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60 papers
Deep learning models have achieved strong performance in artificial intelligence for science, yet their black-box nature limits our understanding of how they learn scientific tasks. Existing methods for interpretability provide limited insight into how models organize evidence and evolve during learning. We introduce e...
AssayRouter treats completed assays as pseudo-targets and labels each candidate by its post-fit utility: the reduction in held-out discovery loss when the candidate is added to the local target predictor.
This work introduces a framework that augments force matching with stochastic Hessian-vector product (HVP) matching, instilling second-order curvature information into CG potentials without constructing the full Hessian.
This article trains ``free''variational synthesis models using stochastic gradient descent in continuous space, and then discretize with post-training quantization to impose hardware and wetware constraints, enabling variational synthesis models to satisfy stringent reward criteria, while still synthesizing diverse des...
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Although $\mathbb{E}[\mathrm{dropout}(x)] = x$, here we show that $\mathbb{E}[\mathrm{LayerNorm}(\mathrm{dropout}(x))]$ is not equal to $\mathrm{LayerNorm}(x)$. Accordingly, the pattern of a Dropout layer followed by a LayerNorm, which is common to many AlphaFold2-based protein structure predictors, produces a systemat...
M3OS is presented, a multi-agent LLM system that decouples molecular-design reasoning from optimization-state management through Monte Carlo graph search, and achieves higher success rates than baselines, supporting the integration of persistent search state, specialized agents and controlled execution for multi-constr...
SPINET is introduced, which predicts sequences from molecular dynamics trajectories that uses cellular sheaves to represent residue interactions within each frame and recurrent units to integrate information across frames, then predicts all amino acids in a single pass.
A fully automated annotation framework for generating precise molecular descriptions at scale, such that the original molecule can be unambiguously reconstructed from the description alone and the proposed framework and dataset provide a reliable foundation for molecule--language alignment.
Recent advancements in image-based profiling techniques have enabled the collection of high-volume cell morphology data, allowing new molecular embedding models to learn from the experimental phenotypic perturbations of a molecule in a cell. Previously, we developed Molecule-Morphology Contrastive Pretraining (MoCoP),...
LapDDPM is introduced, a novel conditional Graph Diffusion Probabilistic Model designed for robust manifold learning and high-fidelity generation that significantly outperforms state-of-the-art baselines in distribution matching, manifold preservation, and downstream utility.
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.
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