Computational peptide-target interaction (PTI) prediction has advanced rapidly as a tool for drug discovery, yet the field lacks agreed evaluation standards. Discrepancies in metrics, negative sampling strategies, and dataset construction across publications currently render cross-study comparisons methodologically int...
William J. Waldock, Guang-Shun Wang, A. Darzi et al.· Frontiers in Bioinformatics· 0 citations
Mitochondria are bacteria-derived organelles that coordinate major innate immune pathways, but whether mitochondrial genomes themselves encode direct antimicrobial functions is underexplored. Here, we test the hypothesis that ancient human mitochondrial DNA is not only an evolutionary record, but also contains encrypte...
M. D. Torres, A. Ali, Hyun-Su Lee et al.· bioRxiv· 0 citations
OmegAMP, a conditional diffusion framework controlling net charge, mean hydrophobicity, and sequence length, supporting de novo, analog, and motif-guided design, opens a programmable route to new peptide antibiotics whose mechanism and safety follow from the chosen prototype.
Paulina Szymczak, Marcelo Der Torossian Torres, Diogo Soares et al.· bioRxiv· 0 citations
This Review surveys machine learning methods, including surrogate modelling, generative design and active learning loops, for predicting and optimizing properties across metals, polymers, hydrogels, ceramics and biological biomaterials, and outlines key datasets, challenges, and future autonomous discovery pipelines.
Xiao-Qiong Xia, Tianang Leng, Andrés J. García et al.· Nature Reviews Bioengineerin...· 3 citations
Diagnostics reveal that RL on PLMs is governed by two reward properties: verifiability, whether the reward is a fixed environment or a learned surrogate vulnerable to distribution shift, and coverage, the fraction of sequence space giving an informative gradient.
Hanqun Cao, Hongrui Zhang, Junde Xu et al.· Proceedings of the 32nd ACM...· 0 citations
Reinforcement learning (RL) is increasingly applied to Protein Language Models (PLMs), yet its effectiveness varies across tasks, and standard metrics such as pass@k can rise even when the model's solvable problem set is shrinking. We introduce two capability-level diagnostics. The Expansion-Shrinkage Ratio (ESR) measu...
Hanqun Cao, Hongrui Zhang, Junde Xu et al.· Proceedings of the 32nd ACM...· 0 citations
ApexFold, an environment-conditioned AI framework that combines sequence representations with physicochemical descriptors of the surrounding medium to predict circular-dichroism-derived fractions of α-helical, β-like, and unstructured conformations is developed.
M. D. Torres, Hanqun Cao, César de la Fuente-Núñez· bioRxiv· 0 citations
Findings identify cyclotide grafting as a strategy to improve peptide stability and intracellular delivery, and support MCo-KTR2 as a scaffold for further optimization against intracellular MRSA infections.
Álvaro Mourenza, Jesús Llano-Verdeja, Pablo Castañera et al.· Molecular Biomedicine· 0 citations
The transition from classical "dirt mining" and phenotypic screening toward digital discovery approaches that treat chemical structures and biological sequences as searchable, engineerable substrates for antibiotic innovation is reviewed.
A. Crysler, César de la Fuente-Núñez· Cell Host and Microbe· 1 citation
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