The article discusses how Google Antigravity, combined with DeepMind’s Cheminformatics Skills, can speed up drug discovery processes. It explains how these tools help researchers search for compounds and suggest new rigidified macrocycles. The focus is on accelerating workflow efficiency in pharmaceutical research.
César de la Fuente’s laboratory employs Codex and ChatGPT to explore both living and extinct genomes in search of novel antimicrobial molecules. By leveraging these AI tools, the team aims to identify candidates that could help combat drug‑resistant infections.
arXiv:2504. 17247v3 Announce Type: replace Abstract: Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as limited controllability, lack of representations that efficiently model antimicrobial properties, and low experimental hit rates.
By Diogo Soares, Leon Hetzel, Paulina Szymczak, Marcelo Der Torossian Torres, Johanna Sommer, Cesar de la Fuente-Nunez, Fabian Theis, Stephan G\"unnemann, Ewa Szczurek
PepEVOLVE is a dynamic, position‑aware framework for optimizing macrocyclic peptides that learns where to edit and how to improve multiple properties. It enhances pretraining with dynamic masking and CHUCKLES shifting, employs a multi‑armed‑bandit router to identify high‑reward residues, and uses an evolving optimization algorithm with group‑relative advantage to stabilize updates. In in‑silico tests on a Rev‑binding macrocycle benchmark, PepEVOLVE surpassed PepINVENT, achieving higher permeability‑weighted scores and converging faster while respecting structural constraints.
By Trieu Nguyen, Hao-Wei Pang, Shasha Feng
Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE...
OpenAI and Molecule. one show how a near-autonomous AI chemist using GPT-5.