AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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62 papers
The evaluation reveals that model performance shifts systematically with the availability of target-specific experimental evidence: PLMs demonstrate proficiency in open-ended single-mutant generation by leveraging protein-specific priors, whereas LLMs and agents perform strongly in multi-mutant ranking, particularly wh...
Protein language models are trained primarily with masked language modeling (MLM), which predicts masked amino-acid identities, but joint-embedding predictive architectures (JEPA) instead predict latent representations, but have not been applied to proteins.
It is found that, at variance with networks not based on the attention mechanism, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties; this is true both for synthetic and natural protein sequences.
A hierarchical Bayesian multitask learning model that is applicable to the general multi-task binary classification learning problem where the model assumes a shared sparsity structure across different tasks is proposed and derived based on variational inference to approximate the posterior distribution.
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Many molecular Transformers lack probabilistic latent variables for posterior inference and latent interpolation. We introduce STAR-VAE, a SELFIES-encoded, Transformer-based, AutoRegressive Variational AutoEncoder combining a bidirectional encoder with an autoregressive decoder pretrained on 79 million PubChem molecule...
Virtual screening aims to prioritize active compounds from large chemical libraries within a limited experimental budget. When applying Boltz-2 to virtual screening, a key challenge is how to use limited experimental data from the target assay to improve the prioritization of active compounds. We investigated whether f...
This work develops a systematic understanding of how physical patterns can alternatively be discovered directly from data by training a model without domain-specific priors, including any manually defined atomistic pairwise interactions, and finds that the model autonomously recovers key physical structure.
STOMP is a powerful, robust multi-objective alignment algorithm that can meaningfully improve post-training in multiple domains and achieves or ties for the highest hypervolumes on 16/18 protein tasks and 5/6 natural language tasks.
ReGeoDTA is developed, a representation-preserving framework that maintains affinity-relevant chemical heterogeneity in molecular representations and continuous geometric relationships in protein structures that identifies representation fidelity as an upstream design principle for accurate and generalizable drug--targ...
This work proposes LabAgent, a reproduce and discovery harness tailored for a lab's continuous work that ranks first over commercial generalist agents in every domain, and demonstrates accurate reproduction of a published figure.
GRACE (Geometric Residual Adduct Conditioning via Early-fusion), a 3D CCS predictor that adapts a pretrained molecular geometry encoder using geometric residual adduct conditioning via early fusion, is presented.
Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator t...
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