While public conversations may often focus on chatbots and workplace automation, To sees specialized AI systems trained on ...

The drug development pipeline is a costly and lengthy process. Identifying high-quality "hit" compounds-those with high potency, selectivity, and favorable metabolic properties-at the earliest stages ...

Drug discovery is like molecular Tetris. Chemists snap atoms together, adjusting the pieces until everything fits and suddenly, a molecule makes a promising new medicine. Normally, creating better ...

Ardigen’s AI and computational biology capabilities will strengthen VERAXA’s in-house AI efforts to optimize cancer target pair selection for conditionally active T cell engagers and antibody-drug ...

A feature News and Perspectives story on technological advances in oncology was published in Journal of Medical Internet ...

“It’s like a paradigm shift approach… to drive discovery”: a new machine-learning model predicts how molecules will influence gene expression and has been used to pick out promising drug candidates ...

Drug discovery has not yet had its “ChatGPT moment,” according to Arman Zaribafiyan, PhD, head of product, AI simulation and platforms, at SandboxAQ, in an interview with GEN. “We can’t rely only on ...

How are we improving the way the field of drug discovery creates machine learning algorithms to predict a protein’s interactions with a small molecule? The drug development pipeline is a costly and ...

The drug discovery industry’s investment in and reliance on AI have reached unprecedented levels over the last five years. Amid this excitement, one fundamental question is often overlooked: what ...

Computational drug discovery and design strategies encompass an array of in silico methods that accelerate the identification, optimisation and validation of therapeutic candidates. Advances in ...