RosettaSearch applies LLM-driven multi-objective search at inference time to improve backbone-conditioned protein sequences, recovering designs with 18-68% better structural fidelity and 2.5x higher success rates than single-pass models like LigandMPNN.
J., Bambrick, J., Bodenstein, S
12 Pith papers cite this work. Polarity classification is still indexing.
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Dual Triangle Attention achieves effective bidirectional attention with built-in positional inductive bias via dual triangular masks, outperforming standard bidirectional attention on position-sensitive tasks and showing strong masked language modeling results with or without positional embeddings.
Presents Hack-Verifiable TextArena, a benchmark that embeds verifiable reward hacking opportunities into environments to enable deterministic measurement of exploitation by language models.
CrystalBoltz performs experiment-guided posterior sampling with diffusion models on structure-factor amplitudes for protein structure determination, reporting lower RMSD and R-factors than baselines with 33x faster runtime.
A two-layer certification framework decouples knowledge validity from human authorship to accommodate AI-enabled research in existing publication systems.
ADIOS applies opponent shaping in a meta-learning setup to create antibodies that target current and future viral variants while biasing evolution toward weaker strains, demonstrated in Absolut! simulations.
Explicit E(3)-equivariance in neural CFD surrogates improves generalization on diverse-geometry hemodynamics benchmarks but degrades in-distribution performance on strongly aligned aerodynamics data, consistently beating data augmentation.
Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.
An integrated framework with ConGA-PepPI for PepPI prediction and binding-site localization plus TC-PepGen for target-conditioned peptide generation reports 0.839 accuracy and 0.921 AUROC in cross-validation along with 40.39% of generated peptides exceeding native templates on AlphaFold 3 ipTM.
AIMBio-Mat is a conceptual blueprint for an AI-native, FAIR, governance-aware decision layer that formulates biomedical-materials discovery as constrained multi-objective optimization under uncertainty.
A putative homotrimeric structure of BP180 is predicted with Boltz-2 and shown to remain mostly folded over 500 ns MD trajectories, with a stiff NC16A domain and flexible Col-15.
Agentic AI platforms autonomously train 87%-accurate PPI prediction models on protein-disjoint data and induce aligning human-readable rules for human-human and virus-human interactions.
citing papers explorer
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RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design
RosettaSearch applies LLM-driven multi-objective search at inference time to improve backbone-conditioned protein sequences, recovering designs with 18-68% better structural fidelity and 2.5x higher success rates than single-pass models like LigandMPNN.
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Dual Triangle Attention: Effective Bidirectional Attention Without Positional Embeddings
Dual Triangle Attention achieves effective bidirectional attention with built-in positional inductive bias via dual triangular masks, outperforming standard bidirectional attention on position-sensitive tasks and showing strong masked language modeling results with or without positional embeddings.
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Hack-Verifiable Environments: Towards Evaluating Reward Hacking at Scale
Presents Hack-Verifiable TextArena, a benchmark that embeds verifiable reward hacking opportunities into environments to enable deterministic measurement of exploitation by language models.
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CrystalBoltz: End-to-End Protein Structure Determination via Experiment-Guided Diffusion for X-Ray Crystallography
CrystalBoltz performs experiment-guided posterior sampling with diffusion models on structure-factor amplitudes for protein structure determination, reporting lower RMSD and R-factors than baselines with 33x faster runtime.
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Rethinking Publication: A Certification Framework for AI-Enabled Research
A two-layer certification framework decouples knowledge validity from human authorship to accommodate AI-enabled research in existing publication systems.
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ADIOS: Antibody Development via Opponent Shaping
ADIOS applies opponent shaping in a meta-learning setup to create antibodies that target current and future viral variants while biasing evolution toward weaker strains, demonstrated in Absolut! simulations.
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Symmetry in the Wild: The Role of Equivariance in Neural Fluid Surrogates
Explicit E(3)-equivariance in neural CFD surrogates improves generalization on diverse-geometry hemodynamics benchmarks but degrades in-distribution performance on strongly aligned aerodynamics data, consistently beating data augmentation.
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Benchmarking open-source tools for in silico antiviral drug discovery
Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.
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An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen
An integrated framework with ConGA-PepPI for PepPI prediction and binding-site localization plus TC-PepGen for target-conditioned peptide generation reports 0.839 accuracy and 0.921 AUROC in cross-validation along with 40.39% of generated peptides exceeding native templates on AlphaFold 3 ipTM.
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AIMBio-Mat: An AI-Native FAIR Platform for Closed-Loop Materials Discovery and Biomedical Translation
AIMBio-Mat is a conceptual blueprint for an AI-native, FAIR, governance-aware decision layer that formulates biomedical-materials discovery as constrained multi-objective optimization under uncertainty.
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A putative, computationally stable structure of homotrimeric BP180/collagen XVII
A putative homotrimeric structure of BP180 is predicted with Boltz-2 and shown to remain mostly folded over 500 ns MD trajectories, with a stiff NC16A domain and flexible Col-15.
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Agentic AI platforms for autonomous training and rule induction of human-human and virus-human protein-protein interactions
Agentic AI platforms autonomously train 87%-accurate PPI prediction models on protein-disjoint data and induce aligning human-readable rules for human-human and virus-human interactions.