Abstract Building a working mental model of a protein typically requires weeks of reading, cross-referencing crystal and predicted structures, and inspecting ligand complexes, an effort that is slow, unevenly accessible, and often requires specialized computational skills. We introduce Speak to a Protein, a new capabil...
Carles Navarro, M. Torrens, Philipp Thölke et al.· Journal of Chemical Informat...· 0 citations
Molecular dynamics (MD) simulations are a principled but computationally expensive approach for studying protein conformational variability, making it challenging to generate large ensembles of structures or characterize transitions between metastable conformations. AI methods for upsampling MD simulations have been de...
Siddharth Viswanath, Xingzhi Sun, Lucas Lee et al.· 0 citations
RNA design aims to identify sequences that fold into specified secondary structures. Existing methods formulate the task as target-specific search or conditional generation. However, natural RNA evolution proceeds through sequence variation and selection, with compensatory substitutions, whereas these methods do not ex...
Ze-Feng Lin, Xian-Yong Fang, Tian-Fan Fu et al.· 0 citations
Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and structure-based tasks, yet the potential of tokenization remains underexploited. Unlike human language, proteins preserve structure despite extensive sequence variation a p...
Biswajit Banerjee, Claudia A. Carreno, Anton S. Petrov· 0 citations
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Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical stages. As a result, accurate early prediction of chemical toxicity is essential to reduce downstream costs and improve compound prioritization. In this context, graph dee...
Noel Suarez-Barro, M. Lama, J. C. Vidal· 0 citations
CrystAF is introduced, an all-atom crystal flow-map generation model, and CrystAF with the UMA interatomic potential is used to systematically study where physics should enter, suggesting a simple principle: learn reusable physical alignment into the generator, and reserve inference-time physics for residual constraint...
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...
Jiarui Li, Zi-Xiang Yin, S. Landry et al.· 0 citations
Laboratories often face a new molecular assay with 16-64 labels and a bank of predictors whose training data and parameters are unavailable. The practical question is which frozen outputs to include in a small local model. AssayRouter treats completed assays as pseudo-targets and labels each candidate by its post-fit u...
Dong Xu, Zhangfan Yang, Jiantao Wu et al.· 0 citations
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.
S. Murdeshwar, Sanjit Shashi, Kevin Bachelor et al.· arXiv.org· 0 citations
Biological machine learning was long bottlenecked by the ability to synthesize designed DNA. Variational synthesis models control chemical reactions to physically manufacture quadrillions of designed sequences in DNA. However, training these generative models is challenging: constraints on chemical synthesis can force...
Alan N. Amin, Mattia G. Gollub, Andrei Slabodkin et al.· 0 citations
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...
Isaac Ellmen, David Errington, Matthew I. J. Raybould et al.· 0 citations
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...
Jun-Jie Wang, Yao-Wei Jin, Ruo-Hui Tang et al.· 0 citations
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