthiness of AI-driven insights and guide academics, industry professionals and policymakers. Welcome to submit!

Using catalytic chemistry, researchers at Institute of Science Tokyo have achieved dynamic control of artificial membranes, enabling life-like membrane behavior. By employing an artificial metalloenzyme that performs a ring-closing metathesis reaction, the team induced the disappearance of phase-separated domains as well as membrane division in artificial membranes, imitating the dynamic behavior of natural biological membranes. This transformative research marks a milestone in synthetic cell technologies, paving the way for innovative therapeutic breakthroughs.

AI and human-movement research intersect in a study that enables precise estimation of hand muscle activity from standard video recordings. Using a deep-learning framework trained on a large, comprehensive multimodal dataset from professional pianists, the researchers introduce a system that accurately reconstructs muscle activation patterns without sensors. This advancement provides a low-cost, non-invasive method for analyzing fine motor control, optimizing rehabilitation strategies, enhancing performance training, and informing future developments in human-machine interaction.

In an increasingly acute surgeon shortage, artificial intelligence could help fill the gap, coaching medical students as they practice surgical techniques.

A new tool, trained on videos of expert surgeons at work, offers students real-time personalized advice as they practice suturing. Initial trials suggest AI can be a powerful substitute teacher for more experienced students.