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.

Drug discovery is like molecular Tetris. Chemists snap atoms together, adjusting the pieces until everything fits, and ...

The large language model automates literature search, synthesis, and structural analysis to speed up materials discovery and ...

Researchers have developed a human intestinal cell model that closely mimics the structure and function of the human gut, enabling more precise prediction of drug-induced gastrointestinal toxicity ...

In what ways does chromatographic co-elution affect MS2 spectral quality, molecular networking, and the accuracy of ML models trained on MS2 data?

Researchers developed a machine-learning workflow that predicts how chemical reactions will form specific “handed” versions of molecules—critical for safe and effective drugs. Trained on small ...

Innate immune sensors—known as pattern recognition receptors (PRRs)—detect specific molecular components of bacterial or ...

A leading oncologist proposes classifying metastatic cancer by molecular alterations rather than organ origin, arguing that the shift could accelerate drug access.

Abstract: Predicting molecular properties is vital for drug discovery, but experimental measurement is costly and limited by scarce labeled data. Self-supervised molecular pretraining can leverage ...

Recursive language models (RLMs) are an inference technique developed by researchers at MIT CSAIL that treat long prompts as an external environment to the model. Instead of forcing the entire prompt ...