The article discusses how DeepMind is speeding up the discovery of new antimicrobial peptides by employing Google Antigravity for Science. It highlights the use of this technology to accelerate the search process for novel peptides that could combat microbial threats.
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.
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.
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.
arXiv:2606. 17127v1 Announce Type: cross Abstract: Antimicrobial resistance causes to over a million deaths annually.
arXiv:2611.00749v2 Announce Type: replace Abstract: Liquid-phase peptide synthesis (LPPS) offers a scalable route to peptide manufacturing, but route development still depends on empirical solvent sc...
Mirror-Score is a calibrated, inference‑only scoring framework that evaluates heterochiral D‑peptide/L‑protein complexes, addressing the inadequacies of raw ProteinMPNN negative log‑likelihood (NLL) ranking. The authors benchmarked 31 crystal complexes across four target families, showing that NLL has poor affinity correlation (Spearman 0.19) while Boltz‑2 mirror‑space co‑folding confidence and pLDDT metrics can correctly rank peptides within a viral‑entry family. The study also proposes a prospective design protocol for antimicrobial‑resistance targets and releases all code, data, and structures publicly.
arXiv:2511.23120v2 Announce Type: replace Abstract: Pretrained transformers provide rich, general-purpose embeddings, which are transferred to downstream tasks. However, current transfer strategies:...
Freeze, Diffuse, Decode (FDD) is a diffusion-based framework that adapts pre‑trained transformer embeddings to downstream tasks while preserving their geometric structure. By propagating supervised signals along the intrinsic manifold of frozen embeddings, FDD produces low‑dimensional, predictive, and interpretable representations. In antimicrobial peptide design, these representations support property prediction, retrieval, and latent‑space interpolation.
